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null | https://openreview.net/forum?id=L1mMK39Z7P | @inproceedings{
pourcel2024aces,
title={{ACES}: Generating a Diversity of Challenging Programming Puzzles with Autotelic Generative Models},
author={Julien Pourcel and C{\'e}dric Colas and Gaia Molinaro and Pierre-Yves Oudeyer and Laetitia Teodorescu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=L1mMK39Z7P}
} | The ability to invent novel and interesting problems is a remarkable feature of human intelligence that drives innovation, art, and science. We propose a method that aims to automate this process by harnessing the power of state-of-the-art generative models to produce a diversity of challenging yet solvable problems, here in the context of Python programming puzzles. Inspired by the intrinsically motivated literature, Autotelic CodE Search (ACES) jointly optimizes for the diversity and difficulty of generated problems. We represent problems in a space of LLM-generated semantic descriptors describing the programming skills required to solve them (e.g. string manipulation, dynamic programming, etc.) and measure their difficulty empirically as a linearly decreasing function of the success rate of \textit{Llama-3-70B}, a state-of-the-art LLM problem solver. ACES iteratively prompts a large language model to generate difficult problems achieving a diversity of target semantic descriptors (goal-directed exploration) using previously generated problems as in-context examples. ACES generates problems that are more diverse and more challenging than problems produced by baseline methods and three times more challenging than problems found in existing Python programming benchmarks on average across 11 state-of-the-art code LLMs. | ACES: Generating a Diversity of Challenging Programming Puzzles with Autotelic Generative Models | [
"Julien Pourcel",
"Cédric Colas",
"Gaia Molinaro",
"Pierre-Yves Oudeyer",
"Laetitia Teodorescu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=L1jajNWON5 | @inproceedings{
ding2024condtsf,
title={Cond{TSF}: One-line Plugin of Dataset Condensation for Time Series Forecasting},
author={Jianrong Ding and Zhanyu Liu and Guanjie Zheng and Haiming Jin and Linghe Kong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=L1jajNWON5}
} | \textit{Dataset condensation} is a newborn technique that generates a small dataset that can be used in training deep neural networks (DNNs) to lower storage and training costs. The objective of dataset condensation is to ensure that the model trained with the synthetic dataset can perform comparably to the model trained with full datasets. However, existing methods predominantly concentrate on classification tasks, posing challenges in their adaptation to time series forecasting (TS-forecasting). This challenge arises from disparities in the evaluation of synthetic data. In classification, the synthetic data is considered well-distilled if the model trained with the full dataset and the model trained with the synthetic dataset yield identical labels for the same input, regardless of variations in output logits distribution. Conversely, in TS-forecasting, the effectiveness of synthetic data distillation is determined by the distance between predictions of the two models. The synthetic data is deemed well-distilled only when all data points within the predictions are similar. Consequently, TS-forecasting has a more rigorous evaluation methodology compared to classification. To mitigate this gap, we theoretically analyze the optimization objective of dataset condensation for TS-forecasting and propose a new one-line plugin of dataset condensation for TS-forecasting designated as Dataset \textbf{Cond}ensation for \textbf{T}ime \textbf{S}eries \textbf{F}orecasting (CondTSF) based on our analysis. Plugging CondTSF into previous dataset condensation methods facilitates a reduction in the distance between the predictions of the model trained with the full dataset and the model trained with the synthetic dataset, thereby enhancing performance. We conduct extensive experiments on eight commonly used time series datasets. CondTSF consistently improves the performance of all previous dataset condensation methods across all datasets, particularly at low condensing ratios. | CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting | [
"Jianrong Ding",
"Zhanyu Liu",
"Guanjie Zheng",
"Haiming Jin",
"Linghe Kong"
] | NeurIPS.cc/2024/Conference | 2406.02131 | [
"https://github.com/rafadd/condtsf"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Kzno1r3Xef | @inproceedings{
duan2024a,
title={A Structure-Aware Framework for Learning Device Placements on Computation Graphs},
author={Shukai Duan and Heng Ping and Nikos Kanakaris and Xiongye Xiao and Panagiotis Kyriakis and Nesreen K. Ahmed and Peiyu Zhang and Guixiang Ma and Mihai Capot{\u{a}} and Shahin Nazarian and Theodore L. Willke and Paul Bogdan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Kzno1r3Xef}
} | Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing approaches rely on two types of architectures known as grouper-placer and encoder-placer, respectively. In this work, we bridge the gap between encoder-placer and grouper-placer techniques and propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit. The framework consists of five steps, including graph coarsening, node representation learning and policy optimization. It facilitates end-to-end training and takes into account the DAG nature of the computation graphs. We also propose a model variant, inspired by graph parsing networks and complex network analysis, enabling graph representation learning and jointed, personalized graph partitioning, using an unspecified number of groups. To train the entire framework, we use reinforcement learning using the execution time of the placement as a reward. We demonstrate the flexibility and effectiveness of our approach through multiple experiments with three benchmark models, namely Inception-V3, ResNet, and BERT. The robustness of the proposed framework is also highlighted through an ablation study. The suggested placements improve the inference speed for the benchmark models by up to $58.2\%$ over CPU execution and by up to $60.24\%$ compared to other commonly used baselines. | A Structure-Aware Framework for Learning Device Placements on Computation Graphs | [
"Shukai Duan",
"Heng Ping",
"Nikos Kanakaris",
"Xiongye Xiao",
"Panagiotis Kyriakis",
"Nesreen K. Ahmed",
"Peiyu Zhang",
"Guixiang Ma",
"Mihai Capotă",
"Shahin Nazarian",
"Theodore L. Willke",
"Paul Bogdan"
] | NeurIPS.cc/2024/Conference | 2405.14185 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KyVBzkConO | @inproceedings{
kalavasis2024injecting,
title={Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models},
author={Alkis Kalavasis and Amin Karbasi and Argyris Oikonomou and Katerina Sotiraki and Grigoris Velegkas and Manolis Zampetakis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KyVBzkConO}
} | As ML models become increasingly complex and integral to high-stakes domains such as finance and healthcare, they also become more susceptible to sophisticated adversarial attacks. We investigate the threat posed by undetectable backdoors, as defined in Goldwasser et al. [2022], in models developed by insidious external expert firms. When such backdoors exist, they allow the designer of the model to sell information on how to slightly perturb their input to change the outcome of the model. We develop a general strategy to plant backdoors to obfuscated neural networks, that satisfy the security properties of the celebrated notion of indistinguishability obfuscation. Applying obfuscation before releasing neural networks is a strategy that is well motivated to protect sensitive information of the external expert firm. Our method to plant backdoors ensures that even if the weights and architecture of the obfuscated model are accessible, the existence of
the backdoor is still undetectable. Finally, we introduce the notion of undetectable backdoors to language models and extend our neural network backdoor attacks to such models based on the existence of steganographic functions. | Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models | [
"Alkis Kalavasis",
"Amin Karbasi",
"Argyris Oikonomou",
"Katerina Sotiraki",
"Grigoris Velegkas",
"Manolis Zampetakis"
] | NeurIPS.cc/2024/Conference | 2406.05660 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KyNO0n1bJ9 | @inproceedings{
kalinke2024the,
title={The Minimax Rate of {HSIC} Estimation for Translation-Invariant Kernels},
author={Florian Kalinke and Zolt{\'a}n Szab{\'o}},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KyNO0n1bJ9}
} | Kernel techniques are among the most influential approaches in data science and statistics. Under mild conditions, the reproducing kernel Hilbert space associated to a kernel is capable of encoding the independence of $M\ge2$ random variables. Probably the most widespread independence measure relying on kernels is the so-called Hilbert-Schmidt independence criterion (HSIC; also referred to as distance covariance in the statistics literature). Despite various existing HSIC estimators designed since its introduction close to two decades ago, the fundamental question of the rate at which HSIC can be estimated is still open. In this work, we prove that the minimax optimal rate of HSIC estimation on $\mathbb{R}^d$ for Borel measures containing the Gaussians with continuous bounded translation-invariant characteristic kernels is $\mathcal{O}\left(n^{-1/2}\right)$. Specifically, our result implies the optimality in the minimax sense of many of the most-frequently used estimators (including the U-statistic, the V-statistic, and the Nyström-based one) on $\mathbb{R}^d$. | The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels | [
"Florian Kalinke",
"Zoltán Szabó"
] | NeurIPS.cc/2024/Conference | 2403.07735 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KxjGi1krBi | @inproceedings{
liang2024bayesian,
title={Bayesian Optimization of Functions over Node Subsets in Graphs},
author={Huidong Liang and Xingchen Wan and Xiaowen Dong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KxjGi1krBi}
} | We address the problem of optimizing over functions defined on node subsets in a graph. The optimization of such functions is often a non-trivial task given their combinatorial, black-box and expensive-to-evaluate nature. Although various algorithms have been introduced in the literature, most are either task-specific or computationally inefficient and only utilize information about the graph structure without considering the characteristics of the function. To address these limitations, we utilize Bayesian Optimization (BO), a sample-efficient black-box solver, and propose a novel framework for combinatorial optimization on graphs. More specifically, we map each $k$-node subset in the original graph to a node in a new combinatorial graph and adopt a local modeling approach to efficiently traverse the latter graph by progressively sampling its subgraphs using a recursive algorithm. Extensive experiments under both synthetic and real-world setups demonstrate the effectiveness of the proposed BO framework on various types of graphs and optimization tasks, where its behavior is analyzed in detail with ablation studies. | Bayesian Optimization of Functions over Node Subsets in Graphs | [
"Huidong Liang",
"Xingchen Wan",
"Xiaowen Dong"
] | NeurIPS.cc/2024/Conference | 2405.15119 | [
"https://github.com/LeonResearch/GraphComBO"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Kx8I0rP7w2 | @inproceedings{
dreveton2024why,
title={Why the Metric Backbone Preserves Community Structure},
author={Maximilien Dreveton and Charbel Chucri and Matthias Grossglauser and Patrick Thiran},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Kx8I0rP7w2}
} | The metric backbone of a weighted graph is the union of all-pairs shortest paths. It is obtained by removing all edges $(u,v)$ that are not the shortest path between $u$ and $v$. In networks with well-separated communities, the metric backbone tends to preserve many inter-community edges, because these edges serve as bridges connecting two communities, but tends to delete many intra-community edges because the communities are dense. This suggests that the metric backbone would dilute or destroy the community structure of the network. However, this is not borne out by prior empirical work, which instead showed that the metric backbone of real networks preserves the community structure of the original network well. In this work, we analyze the metric backbone of a broad class of weighted random graphs with communities, and we formally prove the robustness of the community structure with respect to the deletion of all the edges that are not in the metric backbone. An empirical comparison of several graph sparsification techniques confirms our theoretical finding and shows that the metric backbone is an efficient sparsifier in the presence of communities. | Why the Metric Backbone Preserves Community Structure | [
"Maximilien Dreveton",
"Charbel Chucri",
"Matthias Grossglauser",
"Patrick Thiran"
] | NeurIPS.cc/2024/Conference | 2406.03852 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KwRLDkyVOl | @inproceedings{
chen2024noise,
title={Noise Contrastive Alignment of Language Models with Explicit Rewards},
author={Huayu Chen and Guande He and Lifan Yuan and Ganqu Cui and Hang Su and Jun Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KwRLDkyVOl}
} | User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs). Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given. In this paper, we introduce a general framework for LM alignment, leveraging Noise Contrastive Estimation (NCE) to bridge the gap in handling reward datasets explicitly annotated with scalar evaluations. Our framework comprises two parallel algorithms, NCA and InfoNCA, both enabling the direct extraction of an LM policy from reward data as well as preference data. Notably, we show that the DPO loss is a special case of our proposed InfoNCA objective under pairwise preference settings, thereby integrating and extending current alignment theories. By comparing NCA and InfoNCA, we demonstrate that the well-observed decreasing-likelihood trend of DPO/InfoNCA is caused by their focus on adjusting relative likelihood across different responses.
In contrast, NCA optimizes the absolute likelihood for each response, thereby effectively preventing the chosen likelihood from decreasing. We evaluate our methods in both reward and preference settings with Mistral-8$\times$7B and 7B models. Experiments suggest that InfoNCA/NCA surpasses various preference baselines when reward datasets are available. We also find NCA significantly outperforms DPO in complex reasoning tasks like math and coding. | Noise Contrastive Alignment of Language Models with Explicit Rewards | [
"Huayu Chen",
"Guande He",
"Lifan Yuan",
"Ganqu Cui",
"Hang Su",
"Jun Zhu"
] | NeurIPS.cc/2024/Conference | 2402.05369 | [
"https://github.com/thu-ml/noise-contrastive-alignment"
] | https://huggingface.co/papers/2402.05369 | 1 | 1 | 0 | 4 | [
"openbmb/Eurux-8x22b-nca",
"openbmb/Eurus-70b-nca",
"jukofyork/Eurus-70b-nca-fixed",
"pharaouk/Eurus-70b-nca",
"ChenDRAG/CCA_LlamaGen",
"ChenDRAG/CCA_VAR",
"RichardErkhov/openbmb_-_Eurus-70b-nca-gguf"
] | [] | [] | [
"openbmb/Eurux-8x22b-nca",
"openbmb/Eurus-70b-nca",
"jukofyork/Eurus-70b-nca-fixed",
"pharaouk/Eurus-70b-nca",
"ChenDRAG/CCA_LlamaGen",
"ChenDRAG/CCA_VAR",
"RichardErkhov/openbmb_-_Eurus-70b-nca-gguf"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=Kw6MRGFx0R | @inproceedings{
bulat2024qbb,
title={{QBB}: Quantization with Binary Bases for {LLM}s},
author={Adrian Bulat and Yassine Ouali and Georgios Tzimiropoulos},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Kw6MRGFx0R}
} | Current post-training quantization methods for LLMs compress the weights down to 4-bits, with moderate to low degradation in accuracy. However, further reducing the number of bits or accelerating the network while avoiding large accuracy drops, especially for smaller, sub 7B models, remains an actively researched and open problem. To address this, in this work, we introduce Quantization with Binary Bases (QBB), a new approach for low-bit quantization that effectively removes (nearly) all multiplications, reducing the implementation to summations. Our novel approach works by decomposing the original weights into a set of binary (1-bit) matrices using an iterative process. For a given layer, starting from a weight matrix, we first construct an initial approximation using an analytical solution, where each new binary matrix, paired with a scaling vector, approximates the residual error of the previous estimation. Secondly, using gradient descent and a progressive learning curriculum, we find the optimal set of binary matrices and scaling vectors that minimize the $\ell_2$ distance between the produced approximation and original weights. Thirdly, as previous steps are input agnostic, we holistically optimize the scaling vectors alone, calibrating them in student-teacher fashion, with the teacher providing both the data,
by autoregressive generation starting from a random token, and the target logits.
When evaluated across multiple LLM families, our approach matches and outperforms all prior works, setting a new state-of-the-art result using a summation-only based approach. | QBB: Quantization with Binary Bases for LLMs | [
"Adrian Bulat",
"Yassine Ouali",
"Georgios Tzimiropoulos"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KvAaIJhqhI | @inproceedings{
lu2024style,
title={Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation},
author={Yuwu Lu and Haoyu Huang and Xue Hu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KvAaIJhqhI}
} | Blended-target domain adaptation (BTDA), which implicitly mixes multiple sub-target domains into a fine domain, has attracted more attention in recent years. Most previously developed BTDA approaches focus on utilizing a single source domain, which makes it difficult to obtain sufficient feature information for learning domain-invariant representations. Furthermore, different feature distributions derived from different domains may increase the uncertainty of models. To overcome these issues, we propose a style adaptation and uncertainty estimation (SAUE) approach for multi-source blended-target domain adaptation (MBDA). Specifically, we exploit the extra knowledge acquired from the blended-target domain, where a similarity factor is adopted to select more useful target style information for augmenting the source features. \!Then, to mitigate the negative impact of the domain-specific attributes, we devise a function to estimate and mitigate uncertainty in category prediction. Finally, we construct a simple and lightweight adversarial learning strategy for MBDA, effectively aligning multi-source and blended-target domains without the requirements of domain labels of the target domains. Extensive experiments conducted on several challenging DA benchmarks, including the ImageCLEF-DA, Office-Home, VisDA 2017, and DomainNet datasets, demonstrate the superiority of our method over the state-of-the-art (SOTA) approaches. | Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation | [
"Yuwu Lu",
"Haoyu Huang",
"Xue Hu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Ku35qKpveg | @inproceedings{
qiu2024dualperspective,
title={Dual-Perspective Activation: Efficient Channel Denoising via Joint Forward-Backward Criterion for Artificial Neural Networks},
author={Tian Qiu and Chenchao Gao and Zunlei Feng and Jie Lei and Bingde Hu and Xingen Wang and Yi Gao and Mingli Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ku35qKpveg}
} | The design of Artificial Neural Network (ANN) is inspired by the working patterns of the human brain. Connections in biological neural networks are sparse, as they only exist between few neurons. Meanwhile, the sparse representation in ANNs has been shown to possess significant advantages. Activation responses of ANNs are typically expected to promote sparse representations, where key signals get activated while irrelevant/redundant signals are suppressed. It can be observed that samples of each category are only correlated with sparse and specific channels in ANNs. However, existing activation mechanisms often struggle to suppress signals from other irrelevant channels entirely, and these signals have been verified to be detrimental to the network's final decision. To address the issue of channel noise interference in ANNs, a novel end-to-end trainable Dual-Perspective Activation (DPA) mechanism is proposed. DPA efficiently identifies irrelevant channels and applies channel denoising under the guidance of a joint criterion established online from both forward and backward propagation perspectives while preserving activation responses from relevant channels. Extensive experiments demonstrate that DPA successfully denoises channels and facilitates sparser neural representations. Moreover, DPA is parameter-free, fast, applicable to many mainstream ANN architectures, and achieves remarkable performance compared to other existing activation counterparts across multiple tasks and domains. Code is available at https://github.com/horrible-dong/DPA. | Dual-Perspective Activation: Efficient Channel Denoising via Joint Forward-Backward Criterion for Artificial Neural Networks | [
"Tian Qiu",
"Chenchao Gao",
"Zunlei Feng",
"Jie Lei",
"Bingde Hu",
"Xingen Wang",
"Yi Gao",
"Mingli Song"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Ku31aRq3sW | @inproceedings{
fan2024how,
title={How to Solve Contextual Goal-Oriented Problems with Offline Datasets?},
author={Ying Fan and Jingling Li and Adith Swaminathan and Aditya Modi and Ching-An Cheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ku31aRq3sW}
} | We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-Oriented (CGO) problems. By carefully constructing an action-augmented MDP that is equivalent to the original MDP, CODA creates a fully labeled transition dataset under training contexts without additional approximation error. We conduct a novel theoretical analysis to demonstrate CODA's capability to solve CGO problems in the offline data setup. Empirical results also showcase the effectiveness of CODA, which outperforms other baseline methods across various context-goal relationships of CGO problem. This approach offers a promising direction to solving CGO problems using offline datasets. | How to Solve Contextual Goal-Oriented Problems with Offline Datasets? | [
"Ying Fan",
"Jingling Li",
"Adith Swaminathan",
"Aditya Modi",
"Ching-An Cheng"
] | NeurIPS.cc/2024/Conference | 2408.07753 | [
"https://github.com/yingfan-bot/coda"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Ktx95ZuRjP | @inproceedings{
bi2024learning,
title={Learning to Handle Complex Constraints for Vehicle Routing Problems},
author={Jieyi Bi and Yining Ma and Jianan Zhou and Wen Song and Zhiguang Cao and Yaoxin Wu and Jie Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ktx95ZuRjP}
} | Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive strategies to learn and predict these tailored masks, potentially enhancing performance while significantly reducing computational costs during training. To verify our PIP designs, we conduct extensive experiments on the highly challenging Traveling Salesman Problem with Time Window (TSPTW), and TSP with Draft Limit (TSPDL) variants under different constraint hardness levels. Notably, our PIP is generic to boost many neural methods, and exhibits both a significant reduction in infeasible rate and a substantial improvement in solution quality. | Learning to Handle Complex Constraints for Vehicle Routing Problems | [
"Jieyi Bi",
"Yining Ma",
"Jianan Zhou",
"Wen Song",
"Zhiguang Cao",
"Yaoxin Wu",
"Jie Zhang"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/jieyibi/pip-constraint"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KsLX5pFpOs | @inproceedings{
kellerhals2024proportional,
title={Proportional Fairness in Clustering: A Social Choice Perspective},
author={Leon Kellerhals and Jannik Peters},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KsLX5pFpOs}
} | We study the proportional clustering problem of Chen et al. (ICML'19) and relate it to the area of multiwinner voting in computational social choice. We show that any clustering satisfying a weak proportionality notion of Brill and Peters (EC'23) simultaneously obtains the best known approximations to the proportional fairness notion of Chen et al., but also to individual fairness (Jung et al., FORC'20) and the ``core'' (Li et al., ICML'21). In fact, we show that any approximation to proportional fairness is also an approximation to individual fairness and vice versa. Finally, we also study stronger notions of proportional representation, in which deviations do not only happen to single, but multiple candidate centers, and show that stronger proportionality notions of Brill and Peters imply approximations to these stronger guarantees. | Proportional Fairness in Clustering: A Social Choice Perspective | [
"Leon Kellerhals",
"Jannik Peters"
] | NeurIPS.cc/2024/Conference | 2310.18162 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KrHFICMPjm | @inproceedings{
zhang2024guide,
title={{GUIDE}: Real-Time Human-Shaped Agents},
author={Lingyu Zhang and Zhengran Ji and Nicholas R Waytowich and Boyuan Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KrHFICMPjm}
} | The recent rapid advancement of machine learning has been driven by increasingly powerful models with the growing availability of training data and computational resources. However, real-time decision-making tasks with limited time and sparse learning signals remain challenging. One way of improving the learning speed and performance of these agents is to leverage human guidance. In this work, we introduce GUIDE, a framework for real-time human-guided reinforcement learning by enabling continuous human feedback and grounding such feedback into dense rewards to accelerate policy learning. Additionally, our method features a simulated feedback module that learns and replicates human feedback patterns in an online fashion, effectively reducing the need for human input while allowing continual training. We demonstrate the performance of our framework on challenging tasks with sparse rewards and visual observations. Our human study involving 50 subjects offers strong quantitative and qualitative evidence of the effectiveness of our approach. With only 10 minutes of human feedback, our algorithm achieves up to 30\% increase in success rate compared to its RL baseline. | GUIDE: Real-Time Human-Shaped Agents | [
"Lingyu Zhang",
"Zhengran Ji",
"Nicholas R Waytowich",
"Boyuan Chen"
] | NeurIPS.cc/2024/Conference | 2410.15181 | [
""
] | https://huggingface.co/papers/2410.15181 | 0 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=KqgSzXbufw | @inproceedings{
yan2024decentralized,
title={Decentralized Noncooperative Games with Coupled Decision-Dependent Distributions},
author={Wenjing Yan and Xuanyu Cao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KqgSzXbufw}
} | Distribution variations in machine learning, driven by the dynamic nature of deployment environments, significantly impact the performance of learning models. This paper explores endogenous distribution shifts in learning systems, where deployed models influence environments and subsequently alter data distributions. This phenomenon is formulated by a decision-dependent distribution mapping within the recently proposed framework of performative prediction (PP) Perdomo et al. (2020). We investigate the performative effect in a decentralized noncooperative game, where players aim to minimize private cost functions while simultaneously managing coupled inequality constraints. Under performativity, we examine two equilibrium concepts for the studied game: performative stable equilibrium (PSE) and Nash equilibrium (NE), and establish sufficient conditions for their existence and uniqueness. Notably, we provide the first upper bound on the distance between the PSE and NE in the literature, which is challenging to evaluate due to the absence of strong convexity on the joint cost function. Furthermore, we develop a decentralized stochastic primal-dual algorithm for efficiently computing the PSE point. By carefully bounding the performative effect in theoretical analysis, we prove that the proposed algorithm achieves sublinear convergence rates for both performative regrets and constraint violation and maintains the same order of convergence rate as the case without performativity. Numerical experiments validate the effectiveness of our algorithm and theoretical results. | Decentralized Noncooperative Games with Coupled Decision-Dependent Distributions | [
"Wenjing Yan",
"Xuanyu Cao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KqbLzSIXkm | @inproceedings{
phung2024dimsum,
title={Di{MSUM}: Diffusion Mamba - A Scalable and Unified Spatial-Frequency Method for Image Generation},
author={Hao Phung and Quan Dao and Trung Tuan Dao and Hoang Phan and Dimitris N. Metaxas and Anh Tuan Tran},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KqbLzSIXkm}
} | We introduce a novel state-space architecture for diffusion models, effectively harnessing spatial and frequency information to enhance the inductive bias towards local features in input images for image generation tasks. While state-space networks, including Mamba, a revolutionary advancement in recurrent neural networks, typically scan input sequences from left to right, they face difficulties in designing effective scanning strategies, especially in the processing of image data. Our method demonstrates that integrating wavelet transformation into Mamba enhances the local structure awareness of visual inputs and better captures long-range relations of frequencies by disentangling them into wavelet subbands, representing both low- and high-frequency components. These wavelet-based outputs are then processed and seamlessly fused with the original Mamba outputs through a cross-attention fusion layer, combining both spatial and frequency information to optimize the order awareness of state-space models which is essential for the details and overall quality of image generation. Besides, we introduce a globally-shared transformer to supercharge the performance of Mamba, harnessing its exceptional power to capture global relationships. Through extensive experiments on standard benchmarks, our method demonstrates superior results compared to DiT and DIFFUSSM, achieving faster training convergence and delivering high-quality outputs. The codes and pretrained models are released at https://github.com/VinAIResearch/DiMSUM.git. | DiMSUM: Diffusion Mamba - A Scalable and Unified Spatial-Frequency Method for Image Generation | [
"Hao Phung",
"Quan Dao",
"Trung Tuan Dao",
"Hoang Phan",
"Dimitris N. Metaxas",
"Anh Tuan Tran"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KppBAWJbry | @inproceedings{
wen2024privacy,
title={Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models},
author={Yuxin Wen and Leo Marchyok and Sanghyun Hong and Jonas Geiping and Tom Goldstein and Nicholas Carlini},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KppBAWJbry}
} | It is commonplace to produce application-specific models by fine-tuning large pre-trained models using a small bespoke dataset. The widespread availability of foundation model checkpoints on the web poses considerable risks, including the vulnerability to backdoor attacks. In this paper, we unveil a new vulnerability: the privacy backdoor attack. This black-box privacy attack aims to amplify the privacy leakage that arises when fine-tuning a model: when a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. We conduct extensive experiments on various datasets and models, including both vision-language models (CLIP) and large language models, demonstrating the broad applicability and effectiveness of such an attack. Additionally, we carry out multiple ablation studies with different fine-tuning methods and inference strategies to thoroughly analyze this new threat. Our findings highlight a critical privacy concern within the machine learning community and call for a re-evaluation of safety protocols in the use of open-source pre-trained models. | Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models | [
"Yuxin Wen",
"Leo Marchyok",
"Sanghyun Hong",
"Jonas Geiping",
"Tom Goldstein",
"Nicholas Carlini"
] | NeurIPS.cc/2024/Conference | 2404.01231 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KoyTqNs6SZ | @inproceedings{
chen2024learning,
title={Learning to Price Homogeneous Data},
author={Keran Chen and Joon Suk Huh and Kirthevasan Kandasamy},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KoyTqNs6SZ}
} | We study a data pricing problem, where a seller has access to $N$ homogeneous data points (e.g. drawn i.i.d. from some distribution).
There are $m$ types of buyers in the market, where buyers of the same type $i$ have the same valuation curve $v_i:[N]\rightarrow [0,1]$, where $v_i(n)$ is the value for having $n$ data points.
*A priori*, the seller is unaware of the
distribution of buyers, but can repeat the market for $T$ rounds so as to learn the revenue-optimal pricing curve $p:[N] \rightarrow [0, 1]$.
To solve this online learning problem,
we first develop novel discretization schemes to approximate any pricing curve.
When compared to prior work,
the size of our discretization schemes scales gracefully with the approximation parameter, which translates to better regret in online learning.
Under assumptions like smoothness and diminishing returns which are satisfied by data, the discretization size can be reduced further.
We then turn to the online learning problem,
both in the stochastic and adversarial settings.
On each round, the seller chooses an *anonymous* pricing curve $p_t$.
A new buyer appears and may choose to purchase some amount of data.
She then reveals her type *only if* she makes a purchase.
Our online algorithms build on classical algorithms such as UCB and FTPL, but require novel ideas to account for the asymmetric nature of this feedback and to deal with the vastness of the space of pricing curves.
Using the improved discretization schemes previously developed, we are able to achieve
$\widetilde{O}(m\sqrt{T})$ regret in the stochastic setting and $\widetilde{\mathcal{O}}(m^{3/2}\sqrt{T})$ regret in the adversarial setting. | Learning to Price Homogeneous Data | [
"Keran Chen",
"Joon Suk Huh",
"Kirthevasan Kandasamy"
] | NeurIPS.cc/2024/Conference | 2407.05484 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Kl13lipxTW | @inproceedings{
lin2024backtime,
title={BackTime: Backdoor Attacks on Multivariate Time Series Forecasting},
author={Xiao Lin and Zhining Liu and Dongqi Fu and Ruizhong Qiu and Hanghang Tong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Kl13lipxTW}
} | Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology. While a myriad of powerful deep learning models have been developed for this task, few works have explored the robustness of MTS forecasting models to malicious attacks, which is crucial for their trustworthy employment in high-stake scenarios. To address this gap, we dive deep into the backdoor attacks on MTS forecasting models and propose an effective attack method named BackTime. By subtly injecting a few \textit{stealthy triggers} into the MTS data, BackTime can alter the predictions of the forecasting model according to the attacker's intent. Specifically, BackTime first identifies vulnerable timestamps in the data for poisoning, and then adaptively synthesizes stealthy and effective triggers by solving a bi-level optimization problem with a GNN-based trigger generator. Extensive experiments across multiple datasets and state-of-the-art MTS forecasting models demonstrate the effectiveness, versatility, and stealthiness of BackTime attacks. | BackTime: Backdoor Attacks on Multivariate Time Series Forecasting | [
"Xiao Lin",
"Zhining Liu",
"Dongqi Fu",
"Ruizhong Qiu",
"Hanghang Tong"
] | NeurIPS.cc/2024/Conference | 2410.02195 | [
"https://github.com/xiaolin-cs/backtime"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=KkYZmepjHn | @inproceedings{
chen2024fast,
title={Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time},
author={Zixiang Chen and Huizhuo Yuan and Yongqian Li and Yiwen Kou and Junkai Zhang and Quanquan Gu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KkYZmepjHn}
} | Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration of discrete diffusion models remains under-explored. In this paper, we propose discrete non-Markov diffusion models (DNDM), which naturally induce the predetermined transition time set. This enables a training-free sampling algorithm that significantly reduces the number of function evaluations (i.e., calls to the neural network), making the sampling process much faster. Furthermore, we study the transition from finite to infinite step sampling, offering new insights into bridging the gap between discrete and continuous-time processes for discrete diffusion models. Extensive experiments on natural language generation and machine translation tasks demonstrate the superior performance of our method in terms of both generation speed and sample quality compared to existing methods for discrete diffusion models. Codes are available at \url{https://github.com/uclaml/DNDM}. | Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time | [
"Zixiang Chen",
"Huizhuo Yuan",
"Yongqian Li",
"Yiwen Kou",
"Junkai Zhang",
"Quanquan Gu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KjNEzWRIqn | @inproceedings{
ou2024synatra,
title={Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale},
author={Tianyue Ou and Frank F. Xu and Aman Madaan and Jiarui Liu and Robert Lo and Abishek Sridhar and Sudipta Sengupta and Dan Roth and Graham Neubig and Shuyan Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KjNEzWRIqn}
} | LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives (e.g., arranging an online meeting). However, accuracy is still far from satisfactory, partly due to a lack of large-scale, direct demonstrations for digital tasks. Obtaining supervised data from humans is costly, and automatic data collection through exploration or reinforcement learning relies on complex environmental and content setup, resulting in datasets that lack comprehensive coverage of various scenarios. On the other hand, there is abundant knowledge that may indirectly assist task completion, such as online tutorials that were created for human consumption. In this work, we present Synatra, an approach that effectively transforms this indirect knowledge into direct supervision at scale. We define different types of indirect knowledge, and carefully study the available sources to obtain it, methods to encode the structure of direct demonstrations, and finally methods to transform indirect knowledge into direct demonstrations. We use 100k such synthetically-created demonstrations to finetune a 7B CodeLlama, and demonstrate that the resulting agent surpasses all comparably sized models on three web-based task benchmarks Mind2Web, MiniWoB++ and WebArena, as well as surpassing GPT-3.5 on WebArena and Mind2Web. In addition, while synthetic demonstrations prove to be only 3% the cost of human demonstrations (at $0.031 each), we show that the synthetic demonstrations can be more effective than an identical number of human demonstrations collected from limited domains. | Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale | [
"Tianyue Ou",
"Frank F. Xu",
"Aman Madaan",
"Jiarui Liu",
"Robert Lo",
"Abishek Sridhar",
"Sudipta Sengupta",
"Dan Roth",
"Graham Neubig",
"Shuyan Zhou"
] | NeurIPS.cc/2024/Conference | 2409.15637 | [
""
] | https://huggingface.co/papers/2409.15637 | 0 | 0 | 0 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=KhwOuB0fs9 | @inproceedings{
huang2024effilearner,
title={EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization},
author={Dong HUANG and Jianbo Dai and Han Weng and Puzhen Wu and Yuhao QING and Heming Cui and Zhijiang Guo and Jie Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KhwOuB0fs9}
} | Large language models (LLMs) have shown remarkable progress in code generation, but their generated code often suffers from inefficiency, resulting in longer execution times and higher memory consumption. To address this issue, we propose EffiLearner, a self-optimization framework that utilizes execution overhead profiles to improve the efficiency of LLM-generated code. EffiLearner first generates code using an LLM, then executes it locally to capture execution time and memory usage profiles. These profiles are fed back to the LLM, which then revises the code to reduce overhead. To evaluate the effectiveness of EffiLearner, we conduct extensive experiments on EffiBench and two commonly used code generation benchmarks with 16 open-source and 6 closed-source models. Our evaluation results demonstrate that through iterative self-optimization, EffiLearner significantly enhances the efficiency of LLM-generated code. For example, the execution time (ET) of StarCoder2-15B for the EffiBench decreases from 0.93 (s) to 0.12 (s) which reduces 87.1\% execution time requirement compared with the initial code. The total memory usage (TMU) of StarCoder2-15B also decreases from 22.02 (Mb*s) to 2.03 (Mb*s), which decreases 90.8\% total memory consumption during the execution process. | EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization | [
"Dong HUANG",
"Jianbo Dai",
"Han Weng",
"Puzhen Wu",
"Yuhao QING",
"Heming Cui",
"Zhijiang Guo",
"Jie Zhang"
] | NeurIPS.cc/2024/Conference | 2405.15189 | [
"https://github.com/huangd1999/soap"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Ke40kfOT2E | @inproceedings{
gala2024scaling,
title={Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits},
author={Gennaro Gala and Cassio de Campos and Antonio Vergari and Erik Quaeghebeur},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ke40kfOT2E}
} | Probabilistic integral circuits (PICs) have been recently introduced as probabilistic models enjoying the key ingredient behind expressive generative models: continuous latent variables (LVs). PICs are symbolic computational graphs defining continuous LV models as hierarchies of functions that are summed and multiplied together, or integrated over some LVs. They are tractable if LVs can be analytically integrated out, otherwise they can be approximated by tractable probabilistic circuits (PC) encoding a hierarchical numerical quadrature process, called QPCs.
So far, only tree-shaped PICs have been explored, and training them via numerical quadrature requires memory-intensive processing at scale. In this paper, we address these issues, and present: (i) a pipeline for building DAG-shaped PICs out of arbitrary variable decompositions, (ii) a procedure for training PICs using tensorized circuit architectures, and (iii) neural functional sharing techniques to allow scalable training. In extensive experiments, we showcase the effectiveness of functional sharing and the superiority of QPCs over traditional PCs. | Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits | [
"Gennaro Gala",
"Cassio de Campos",
"Antonio Vergari",
"Erik Quaeghebeur"
] | NeurIPS.cc/2024/Conference | 2406.06494 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=Ke3MSP8Nr6 | @inproceedings{
wu2024informationtheoretic,
title={Information-theoretic Limits of Online Classification with Noisy Labels},
author={Changlong Wu and Ananth Grama and Wojciech Szpankowski},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ke3MSP8Nr6}
} | We study online classification with general hypothesis classes where the true labels are determined by some function within the class, but are corrupted by *unknown* stochastic noise, and the features are generated adversarially. Predictions are made using observed *noisy* labels and noiseless features, while the performance is measured via minimax risk when comparing against *true* labels. The noisy mechanism is modeled via a general noisy kernel that specifies, for any individual data point, a set of distributions from which the actual noisy label distribution is chosen. We show that minimax risk is *tightly* characterized (up to a logarithmic factor of the hypothesis class size) by the *Hellinger gap* of the noisy label distributions induced by the kernel, *independent* of other properties such as the means and variances of the noise. Our main technique is based on a novel reduction to an online comparison scheme of two hypotheses, along with a new *conditional* version of Le Cam-Birgé testing suitable for online settings. Our work provides the first comprehensive characterization of noisy online classification with guarantees that apply to the *ground truth* while addressing *general* noisy observations. | Information-theoretic Limits of Online Classification with Noisy Labels | [
"Changlong Wu",
"Ananth Grama",
"Wojciech Szpankowski"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Kcsj9FGnKR | @inproceedings{
shao2024diffult,
title={Diffu{LT}: Diffusion for Long-tail Recognition Without External Knowledge},
author={Jie Shao and Ke Zhu and Hanxiao Zhang and Jianxin Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Kcsj9FGnKR}
} | This paper introduces a novel pipeline for long-tail (LT) recognition that diverges from conventional strategies. Instead, it leverages the long-tailed dataset itself to generate a balanced proxy dataset without utilizing external data or model. We deploy a diffusion model trained from scratch on only the long-tailed dataset to create this proxy and verify the effectiveness of the data produced. Our analysis identifies approximately-in-distribution (AID) samples, which slightly deviate from the real data distribution and incorporate a blend of class information, as the crucial samples for enhancing the generative model's performance in long-tail classification. We promote the generation of AID samples during the training of a generative model by utilizing a feature extractor to guide the process and filter out detrimental samples during generation. Our approach, termed Diffusion model for Long-Tail recognition (DiffuLT), represents a pioneer application of generative models in long-tail recognition. DiffuLT achieves state-of-the-art results on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT, surpassing leading competitors by significant margins. Comprehensive ablations enhance the interpretability of our pipeline. Notably, the entire generative process is conducted without relying on external data or pre-trained model weights, which leads to its generalizability to real-world long-tailed scenarios. | DiffuLT: Diffusion for Long-tail Recognition Without External Knowledge | [
"Jie Shao",
"Ke Zhu",
"Hanxiao Zhang",
"Jianxin Wu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KcmhSrHzJB | @inproceedings{
li2024on,
title={On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models},
author={Boyao Li and Alexander Joseph Thomson and houssam nassif and Matthew M. Engelhard and David Page},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KcmhSrHzJB}
} | Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs). In this paper, we propose an innovative solution by constructing infinite tree-structured PGMs that correspond exactly to neural networks. Our research reveals that DNNs, during forward propagation, indeed perform approximations of PGM inference that are precise in this alternative PGM structure. Not only does our research complement existing studies that describe neural networks as kernel machines or infinite-sized Gaussian processes, it also elucidates a more direct approximation that DNNs make to exact inference in PGMs. Potential benefits include improved pedagogy and interpretation of DNNs, and algorithms that can merge the strengths of PGMs and DNNs. | On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models | [
"Boyao Li",
"Alexander Joseph Thomson",
"houssam nassif",
"Matthew M. Engelhard",
"David Page"
] | NeurIPS.cc/2024/Conference | 2305.17583 | [
"https://github.com/engelhard-lab/dnn_treepgm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KcDcaVOW1S | @inproceedings{
chen2024conformalized,
title={Conformalized Time Series with Semantic Features},
author={Baiting Chen and Zhimei Ren and Lu Cheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KcDcaVOW1S}
} | Conformal prediction is a powerful tool for uncertainty quantification, but its application to time-series data is constrained by the violation of the exchangeability assumption. Current solutions for time-series prediction typically operate in the output space and rely on manually selected weights to address distribution drift, leading to overly conservative predictions. To enable dynamic weight learning in the semantically rich latent space, we introduce a novel approach called Conformalized Time Series with Semantic Features (CT-SSF). CT-SSF utilizes the inductive bias in deep representation learning to dynamically adjust weights, prioritizing semantic features relevant to the current prediction. Theoretically, we show that CT-SSF surpasses previous methods defined in the output space. Experiments on synthetic and benchmark datasets demonstrate that CT-SSF significantly outperforms existing state-of-the-art (SOTA) conformal prediction techniques in terms of prediction efficiency while maintaining a valid coverage guarantee. | Conformalized Time Series with Semantic Features | [
"Baiting Chen",
"Zhimei Ren",
"Lu Cheng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Kc37srXvan | @inproceedings{
liang2024pointmamba,
title={PointMamba: A Simple State Space Model for Point Cloud Analysis},
author={Dingkang Liang and Xin Zhou and Wei Xu and Xingkui Zhu and Zhikang Zou and Xiaoqing Ye and Xiao Tan and Xiang Bai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Kc37srXvan}
} | Transformers have become one of the foundational architectures in point cloud analysis tasks due to their excellent global modeling ability. However, the attention mechanism has quadratic complexity, making the design of a linear complexity method with global modeling appealing. In this paper, we propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs. Specifically, our method leverages space-filling curves for effective point tokenization and adopts an extremely simple, non-hierarchical Mamba encoder as the backbone. Comprehensive evaluations demonstrate that PointMamba achieves superior performance across multiple datasets while significantly reducing GPU memory usage and FLOPs. This work underscores the potential of SSMs in 3D vision-related tasks and presents a simple yet effective Mamba-based baseline for future research. The code is available at https://github.com/LMD0311/PointMamba. | PointMamba: A Simple State Space Model for Point Cloud Analysis | [
"Dingkang Liang",
"Xin Zhou",
"Wei Xu",
"Xingkui Zhu",
"Zhikang Zou",
"Xiaoqing Ye",
"Xiao Tan",
"Xiang Bai"
] | NeurIPS.cc/2024/Conference | 2402.10739 | [
"https://github.com/lmd0311/pointmamba"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KZrfBTrPey | @inproceedings{
zheng2024aliagent,
title={{ALI}-Agent: Assessing {LLM}s' Alignment with Human Values via Agent-based Evaluation},
author={Jingnan Zheng and Han Wang and An Zhang and Tai D. Nguyen and Jun Sun and Tat-Seng Chua},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KZrfBTrPey}
} | Large Language Models (LLMs) can elicit unintended and even harmful content when misaligned with human values, posing severe risks to users and society. To mitigate these risks, current evaluation benchmarks predominantly employ expert-designed contextual scenarios to assess how well LLMs align with human values. However, the labor-intensive nature of these benchmarks limits their test scope, hindering their ability to generalize to the extensive variety of open-world use cases and identify rare but crucial long-tail risks. Additionally, these static tests fail to adapt to the rapid evolution of LLMs, making it hard to evaluate timely alignment issues. To address these challenges, we propose ALI-Agent, an evaluation framework that leverages the autonomous abilities of LLM-powered agents to conduct in-depth and adaptive alignment assessments. ALI-Agent operates through two principal stages: Emulation and Refinement. During the Emulation stage, ALI-Agent automates the generation of realistic test scenarios. In the Refinement stage, it iteratively refines the scenarios to probe long-tail risks. Specifically, ALI-Agent incorporates a memory module to guide test scenario generation, a tool-using module to reduce human labor in tasks such as evaluating feedback from target LLMs, and an action module to refine tests. Extensive experiments across three aspects of human values--stereotypes, morality, and legality--demonstrate that ALI-Agent, as a general evaluation framework, effectively identifies model misalignment. Systematic analysis also validates that the generated test scenarios represent meaningful use cases, as well as integrate enhanced measures to probe long-tail risks. | ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluation | [
"Jingnan Zheng",
"Han Wang",
"An Zhang",
"Tai D. Nguyen",
"Jun Sun",
"Tat-Seng Chua"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/sophiezheng998/ali-agent"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KYHma7hzjr | @inproceedings{
laguna2024beyond,
title={Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?},
author={Sonia Laguna and Ri{\v{c}}ards Marcinkevi{\v{c}}s and Moritz Vandenhirtz and Julia E Vogt},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KYHma7hzjr}
} | Recently, interpretable machine learning has re-explored concept bottleneck models (CBM). An advantage of this model class is the user's ability to intervene on predicted concept values, affecting the downstream output. In this work, we introduce a method to perform such concept-based interventions on *pretrained* neural networks, which are not interpretable by design, only given a small validation set with concept labels. Furthermore, we formalise the notion of *intervenability* as a measure of the effectiveness of concept-based interventions and leverage this definition to fine-tune black boxes. Empirically, we explore the intervenability of black-box classifiers on synthetic tabular and natural image benchmarks. We focus on backbone architectures of varying complexity, from simple, fully connected neural nets to Stable Diffusion. We demonstrate that the proposed fine-tuning improves intervention effectiveness and often yields better-calibrated predictions. To showcase the practical utility of our techniques, we apply them to deep chest X-ray classifiers and show that fine-tuned black boxes are more intervenable than CBMs. Lastly, we establish that our methods are still effective under vision-language-model-based concept annotations, alleviating the need for a human-annotated validation set. | Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? | [
"Sonia Laguna",
"Ričards Marcinkevičs",
"Moritz Vandenhirtz",
"Julia E Vogt"
] | NeurIPS.cc/2024/Conference | 2401.13544 | [
"https://github.com/sonialagunac/beyond-cbm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KYHVBsEHuC | @inproceedings{
jasni2024diffupac,
title={DiffuPac: Contextual Mimicry in Adversarial Packets Generation via Diffusion Model},
author={Abdullah Bin Jasni and Akiko Manada and Kohei Watabe},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KYHVBsEHuC}
} | In domains of cybersecurity, recent advancements in Machine Learning (ML) and Deep Learning (DL) have significantly enhanced Network Intrusion Detection Systems (NIDS), improving the effectiveness of cybersecurity operations. However, attackers have also leveraged ML/DL to develop sophisticated models that generate adversarial packets capable of evading NIDS detection. Consequently, defenders must study and analyze these models to prepare for the evasion attacks that exploit NIDS detection mechanisms. Unfortunately, conventional generation models often rely on unrealistic assumptions about attackers' knowledge of NIDS components, making them impractical for real-world scenarios. To address this issue, we present DiffuPac, a first-of-its-kind generation model designed to generate adversarial packets that evade detection without relying on specific NIDS components. DiffuPac integrates a pre-trained Bidirectional Encoder Representations from Transformers (BERT) with diffusion model, which, through its capability for conditional denoising and classifier-free guidance, effectively addresses the real-world constraint of limited attacker knowledge. By concatenating malicious packets with contextually relevant normal packets and applying targeted noising only to the malicious packets, DiffuPac seamlessly blends adversarial packets into genuine network traffic. Through evaluations on real-world datasets, we demonstrate that DiffuPac achieves strong evasion capabilities against sophisticated NIDS, outperforming conventional methods by an average of 6.69 percentage points, while preserving the functionality and practicality of the generated adversarial packets. | DiffuPac: Contextual Mimicry in Adversarial Packets Generation via Diffusion Model | [
"Abdullah Bin Jasni",
"Akiko Manada",
"Kohei Watabe"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KY07A73F3Y | @inproceedings{
gupta2024pretrained,
title={Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control},
author={Gunshi Gupta and Karmesh Yadav and Yarin Gal and Dhruv Batra and Zsolt Kira and Cong Lu and Tim G. J. Rudner},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KY07A73F3Y}
} | Embodied AI agents require a fine-grained understanding of the physical world mediated through visual and language inputs. Such capabilities are difficult to learn solely from task-specific data. This has led to the emergence of pre-trained vision-language models as a tool for transferring representations learned from internet-scale data to downstream tasks and new domains. However, commonly used contrastively trained representations such as in CLIP have been shown to fail at enabling embodied agents to gain a sufficiently fine-grained scene understanding—a capability vital for control. To address this shortcoming, we consider representations from pre-trained text-to-image diffusion models, which are explicitly optimized to generate images from text prompts and as such, contain text-conditioned representations that reflect highly fine-grained visuo-spatial information. Using pre-trained text-to-image diffusion models, we construct Stable Control Representations which allow learning downstream control policies that generalize to complex, open-ended environments. We show that policies learned using Stable Control Representations are competitive with state-of-the-art representation learning approaches across a broad range of simulated control settings, encompassing challenging manipulation and navigation tasks. Most notably, we show that Stable Control Representations enable learning policies that exhibit state-of-the-art performance on OVMM, a difficult open-vocabulary navigation benchmark. | Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control | [
"Gunshi Gupta",
"Karmesh Yadav",
"Yarin Gal",
"Dhruv Batra",
"Zsolt Kira",
"Cong Lu",
"Tim G. J. Rudner"
] | NeurIPS.cc/2024/Conference | 2405.05852 | [
"https://github.com/ykarmesh/stable-control-representations"
] | https://huggingface.co/papers/2405.05852 | 0 | 0 | 0 | 7 | [
"ykarmesh/stable-control-representations"
] | [] | [] | [
"ykarmesh/stable-control-representations"
] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=KXUijdMFdG | @inproceedings{
maehara2024deep,
title={Deep Homomorphism Networks},
author={Takanori Maehara and Hoang NT},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KXUijdMFdG}
} | Many real-world graphs are large and have some characteristic subgraph patterns, such as triangles in social networks, cliques in web graphs, and cycles in molecular networks.
Detecting such subgraph patterns is important in many applications; therefore, establishing graph neural networks (GNNs) that can detect such patterns and run fast on large graphs is demanding.
In this study, we propose a new GNN layer, named \emph{graph homomorphism layer}.
It enumerates local subgraph patterns that match the predefined set of patterns $\mathcal{P}^\bullet$, applies non-linear transformations to node features, and aggregates them along with the patterns.
By stacking these layers, we obtain a deep GNN model called \emph{deep homomorphism network (DHN)}.
The expressive power of the DHN is completely characterised by the set of patterns generated from $\mathcal{P}^\bullet$ by graph-theoretic operations;
hence, it serves as a useful theoretical tool to analyse the expressive power of many GNN models.
Furthermore, the model runs in the same time complexity as the graph homomorphisms, which is fast in many real-word graphs.
Thus, it serves as a practical and lightweight model that solves difficult problems using domain knowledge. | Deep Homomorphism Networks | [
"Takanori Maehara",
"Hoang NT"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KVAx5tys2p | @inproceedings{
dan2024topofr,
title={Topo{FR}: A Closer Look at Topology Alignment on Face Recognition},
author={Jun Dan and Yang Liu and Jiankang Deng and Haoyu Xie and Siyuan Li and Baigui Sun and Shan Luo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KVAx5tys2p}
} | The field of face recognition (FR) has undergone significant advancements with the rise of deep learning. Recently, the success of unsupervised learning and graph neural networks has demonstrated the effectiveness of data structure information. Considering that the FR task can leverage large-scale training data, which intrinsically contains significant structure information, we aim to investigate how to encode such critical structure information into the latent space. As revealed from our observations, directly aligning the structure information between the input and latent spaces inevitably suffers from an overfitting problem, leading to a structure collapse phenomenon in the latent space. To address this problem, we propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE. Concretely, PTSA uses persistent homology to align the topological structures of the input and latent spaces, effectively preserving the structure information and improving the generalization performance of FR model. To mitigate the impact of hard samples on the latent space structure, SDE accurately identifies hard samples by automatically computing structure damage score (SDS) for each sample, and directs the model to prioritize optimizing these samples. Experimental results on popular face benchmarks demonstrate the superiority of our TopoFR over the state-of-the-art methods. Code and models are available at: https://github.com/modelscope/facechain/tree/main/face_module/TopoFR. | TopoFR: A Closer Look at Topology Alignment on Face Recognition | [
"Jun Dan",
"Yang Liu",
"Jiankang Deng",
"Haoyu Xie",
"Siyuan Li",
"Baigui Sun",
"Shan Luo"
] | NeurIPS.cc/2024/Conference | 2410.10587 | [
"https://github.com/modelscope/facechain"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KT6F5Sw0eg | @inproceedings{
kim2024accelerating,
title={Accelerating Blockwise Parallel Language Models with Draft Refinement},
author={Taehyeon Kim and Ananda Theertha Suresh and Kishore A Papineni and Michael Riley and Sanjiv Kumar and Adrian Benton},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KT6F5Sw0eg}
} | Autoregressive language models have achieved remarkable advancements, yet their potential is often limited by the slow inference speeds associated with sequential token generation. Blockwise parallel decoding (BPD) was proposed by Stern et al. [42] as a method to improve inference speed of language models by simultaneously predicting multiple future tokens, termed block drafts, which are subsequently verified by the autoregressive model. This paper advances the understanding and improvement of block drafts in two ways. First, we analyze token distributions generated across multiple prediction heads. Second, leveraging these insights, we propose algorithms to improve BPD inference speed by refining the block drafts using task-independent \ngram and neural language models as lightweight rescorers. Experiments demonstrate that by refining block drafts of open-sourced Vicuna and Medusa LLMs, the mean accepted token length are increased by 5-25% relative. This results in over a 3x speedup in wall clock time compared to standard autoregressive decoding in open-source 7B and 13B LLMs. | Accelerating Blockwise Parallel Language Models with Draft Refinement | [
"Taehyeon Kim",
"Ananda Theertha Suresh",
"Kishore A Papineni",
"Michael Riley",
"Sanjiv Kumar",
"Adrian Benton"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KSyTvgoSrX | @inproceedings{
nikita2024banded,
title={Banded Square Root Matrix Factorization for Differentially Private Model Training},
author={Kalinin Nikita and Christoph H. Lampert},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KSyTvgoSrX}
} | Current state-of-the-art methods for differentially private model training are based on matrix factorization techniques. However, these methods suffer from high computational overhead because they require numerically solving a demanding optimization problem to determine an approximately optimal factorization prior to the actual model training. In this work, we present a new matrix factorization approach, BSR, which overcomes this computational bottleneck. By exploiting properties of the standard matrix square root, BSR allows to efficiently handle also large-scale problems. For the key scenario of stochastic gradient descent with momentum and weight decay, we even derive analytical expressions for BSR that render the computational overhead negligible. We prove bounds on the approximation quality that hold both in the centralized and in the federated learning setting. Our numerical experiments demonstrate that models trained using BSR perform on par with the best existing methods, while completely avoiding their computational overhead. | Banded Square Root Matrix Factorization for Differentially Private Model Training | [
"Kalinin Nikita",
"Christoph H. Lampert"
] | NeurIPS.cc/2024/Conference | 2405.13763 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KSOkkHm9I7 | @inproceedings{
shen2024superposed,
title={Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass},
author={Ethan Shen and Alan Fan and Sarah M Pratt and Jae Sung Park and Matthew Wallingford and Sham M. Kakade and Ari Holtzman and Ranjay Krishna and Ali Farhadi and Aditya Kusupati},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KSOkkHm9I7}
} | Many applications today provide users with multiple auto-complete drafts as they type, including GitHub's code completion, Gmail's smart compose, and Apple's messaging auto-suggestions. Under the hood, language models support this by running an autoregressive inference pass to provide a draft. Consequently, providing $k$ drafts to the user requires running an expensive language model $k$ times. To alleviate the computation cost of running $k$ inference passes, we propose Superposed Decoding, a new decoding algorithm that generates $k$ drafts at the computation cost of one autoregressive inference pass. We achieve this by feeding a superposition of the most recent token embeddings from the $k$ drafts as input to the next decoding step of the language model. At every inference step we combine the $k$ drafts with the top-$k$ tokens to get $k^2$ new drafts and cache the $k$ most likely options, using an n-gram interpolation with minimal compute overhead to filter out incoherent generations. Our experiments show that $k$ drafts from Superposed Decoding are at least as coherent and factual as Nucleus Sampling and Greedy Decoding respectively, while being at least $2.44\times$ faster for $k\ge3$. In a compute-normalized setting, user evaluations demonstrably favor text generated by Superposed Decoding over Nucleus Sampling. Superposed Decoding can also be combined with other decoding strategies, resulting in universal coverage gains when scaling inference time compute. Code and more examples open-sourced at https://github.com/RAIVNLab/SuperposedDecoding. | Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass | [
"Ethan Shen",
"Alan Fan",
"Sarah M Pratt",
"Jae Sung Park",
"Matthew Wallingford",
"Sham M. Kakade",
"Ari Holtzman",
"Ranjay Krishna",
"Ali Farhadi",
"Aditya Kusupati"
] | NeurIPS.cc/2024/Conference | 2405.18400 | [
"https://github.com/raivnlab/superposeddecoding"
] | https://huggingface.co/papers/2405.18400 | 1 | 1 | 0 | 10 | [] | [] | [
"ethanlshen/SuperposedDecoding"
] | [] | [] | [
"ethanlshen/SuperposedDecoding"
] | 1 | poster |
null | https://openreview.net/forum?id=KQp7dk5YYH | @inproceedings{
miao2024taskagnostic,
title={Task-Agnostic Machine-Learning-Assisted Inference},
author={Jiacheng Miao and Qiongshi Lu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KQp7dk5YYH}
} | Machine learning (ML) is playing an increasingly important role in scientific research. In conjunction with classical statistical approaches, ML-assisted analytical strategies have shown great promise in accelerating research findings. This has also opened a whole field of methodological research focusing on integrative approaches that leverage both ML and statistics to tackle data science challenges. One type of study that has quickly gained popularity employs ML to predict unobserved outcomes in massive samples, and then uses predicted outcomes in downstream statistical inference. However, existing methods designed to ensure the validity of this type of post-prediction inference are limited to very basic tasks such as linear regression analysis. This is because any extension of these approaches to new, more sophisticated statistical tasks requires task-specific algebraic derivations and software implementations, which ignores the massive library of existing software tools already developed for the same scientific problem given observed data. This severely constrains the scope of application for post-prediction inference. To address this challenge, we introduce a novel statistical framework named PSPS for task-agnostic ML-assisted inference. It provides a post-prediction inference solution that can be easily plugged into almost any established data analysis routines. It delivers valid and efficient inference that is robust to arbitrary choice of ML model, allowing nearly all existing statistical frameworks to be incorporated into the analysis of ML-predicted data. Through extensive experiments, we showcase our method’s validity, versatility, and superiority compared to existing approaches. Our software is available at https://github.com/qlu-lab/psps. | Task-Agnostic Machine-Learning-Assisted Inference | [
"Jiacheng Miao",
"Qiongshi Lu"
] | NeurIPS.cc/2024/Conference | 2405.20039 | [
"https://github.com/qlu-lab/PSPS"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KP7EUORJYI | @inproceedings{
xudong2024goalconditioned,
title={Goal-Conditioned On-Policy Reinforcement Learning},
author={Gong Xudong and Feng Dawei and Kele Xu and Bo Ding and Huaimin Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KP7EUORJYI}
} | Existing Goal-Conditioned Reinforcement Learning (GCRL) algorithms are built upon Hindsight Experience Replay (HER), which densifies rewards through hindsight replay and leverages historical goal-achieving information to construct a learning curriculum. However, when the task is characterized by a non-Markovian reward (NMR), whose computation depends on multiple steps of states and actions, HER can no longer densify rewards by treating a single encountered state as the hindsight goal. The lack of informative rewards hinders policy learning, resulting in rolling out failed trajectories. Consequently, the replay buffer is overwhelmed with failed trajectories, impeding the establishment of an applicable curriculum. To circumvent these limitations, we deviate from existing HER-based methods and propose an on-policy GCRL framework, GCPO, which is applicable to both multi-goal Markovian reward (MR) and NMR problems.
GCPO consists of (1) Pre-training from Demonstrations, which pre-trains the policy to possess an initial goal-achieving capability, thereby diminishing the difficulty of subsequent online learning. (2) Online Self-Curriculum Learning, which first estimates the policy's goal-achieving capability based on historical evaluation information and then selects progressively challenging goals for learning based on its current capability. We evaluate GCPO on a challenging multi-goal long-horizon task: fixed-wing UAV velocity vector control. Experimental results demonstrate that GCPO is capable of effectively addressing both multi-goal MR and NMR problems. | Goal-Conditioned On-Policy Reinforcement Learning | [
"Gong Xudong",
"Feng Dawei",
"Kele Xu",
"Bo Ding",
"Huaimin Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KNrwaFEi1u | @inproceedings{
chen2024multiobject,
title={Multi-Object Hallucination in Vision Language Models},
author={Xuweiyi Chen and Ziqiao Ma and Xuejun Zhang and Sihan Xu and Shengyi Qian and Jianing Yang and David Fouhey and Joyce Chai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KNrwaFEi1u}
} | Large vision language models (LVLMs) often suffer from object hallucination, producing objects not present in the given images.
While current benchmarks for object hallucination primarily concentrate on the presence of a single object class rather than individual entities, this work systematically investigates multi-object hallucination, examining how models misperceive (e.g., invent nonexistent objects or become distracted) when tasked with focusing on multiple objects simultaneously.
We introduce Recognition-based Object Probing Evaluation (ROPE), an automated evaluation protocol that considers the distribution of object classes within a single image during testing and uses visual referring prompts to eliminate ambiguity.
With comprehensive empirical studies and analysis of potential factors leading to multi-object hallucination, we found that (1) LVLMs suffer more hallucinations when focusing on multiple objects compared to a single object.
(2) The tested object class distribution affects hallucination behaviors, indicating that LVLMs may follow shortcuts and spurious correlations.
(3) Hallucinatory behaviors are influenced by data-specific factors, salience and frequency, and model intrinsic behaviors.
We hope to enable LVLMs to recognize and reason about multiple objects that often occur in realistic visual scenes, provide insights, and quantify our progress towards mitigating the issues. | Multi-Object Hallucination in Vision Language Models | [
"Xuweiyi Chen",
"Ziqiao Ma",
"Xuejun Zhang",
"Sihan Xu",
"Shengyi Qian",
"Jianing Yang",
"David Fouhey",
"Joyce Chai"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/sled-group/moh"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KNZYJ5zQsG | @inproceedings{
li2024generalized,
title={Generalized Fast Exact Conformalization},
author={Diyang Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KNZYJ5zQsG}
} | Conformal prediction converts nearly any point estimator into a prediction interval under standard assumptions while ensuring valid coverage. However, the extensive computational demands of full conformal prediction are daunting in practice, as it necessitates a comprehensive number of trainings across the entire latent label space. Unfortunately, existing efforts to expedite conformalization often carry strong assumptions and are developed specifically for certain models, or they only offer approximate solution sets. To address this gap, we develop a method for fast exact conformalization of generalized statistical estimation. Our analysis reveals that the structure of the solution path is inherently piecewise smooth, and indicates that utilizing second-order information of difference equations suffices to approximate the entire solution spectrum arbitrarily. We provide a unified view that not only encompasses existing work but also attempts to offer geometric insights. Practically, our framework integrates seamlessly with well-studied numerical solvers. The significant speedups of our algorithm as compared to the existing standard methods are demonstrated across numerous benchmarks. | Generalized Fast Exact Conformalization | [
"Diyang Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KNDUBpWV9b | @inproceedings{
lee2024koala,
title={{KOALA}: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis},
author={Youngwan Lee and Kwanyong Park and Yoorhim Cho and Yong-Ju Lee and Sung Ju Hwang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KNDUBpWV9b}
} | As text-to-image (T2I) synthesis models increase in size, they demand higher inference costs due to the need for more expensive GPUs with larger memory, which makes it challenging to reproduce these models in addition to the restricted access to training datasets. Our study aims to reduce these inference costs and explores how far the generative capabilities of T2I models can be extended using only publicly available datasets and open-source models. To this end, by using the de facto standard text-to-image model, Stable Diffusion XL (SDXL), we present three key practices in building an efficient T2I model: (1) Knowledge distillation: we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and find that self-attention is the most crucial part. (2) Data: despite fewer samples, high-resolution images with rich captions are more crucial than a larger number of low-resolution images with short captions. (3) Teacher: Step-distilled Teacher allows T2I models to reduce the noising steps. Based on these findings, we build two types of efficient text-to-image models, called KOALA-Turbo & -Lightning, with two compact U-Nets (1B & 700M), reducing the model size up to 54% and 69% of the SDXL U-Net. In particular, the KOALA-Lightning-700M is 4 times faster than SDXL while still maintaining satisfactory generation quality. Moreover, unlike SDXL, our KOALA models can generate 1024px high-resolution images on consumer-grade GPUs with 8GB of VRAMs (3060Ti). We believe that our KOALA models will have a significant practical impact, serving as cost-effective alternatives to SDXL for academic researchers and general users in resource-constrained environments. | KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis | [
"Youngwan Lee",
"Kwanyong Park",
"Yoorhim Cho",
"Yong-Ju Lee",
"Sung Ju Hwang"
] | NeurIPS.cc/2024/Conference | 2312.04005 | [
""
] | https://huggingface.co/papers/2312.04005 | 1 | 2 | 0 | 5 | [
"etri-vilab/koala-700m",
"etri-vilab/koala-700m-llava-cap",
"etri-vilab/koala-1b",
"etri-vilab/koala-1b-llava-cap",
"etri-vilab/koala-lightning-700m",
"etri-vilab/koala-lightning-1b"
] | [] | [
"Nymbo/image_gen_supaqueue",
"K00B404/image_gen_supaqueue_game_assets",
"nimo97890/etri-vilab-koala-700m",
"ctxwing/etri-vilab-koala-700m",
"ewftrhyjk/etri-vilab-koala-700m-llava-cap"
] | [
"etri-vilab/koala-700m",
"etri-vilab/koala-700m-llava-cap",
"etri-vilab/koala-1b",
"etri-vilab/koala-1b-llava-cap",
"etri-vilab/koala-lightning-700m",
"etri-vilab/koala-lightning-1b"
] | [] | [
"Nymbo/image_gen_supaqueue",
"K00B404/image_gen_supaqueue_game_assets",
"nimo97890/etri-vilab-koala-700m",
"ctxwing/etri-vilab-koala-700m",
"ewftrhyjk/etri-vilab-koala-700m-llava-cap"
] | 1 | poster |
null | https://openreview.net/forum?id=KLv1VLuMo8 | @inproceedings{
cho2024modelbased,
title={Model-Based Transfer Learning for Contextual Reinforcement Learning},
author={Jung-Hoon Cho and Vindula Jayawardana and Sirui Li and Cathy Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KLv1VLuMo8}
} | Deep reinforcement learning (RL) is a powerful approach to complex decision-making. However, one issue that limits its practical application is its brittleness, sometimes failing to train in the presence of small changes in the environment. Motivated by the success of zero-shot transfer—where pre-trained models perform well on related tasks—we consider the problem of selecting a good set of training tasks to maximize generalization performance across a range of tasks. Given the high cost of training, it is critical to select training tasks strategically, but not well understood how to do so. We hence introduce Model-Based Transfer Learning (MBTL), which layers on top of existing RL methods to effectively solve contextual RL problems. MBTL models the generalization performance in two parts: 1) the performance set point, modeled using Gaussian processes, and 2) performance loss (generalization gap), modeled as a linear function of contextual similarity. MBTL combines these two pieces of information within a Bayesian optimization (BO) framework to strategically select training tasks. We show theoretically that the method exhibits sublinear regret in the number of training tasks and discuss conditions to further tighten regret bounds. We experimentally validate our methods using urban traffic and standard continuous control benchmarks. The experimental results suggest that MBTL can achieve up to 50x improved sample efficiency compared with canonical independent training and multi-task training. Further experiments demonstrate the efficacy of BO and the insensitivity to the underlying RL algorithm and hyperparameters. This work lays the foundations for investigating explicit modeling of generalization, thereby enabling principled yet effective methods for contextual RL. Code is available at https://github.com/jhoon-cho/MBTL/. | Model-Based Transfer Learning for Contextual Reinforcement Learning | [
"Jung-Hoon Cho",
"Vindula Jayawardana",
"Sirui Li",
"Cathy Wu"
] | NeurIPS.cc/2024/Conference | 2408.04498 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KLL70pTQ17 | @inproceedings{
hussing2024oracleefficient,
title={Oracle-Efficient Reinforcement Learning for Max Value Ensembles},
author={Marcel Hussing and Michael Kearns and Aaron Roth and Sikata Bela Sengupta and Jessica Sorrell},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KLL70pTQ17}
} | Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with state space cardinality) and experimentally (where function approximation and policy gradient techniques often scale poorly and suffer from instability and high variance). One line of research attempting to address these difficulties
makes the natural assumption that we are given a collection of base or *constituent* policies (possibly heuristic) upon which we would like to improve in a scalable manner. In this work we aim to compete with the *max-following policy*, which at each state follows the action of whichever constituent policy has the highest value. The max-following policy is always at least as good as the best constituent policy, and may be considerably better. Our main result is an efficient algorithm that learns to compete with the max-following policy, given only access to the constituent policies (but not their value functions). In contrast to prior work in similar settings, our theoretical results require only the minimal assumption of an ERM oracle for value function approximation for the constituent policies (and not the global optimal policy or the max-following policy itself) on samplable distributions. We illustrate our algorithm's experimental effectiveness and behavior on several robotic simulation testbeds. | Oracle-Efficient Reinforcement Learning for Max Value Ensembles | [
"Marcel Hussing",
"Michael Kearns",
"Aaron Roth",
"Sikata Bela Sengupta",
"Jessica Sorrell"
] | NeurIPS.cc/2024/Conference | 2405.16739 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KKrj1vCQaG | @inproceedings{
sun2024rectifid,
title={Rectif{ID}: Personalizing Rectified Flow with Anchored Classifier Guidance},
author={Zhicheng Sun and Zhenhao Yang and Yang Jin and Haozhe Chi and Kun Xu and Kun Xu and Liwei Chen and Hao Jiang and Yang Song and Kun Gai and Yadong MU},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KKrj1vCQaG}
} | Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidance, a training-free technique that steers diffusion models using an existing classifier, for personalized image generation. Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators. Moreover, its solving procedure proves to be stable when anchored to a reference flow trajectory, with a convergence guarantee. The derived method is implemented on rectified flow with different off-the-shelf image discriminators, delivering advantageous personalization results for human faces, live subjects, and certain objects. Code is available at https://github.com/feifeiobama/RectifID. | RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance | [
"Zhicheng Sun",
"Zhenhao Yang",
"Yang Jin",
"Haozhe Chi",
"Kun Xu",
"Kun Xu",
"Liwei Chen",
"Hao Jiang",
"Yang Song",
"Kun Gai",
"Yadong MU"
] | NeurIPS.cc/2024/Conference | 2405.14677 | [
"https://github.com/feifeiobama/RectifID"
] | https://huggingface.co/papers/2405.14677 | 2 | 9 | 0 | 12 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=KIrZmlTA92 | @inproceedings{
holt2024datadriven,
title={Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models},
author={Samuel Holt and Zhaozhi Qian and Tennison Liu and Jim Weatherall and Mihaela van der Schaar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KIrZmlTA92}
} | The discovery of dynamical systems is crucial across a range of fields, including pharmacology, epidemiology, and physical sciences. *Accurate* and *interpretable* modeling of these systems is essential for understanding complex temporal processes, optimizing interventions, and minimizing adverse effects. In pharmacology, for example, precise modeling of drug dynamics is vital to maximize therapeutic efficacy while minimizing patient harm, as in chemotherapy. However, current models, often developed by human experts, are limited by high cost, lack of scalability, and restriction to existing human knowledge. In this paper, we present the **Data-Driven Discovery (D3)** framework, a novel approach leveraging Large Language Models (LLMs) to iteratively discover and refine interpretable models of dynamical systems, demonstrated here with pharmacological applications. Unlike traditional methods, D3 enables the LLM to propose, acquire, and integrate new features, validate, and compare dynamical systems models, uncovering new insights into pharmacokinetics. Experiments on a pharmacokinetic Warfarin dataset reveal that D3 identifies a new plausible model that is well-fitting, highlighting its potential for precision dosing in clinical applications. | Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models | [
"Samuel Holt",
"Zhaozhi Qian",
"Tennison Liu",
"Jim Weatherall",
"Mihaela van der Schaar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KI5TANE02e | @inproceedings{
mimikos-stamatopoulos2024scorebased,
title={Score-based generative models are provably robust: an uncertainty quantification perspective},
author={Nikiforos Mimikos-Stamatopoulos and Benjamin Zhang and Markos Katsoulakis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KI5TANE02e}
} | Through an uncertainty quantification (UQ) perspective, we show that score-based generative models (SGMs) are provably robust to the multiple sources of error in practical implementation. Our primary tool is the Wasserstein uncertainty propagation (WUP) theorem, a *model-form UQ* bound that describes how the $L^2$ error from learning the score function propagates to a Wasserstein-1 ($\mathbf{d}_1$) ball around the true data distribution under the evolution of the Fokker-Planck equation. We show how errors due to (a) finite sample approximation, (b) early stopping, (c) score-matching objective choice, (d) score function parametrization expressiveness, and (e) reference distribution choice, impact the quality of the generative model in terms of a $\mathbf{d}_1$ bound of computable quantities. The WUP theorem relies on Bernstein estimates for Hamilton-Jacobi-Bellman partial differential equations (PDE) and the regularizing properties of diffusion processes. Specifically, *PDE regularity theory* shows that *stochasticity* is the key mechanism ensuring SGM algorithms are provably robust. The WUP theorem applies to integral probability metrics beyond $\mathbf{d}_1$, such as the total variation distance and the maximum mean discrepancy. Sample complexity and generalization bounds in $\mathbf{d}_1$ follow directly from the WUP theorem. Our approach requires minimal assumptions, is agnostic to the manifold hypothesis and avoids absolute continuity assumptions for the target distribution. Additionally, our results clarify the *trade-offs* among multiple error sources in SGMs. | Score-based generative models are provably robust: an uncertainty quantification perspective | [
"Nikiforos Mimikos-Stamatopoulos",
"Benjamin Zhang",
"Markos Katsoulakis"
] | NeurIPS.cc/2024/Conference | 2405.15754 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KHcB1drMRX | @inproceedings{
huang2024accelerating,
title={Accelerating Pre-training of Multimodal {LLM}s via Chain-of-Sight},
author={Ziyuan Huang and Kaixiang Ji and Biao Gong and Zhiwu Qing and Qing-Long Zhang and Kecheng Zheng and Jian Wang and Jingdong Chen and Ming Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KHcB1drMRX}
} | This paper introduces Chain-of-Sight, a vision-language bridge module that accelerates the pre-training of Multimodal Large Language Models (MLLMs).
Our approach employs a sequence of visual resamplers that capture visual details at various spacial scales.
This architecture not only leverages global and local visual contexts effectively, but also facilitates the flexible extension of visual tokens through a compound token scaling strategy, allowing up to a 16x increase in the token count post pre-training.
Consequently, Chain-of-Sight requires significantly fewer visual tokens in the pre-training phase compared to the fine-tuning phase.
This intentional reduction of visual tokens during pre-training notably accelerates the pre-training process, cutting down the wall-clock training time by $\sim$73\%.
Empirical results on a series of vision-language benchmarks reveal that the pre-train acceleration through Chain-of-Sight is achieved without sacrificing performance, matching or surpassing the standard pipeline of utilizing all visual tokens throughout the entire training process.
Further scaling up the number of visual tokens for pre-training leads to stronger performances, competitive to existing approaches in a series of benchmarks. | Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight | [
"Ziyuan Huang",
"Kaixiang Ji",
"Biao Gong",
"Zhiwu Qing",
"Qing-Long Zhang",
"Kecheng Zheng",
"Jian Wang",
"Jingdong Chen",
"Ming Yang"
] | NeurIPS.cc/2024/Conference | 2407.15819 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=KHX0dKXdqH | @inproceedings{
ruan2024causal,
title={Causal Imitation for Markov Decision Processes: a Partial Identification Approach},
author={Kangrui Ruan and Junzhe Zhang and Xuan Di and Elias Bareinboim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KHX0dKXdqH}
} | Imitation learning enables an agent to learn from expert demonstrations when the performance measure is unknown and the reward signal is not specified. Standard imitation methods do not generally apply when the learner and the expert's sensory capabilities mismatch and demonstrations are contaminated with unobserved confounding bias. To address these challenges, recent advancements in causal imitation learning have been pursued. However, these methods often require access to underlying causal structures that might not always be available, posing practical challenges.
In this paper, we investigate robust imitation learning within the framework of canonical Markov Decision Processes (MDPs) using partial identification, allowing the agent to achieve expert performance even when the system dynamics are not uniquely determined from the confounded expert demonstrations. Specifically, first, we theoretically demonstrate that when unobserved confounders (UCs) exist in an MDP, the learner is generally unable to imitate expert performance. We then explore imitation learning in partially identifiable settings --- either transition distribution or reward function is non-identifiable from the available data and knowledge. Augmenting the celebrated GAIL method (Ho \& Ermon, 2016), our analysis leads to two novel causal imitation algorithms that can obtain effective policies guaranteed to achieve expert performance. | Causal Imitation for Markov Decision Processes: a Partial Identification Approach | [
"Kangrui Ruan",
"Junzhe Zhang",
"Xuan Di",
"Elias Bareinboim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KFmRMvzAZy | @inproceedings{
schwarzschild2024rethinking,
title={Rethinking {LLM} Memorization through the Lens of Adversarial Compression},
author={Avi Schwarzschild and Zhili Feng and Pratyush Maini and Zachary Chase Lipton and J Zico Kolter},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KFmRMvzAZy}
} | Large language models (LLMs) trained on web-scale datasets raise substantial concerns regarding permissible data usage.
One major question is whether these models "memorize" all their training data or they integrate many data sources in some way more akin to how a human would learn and synthesize information. The answer hinges, to a large degree, on \emph{how we define memorization.} In this work, we propose the Adversarial Compression Ratio (ACR) as a metric for assessing memorization in LLMs. A given string from the training data is considered memorized if it can be elicited by a prompt (much) shorter than the string itself---in other words, if these strings can be ``compressed'' with the model by computing adversarial prompts of fewer tokens. The ACR overcomes the limitations of existing notions of memorization by (i) offering an adversarial view of measuring memorization, especially for monitoring unlearning and compliance; and (ii) allowing for the flexibility to measure memorization for arbitrary strings at a reasonably low compute. Our definition serves as a practical tool for determining when model owners may be violating terms around data usage, providing a potential legal tool and a critical lens through which to address such scenarios. | Rethinking LLM Memorization through the Lens of Adversarial Compression | [
"Avi Schwarzschild",
"Zhili Feng",
"Pratyush Maini",
"Zachary Chase Lipton",
"J Zico Kolter"
] | NeurIPS.cc/2024/Conference | 2404.15146 | [
""
] | https://huggingface.co/papers/2404.15146 | 1 | 0 | 0 | 5 | [] | [] | [
"pratyushmaini/acr_viewer"
] | [] | [] | [
"pratyushmaini/acr_viewer"
] | 1 | poster |
null | https://openreview.net/forum?id=KEe4IUp20I | @inproceedings{
slagle2024spacebyte,
title={SpaceByte: Towards Deleting Tokenization from Large Language Modeling},
author={Kevin Slagle},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KEe4IUp20I}
} | Tokenization is widely used in large language models because it significantly improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity. To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers. We find that performance is significantly improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries. Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures. | SpaceByte: Towards Deleting Tokenization from Large Language Modeling | [
"Kevin Slagle"
] | NeurIPS.cc/2024/Conference | 2404.14408 | [
"https://github.com/kjslag/spacebyte"
] | https://huggingface.co/papers/2404.14408 | 0 | 6 | 2 | 1 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=KAAUvi4kpb | @inproceedings{
mayo2024brainbits,
title={BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?},
author={David Mayo and Christopher Wang and Asa Harbin and Abdulrahman Alabdulkareem and Albert Eaton Shaw and Boris Katz and Andrei Barbu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=KAAUvi4kpb}
} | When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powerful signal extraction from neural recordings. However, in practice, new reconstruction methods could improve performance for at least three other reasons: learning more about the distribution of stimuli, becoming better at reconstructing text or images in general, or exploiting weaknesses in current image and/or text evaluation metrics. Here we disentangle how much of the reconstruction is due to these other factors vs. productively using the neural recordings. We introduce BrainBits, a method that uses a bottleneck to quantify the amount of signal extracted from neural recordings that is actually necessary to reproduce a method's reconstruction fidelity. We find that it takes surprisingly little information from the brain to produce reconstructions with high fidelity. In these cases, it is clear that the priors of the methods' generative models are so powerful that the outputs they produce extrapolate far beyond the neural signal they decode. Given that reconstructing stimuli can be improved independently by either improving signal extraction from the brain or by building more powerful generative models, improving the latter may fool us into thinking we are improving the former. We propose that methods should report a method-specific random baseline, a reconstruction ceiling, and a curve of performance as a function of bottleneck size, with the ultimate goal of using more of the neural recordings. | BrainBits: How Much of the Brain are Generative Reconstruction Methods Using? | [
"David Mayo",
"Christopher Wang",
"Asa Harbin",
"Abdulrahman Alabdulkareem",
"Albert Eaton Shaw",
"Boris Katz",
"Andrei Barbu"
] | NeurIPS.cc/2024/Conference | 2411.02783 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=K9IGlMQpif | @inproceedings{
yang2024smalltolarge,
title={SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models},
author={Yu Yang and Siddhartha Mishra and Jeffrey N Chiang and Baharan Mirzasoleiman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=K9IGlMQpif}
} | Despite the effectiveness of data selection for pretraining and instruction fine-tuning
large language models (LLMs), improving data efficiency in supervised fine-tuning
(SFT) for specialized domains poses significant challenges due to the complexity
of fine-tuning data. To bridge this gap, we introduce an effective and scalable
data selection method for SFT, SmallToLarge (S2L), which trains a small
model, clusters loss trajectories of the examples, and samples from these clusters to
guide data selection for larger models. We prove that during fine-tuning, samples
within the same loss trajectory cluster exhibit similar gradients. Then, we show
that S2L subsets have a bounded gradient error w.r.t. the full data, hence guarantee
convergence to the neighborhood of the optimal solution. We demonstrate through
extensive experiments that S2L significantly improves data efficiency in SFT for
mathematical problem-solving, reducing the training data requirement to just $11$%
of the original MathInstruct dataset to match full dataset performance while
outperforming state-of-the-art data selection algorithms by an average of $4.7$%
across $6$ in- and out-domain evaluation datasets. Remarkably, selecting only 50K
data for SFT, S2L achieves a $32.7$% accuracy on the challenging MATH
benchmark, improving Phi-2 by $16.6$%. In clinical text summarization on the
MIMIC-III dataset, S2L again outperforms training on the full dataset using
only $50$% of the data. Notably, S2L can perform scalable data selection using a
reference model $100\times$ smaller than the target model, proportionally reducing the
computational cost. | SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models | [
"Yu Yang",
"Siddhartha Mishra",
"Jeffrey N Chiang",
"Baharan Mirzasoleiman"
] | NeurIPS.cc/2024/Conference | 2403.07384 | [
""
] | https://huggingface.co/papers/2403.07384 | 1 | 1 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=K5PA3SK2jB | @inproceedings{
nakayama2024provnerf,
title={ProvNe{RF}: Modeling per Point Provenance in Ne{RF}s as a Stochastic Field},
author={Kiyohiro Nakayama and Mikaela Angelina Uy and Yang You and Ke Li and Leonidas Guibas},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=K5PA3SK2jB}
} | Neural radiance fields (NeRFs) have gained popularity with multiple works showing promising results across various applications. However, to the best of our knowledge, existing works do not explicitly model the distribution of training camera poses, or consequently the triangulation quality, a key factor affecting reconstruction quality dating back to classical vision literature. We close this gap with ProvNeRF, an approach that models the provenance for each point -- i.e., the locations where it is likely visible -- of NeRFs as a stochastic field. We achieve this by extending implicit maximum likelihood estimation (IMLE) to functional space with an optimizable objective. We show that modeling per-point provenance during the NeRF optimization enriches the model with information on triangulation leading to improvements in novel view synthesis and uncertainty estimation under the challenging sparse, unconstrained view setting against competitive baselines. The code will be available at https://github.com/georgeNakayama/ProvNeRF. | ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field | [
"Kiyohiro Nakayama",
"Mikaela Angelina Uy",
"Yang You",
"Ke Li",
"Leonidas Guibas"
] | NeurIPS.cc/2024/Conference | 2401.08140 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=K3k4bWuNnk | @inproceedings{
shirzad2024even,
title={Even Sparser Graph Transformers},
author={Hamed Shirzad and Honghao Lin and Balaji Venkatachalam and Ameya Velingker and David Woodruff and Danica J. Sutherland},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=K3k4bWuNnk}
} | Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scaling to large graphs. Sparse attention variants such as Exphormer can help, but may require high-degree augmentations to the input graph for good performance, and do not attempt to sparsify an already-dense input graph. As the learned attention mechanisms tend to use few of these edges, however, such high-degree connections may be unnecessary. We show (empirically and with theoretical backing) that attention scores on graphs are usually quite consistent across network widths, and use this observation to propose a two-stage procedure, which we call Spexphormer: first, train a narrow network on the full augmented graph. Next, use only the active connections to train a wider network on a much sparser graph. We establish theoretical conditions when a narrow network's attention scores can match those of a wide network, and show that Spexphormer achieves good performance with drastically reduced memory requirements on various graph datasets. | Even Sparser Graph Transformers | [
"Hamed Shirzad",
"Honghao Lin",
"Balaji Venkatachalam",
"Ameya Velingker",
"David Woodruff",
"Danica J. Sutherland"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=K3h2kZFz8h | @inproceedings{
rodriguez-soto2024an,
title={An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning},
author={Manel Rodriguez-Soto and Juan Antonio Rodriguez Aguilar and Maite L{\'o}pez-S{\'a}nchez},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=K3h2kZFz8h}
} | Multi-objective reinforcement learning (MORL) is an excellent framework for multi-objective sequential decision-making. MORL employs a utility function to aggregate multiple objectives into one that expresses a user's preferences. However, MORL still misses two crucial theoretical analyses of the properties of utility functions: (1) a characterisation of the utility functions for which an associated optimal policy exists, and (2) a characterisation of the types of preferences that can be expressed as utility functions. As a result, we formally characterise the families of preferences and utility functions that MORL should focus on: those for which an optimal policy is guaranteed to exist. We expect our theoretical results to promote the development of novel MORL algorithms that exploit our theoretical findings. | An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning | [
"Manel Rodriguez-Soto",
"Juan Antonio Rodriguez Aguilar",
"Maite López-Sánchez"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=K1EG2ABzNE | @inproceedings{
alkhouri2024image,
title={Image Reconstruction Via Autoencoding Sequential Deep Image Prior},
author={Ismail Alkhouri and Shijun Liang and Evan Bell and Qing Qu and Rongrong Wang and Saiprasad Ravishankar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=K1EG2ABzNE}
} | Recently, Deep Image Prior (DIP) has emerged as an effective unsupervised one-shot learner, delivering competitive results across various image recovery problems. This method only requires the noisy measurements and a forward operator, relying solely on deep networks initialized with random noise to learn and restore the structure of the data. However, DIP is notorious for its vulnerability to overfitting due to the overparameterization of the network. Building upon insights into the impact of the DIP input and drawing inspiration from the gradual denoising process in cutting-edge diffusion models, we introduce Autoencoding Sequential DIP (aSeqDIP) for image reconstruction. This method progressively denoises and reconstructs the image through a sequential optimization of network weights. This is achieved using an input-adaptive DIP objective, combined with an autoencoding regularization term. Compared to diffusion models, our method does not require training data and outperforms other DIP-based methods in mitigating noise overfitting while maintaining a similar number of parameter updates as Vanilla DIP. Through extensive experiments, we validate the effectiveness of our method in various image reconstruction tasks, such as MRI and CT reconstruction, as well as in image restoration tasks like image denoising, inpainting, and non-linear deblurring. | Image Reconstruction Via Autoencoding Sequential Deep Image Prior | [
"Ismail Alkhouri",
"Shijun Liang",
"Evan Bell",
"Qing Qu",
"Rongrong Wang",
"Saiprasad Ravishankar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Jzog9gvOf6 | @inproceedings{
lu2024progressive,
title={Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images},
author={Zihan Lu and Chenxu Wang and Chunyan Xu and Xiangwei Zheng and Zhen Cui},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Jzog9gvOf6}
} | The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects. Although most existing SAOD methods rely on fixed thresholding to filter pseudo-labels for enhancing detector performance, adapting to aerial objects proves challenging due to the imbalanced probabilities/confidences associated with predicted aerial objects. To address this problem, we propose a novel Progressive Exploration-Conformal Learning (PECL) framework to address the SAOD task, which can adaptively perform the selection of high-quality pseudo-labels in aerial images. Specifically, the pseudo-label exploration can be formulated as a decision-making paradigm by adopting a conformal pseudo-label explorer and a multi-clue selection evaluator. The conformal pseudo-label explorer learns an adaptive policy by maximizing the cumulative reward, which can decide how to select these high-quality candidates by leveraging their essential characteristics and inter-instance contextual information. The multi-clue selection evaluator is designed to evaluate the explorer-guided pseudo-label selections by providing an instructive feedback for policy optimization. Finally, the explored pseudo-labels can be adopted to guide the optimization of aerial object detector in a closed-looping progressive fashion. Comprehensive evaluations on two public datasets demonstrate the superiority of our PECL when compared with other state-of-the-art methods in the sparsely annotated aerial object detection task. | Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images | [
"Zihan Lu",
"Chenxu Wang",
"Chunyan Xu",
"Xiangwei Zheng",
"Zhen Cui"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JzcIKnnOpJ | @inproceedings{
soen2024rejection,
title={Rejection via Learning Density Ratios},
author={Alexander Soen and Hisham Husain and Philip Schulz and Vu Nguyen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JzcIKnnOpJ}
} | Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions.
The predominant approach is to alter the supervised learning pipeline by augmenting typical loss functions, letting model rejection incur a lower loss than an incorrect prediction.
Instead, we propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.
This can be formalized via the optimization of a loss's risk with a $ \phi$-divergence regularization term.
Through this idealized distribution, a rejection decision can be made by utilizing the density ratio between this distribution and the data distribution.
We focus on the setting where our $ \phi $-divergences are specified by the family of $ \alpha $-divergence.
Our framework is tested empirically over clean and noisy datasets. | Rejection via Learning Density Ratios | [
"Alexander Soen",
"Hisham Husain",
"Philip Schulz",
"Vu Nguyen"
] | NeurIPS.cc/2024/Conference | 2405.18686 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JzKFN5fWOk | @inproceedings{
que2024dcpt,
title={D-{CPT} Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models},
author={Haoran Que and Jiaheng Liu and Ge Zhang and Chenchen Zhang and Xingwei Qu and Yinghao Ma and Feiyu Duan and ZhiqiBai and JiakaiWang and Yuanxing Zhang and Xu Tan and Jie Fu and Jiamang Wang and Lin Qu and Wenbo Su and Bo Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JzKFN5fWOk}
} | Continual Pre-Training (CPT) on Large Language Models (LLMs) has been widely used to expand the model’s fundamental understanding of specific downstream domains (e.g., math and code). For the CPT on domain-specific LLMs, one important question is how to choose the optimal mixture ratio between the general-corpus (e.g., Dolma, Slim-pajama) and the downstream domain-corpus. Existing methods usually adopt laborious human efforts by grid-searching on a set of mixture ratios, which require high GPU training consumption costs. Besides, we cannot guarantee the selected ratio is optimal for the specific domain. To address the limitations of existing methods, inspired by the Scaling Law for performance prediction, we propose to investigate the Scaling Law of the Domain-specific Continual Pre-Training (D-CPT Law) to decide the optimal mixture ratio with acceptable training costs for LLMs of different sizes. Specifically, by fitting the D-CPT Law, we can easily predict the general and downstream performance of arbitrary mixture ratios, model sizes, and dataset sizes using small-scale training costs on limited experiments. Moreover, we also extend our standard D-CPT Law on cross-domain settings and propose the Cross-Domain D-CPT Law to predict the D-CPT law of target domains, where very small training costs (about 1\% of the normal training costs) are needed for the target domains. Comprehensive experimental results on six downstream domains demonstrate the effectiveness and generalizability of our proposed D-CPT Law and Cross-Domain D-CPT Law. | D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models | [
"Haoran Que",
"Jiaheng Liu",
"Ge Zhang",
"Chenchen Zhang",
"Xingwei Qu",
"Yinghao Ma",
"Feiyu Duan",
"ZhiqiBai",
"JiakaiWang",
"Yuanxing Zhang",
"Xu Tan",
"Jie Fu",
"Jiamang Wang",
"Lin Qu",
"Wenbo Su",
"Bo Zheng"
] | NeurIPS.cc/2024/Conference | 2406.01375 | [
""
] | https://huggingface.co/papers/2406.01375 | 2 | 0 | 0 | 16 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=Jz7Z7KkR94 | @inproceedings{
bankes2024reducr,
title={{REDUCR}: Robust Data Downsampling using Class Priority Reweighting},
author={William Bankes and George Hughes and Ilija Bogunovic and Zi Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Jz7Z7KkR94}
} | Modern machine learning models are becoming increasingly expensive to train for real-world image and text classification tasks, where massive web-scale data is collected in a streaming fashion. To reduce the training cost, online batch selection techniques have been developed to choose the most informative datapoints. However, many existing techniques are not robust to class imbalance and distributional shifts, and can suffer from poor worst-class generalization performance. This work introduces REDUCR, a robust and efficient data downsampling method that uses class priority reweighting. REDUCR reduces the training data while preserving worst-class generalization performance. REDUCR assigns priority weights to datapoints in a class-aware manner using an online learning algorithm. We demonstrate the data efficiency and robust performance of REDUCR on vision and text classification tasks. On web-scraped datasets with imbalanced class distributions, REDUCR significantly improves worst-class test accuracy (and average accuracy), surpassing state-of-the-art methods by around 15\%. | REDUCR: Robust Data Downsampling using Class Priority Reweighting | [
"William Bankes",
"George Hughes",
"Ilija Bogunovic",
"Zi Wang"
] | NeurIPS.cc/2024/Conference | 2312.00486 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JyWAFGCJPl | @inproceedings{
liu2024fine,
title={Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination},
author={Ruochen Liu and Hao Chen and Yuanchen Bei and Qijie Shen and Fangwei Zhong and Senzhang Wang and Jianxin Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JyWAFGCJPl}
} | Recommending out-of-vocabulary (OOV) items is a challenging problem since the in-vocabulary (IV) items have well-trained behavioral embeddings but the OOV items only have content features. Current OOV recommendation models often generate 'makeshift' embeddings for OOV items from content features and then jointly recommend with the `makeshift' OOV item embeddings and the behavioral IV item embeddings. However, merely using the 'makeshift' embedding will result in suboptimal recommendation performance due to the substantial gap between the content feature and the behavioral embeddings. To bridge the gap, we propose a novel **User Sequence IMagination (USIM)** fine-tuning framework, which first imagines the user sequences and then refines the generated OOV embeddings with the user behavioral embeddings. Specifically, we frame the user sequence imagination as a reinforcement learning problem and develop a recommendation-focused reward function to evaluate to what extent a user can help recommend the OOV items. Besides, we propose an embedding-driven transition function to model the embedding transition after imaging a user. USIM has been deployed on a prominent e-commerce platform for months, offering recommendations for millions of OOV items and billions of users. Extensive experiments demonstrate that USIM outperforms traditional generative models in OOV item recommendation performance across traditional collaborative filtering and GNN-based collaborative filtering models. | Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination | [
"Ruochen Liu",
"Hao Chen",
"Yuanchen Bei",
"Qijie Shen",
"Fangwei Zhong",
"Senzhang Wang",
"Jianxin Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=JxlQ2pbyzS | @inproceedings{
gao2024collaborative,
title={Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling},
author={Weibo Gao and Qi Liu and Linan Yue and Fangzhou Yao and Hao Wang and Yin Gu and Zheng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JxlQ2pbyzS}
} | Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances. Leveraging collaborative connections among such similar learners proves valuable in comprehending human learning. Motivated by the success of collaborative modeling in various domains, such as recommender systems, we aim to investigate how collaborative signals among learners contribute to the diagnosis of human cognitive states (i.e., knowledge proficiency) in the context of intelligent education.
The primary challenges lie in identifying implicit collaborative connections and disentangling the entangled cognitive factors of learners for improved explainability and controllability in learner Cognitive Diagnosis (CD). However, there has been no work on CD capable of simultaneously modeling collaborative and disentangled cognitive states. To address this gap, we present Coral, a $\underline{Co}$llabo$\underline{ra}$tive cognitive diagnosis model with disentang$\underline{l}$ed representation learning. Specifically, Coral first introduces a disentangled state encoder to achieve the initial disentanglement of learners' states.
Subsequently, a meticulously designed collaborative representation learning procedure captures collaborative signals. It dynamically constructs a collaborative graph of learners by iteratively searching for optimal neighbors in a context-aware manner. Using the constructed graph, collaborative information is extracted through node representation learning. Finally, a decoding process aligns the initial cognitive states and collaborative states, achieving co-disentanglement with practice performance reconstructions.
Extensive experiments demonstrate the superior performance of Coral, showcasing significant improvements over state-of-the-art methods across several real-world datasets.
Our code is available at https://github.com/bigdata-ustc/Coral. | Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling | [
"Weibo Gao",
"Qi Liu",
"Linan Yue",
"Fangzhou Yao",
"Hao Wang",
"Yin Gu",
"Zheng Zhang"
] | NeurIPS.cc/2024/Conference | 2411.02066 | [
"https://github.com/bigdata-ustc/coral"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JxOQeg1NkH | @inproceedings{
liu2024robomamba,
title={RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation},
author={Jiaming Liu and Mengzhen Liu and Zhenyu Wang and Pengju An and Xiaoqi Li and Kaichen Zhou and Senqiao Yang and Renrui Zhang and Yandong Guo and Shanghang Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JxOQeg1NkH}
} | A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots can handle a range of basic tasks, they still face challenges in two areas: (1) insufficient reasoning ability to tackle complex tasks, and (2) high computational costs for VLA model fine-tuning and inference. The recently proposed state space model (SSM) known as Mamba demonstrates promising capabilities in non-trivial sequence modeling with linear inference complexity. Inspired by this, we introduce RoboMamba, an end-to-end robotic VLA model that leverages Mamba to deliver both robotic reasoning and action capabilities, while maintaining efficient fine-tuning and inference. Specifically, we first integrate the vision encoder with Mamba, aligning visual tokens with language embedding through co-training, empowering our model with visual common sense and robotic-related reasoning. To further equip RoboMamba with SE(3) pose prediction abilities, we explore an efficient fine-tuning strategy with a simple policy head. We find that once RoboMamba possesses sufficient reasoning capability, it can acquire manipulation skills with minimal fine-tuning parameters (0.1\% of the model) and time. In experiments, RoboMamba demonstrates outstanding reasoning capabilities on general and robotic evaluation benchmarks. Meanwhile, our model showcases impressive pose prediction results in both simulation and real-world experiments, achieving inference speeds 3 times faster than existing VLA models. | RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation | [
"Jiaming Liu",
"Mengzhen Liu",
"Zhenyu Wang",
"Pengju An",
"Xiaoqi Li",
"Kaichen Zhou",
"Senqiao Yang",
"Renrui Zhang",
"Yandong Guo",
"Shanghang Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JvlrUFJMbI | @inproceedings{
zhao2024semantic,
title={Semantic Routing via Autoregressive Modeling},
author={Eric Zhao and Pranjal Awasthi and Zhengdao Chen and Sreenivas Gollapudi and Daniel Delling},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JvlrUFJMbI}
} | We study learning-based approaches to semantic route planning, which concerns producing routes in response to rich queries that specify various criteria and preferences. Semantic routing is already widely found in industry applications, especially navigational services like Google Maps; however, existing implementations only support limited route criteria and narrow query sets as they rely on repurposing classical route optimization algorithms. We argue for a learning-based approach to semantic routing as a more scalable and general alternative. To foster interest in this important application of graph learning, we are releasing a large-scale publicly-licensed benchmark for semantic routing consisting of real-world multi-objective navigation problems---expressed via natural language queries---on the richly annotated road networks of US cities. In addition to being intractable with existing approaches to semantic routing, our benchmark poses a significant scaling challenge for graph learning methods. As a proof-of-concept, we show that---at scale---even a standard transformer network is a powerful semantic routing system and achieves non-trivial performance on our benchmark. In the process, we demonstrate a simple solution to the challenge of scaling up graph learning: an autoregressive approach that decomposes semantic routing into smaller ``next-edge'' prediction problems. | Semantic Routing via Autoregressive Modeling | [
"Eric Zhao",
"Pranjal Awasthi",
"Zhengdao Chen",
"Sreenivas Gollapudi",
"Daniel Delling"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JvQnJWIj6m | @inproceedings{
mo2024connecting,
title={Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning},
author={Shentong Mo and Shengbang Tong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JvQnJWIj6m}
} | In recent advancements in unsupervised visual representation learning, the Joint-Embedding Predictive Architecture (JEPA) has emerged as a significant method for extracting visual features from unlabeled imagery through an innovative masking strategy. Despite its success, two primary limitations have been identified: the inefficacy of Exponential Moving Average (EMA) from I-JEPA in preventing entire collapse and the inadequacy of I-JEPA prediction in accurately learning the mean of patch representations. Addressing these challenges, this study introduces a novel framework, namely C-JEPA (Contrastive-JEPA), which integrates the Image-based Joint-Embedding Predictive Architecture with the Variance-Invariance-Covariance Regularization (VICReg) strategy. This integration is designed to effectively learn the variance/covariance for preventing entire collapse and ensuring invariance in the mean of augmented views, thereby overcoming the identified limitations. Through empirical and theoretical evaluations, our work demonstrates that C-JEPA significantly enhances the stability and quality of visual representation learning. When pre-trained on the ImageNet-1K dataset, C-JEPA exhibits rapid and improved convergence in both linear probing and fine-tuning performance metrics. | Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning | [
"Shentong Mo",
"Shengbang Tong"
] | NeurIPS.cc/2024/Conference | 2410.19560 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=Jup0qZxH7U | @inproceedings{
li2024adaptive,
title={Adaptive Layer Sparsity for Large Language Models via Activation Correlation Assessment},
author={Wei Li and Lujun Li and Mark G. Lee and Shengjie Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Jup0qZxH7U}
} | Large Language Models (LLMs) have revolutionized the field of natural language processing with their impressive capabilities. However, their enormous size presents challenges for deploying them in real-world applications. Traditional compression techniques, like pruning, often lead to suboptimal performance due to their uniform pruning ratios and lack of consideration for the varying importance of features across different layers. To address these limitations, we present a novel Adaptive Layer Sparsity (ALS) approach to optimize LLMs. Our approach consists of two key steps. Firstly, we estimate the correlation matrix between intermediate layers by leveraging the concept of information orthogonality. This novel perspective allows for a precise measurement of the importance of each layer across the model. Secondly, we employ a linear optimization algorithm to develop an adaptive sparse allocation strategy based on evaluating the correlation matrix. This strategy enables us to selectively prune features in intermediate layers, achieving fine-grained optimization of the LLM model. Considering the varying importance across different layers, we can significantly reduce the model size without sacrificing performance. We conduct extensive experiments on publicly available language processing datasets, including the LLaMA-V1|V2|V3 family and OPT, covering various benchmarks. Our experimental results validate the effectiveness of our ALS method, showcasing its superiority over previous approaches. The performance gains demonstrate its potential for enhancing LLMs' efficiency and resource utilization. Notably, our approach surpasses the state-of-the-art models Wanda and SparseGPT, showcasing its ability to excel even under high sparsity levels. Codes at: https://github.com/lliai/ALS. | Adaptive Layer Sparsity for Large Language Models via Activation Correlation Assessment | [
"Wei Li",
"Lujun Li",
"Mark G. Lee",
"Shengjie Sun"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JrraNaaZm5 | @inproceedings{
yang2024fewshot,
title={Few-Shot Diffusion Models Escape the Curse of Dimensionality},
author={Ruofeng Yang and Bo Jiang and Cheng Chen and Ruinan Jin and Baoxiang Wang and Shuai Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JrraNaaZm5}
} | While diffusion models have demonstrated impressive performance, there is a growing need for generating samples tailored to specific user-defined concepts. The customized requirements promote the development of few-shot diffusion models, which use limited $n_{ta}$ target samples to fine-tune a pre-trained diffusion model trained on $n_s$ source samples. Despite the empirical success, no theoretical work specifically analyzes few-shot diffusion models. Moreover, the existing results for diffusion models without a fine-tuning phase can not explain why few-shot models generate great samples due to the curse of dimensionality. In this work, we analyze few-shot diffusion models under a linear structure distribution with a latent dimension $d$. From the approximation perspective, we prove that few-shot models have a $\widetilde{O}(n_s^{-2/d}+n_{ta}^{-1/2})$ bound to approximate the target score function, which is better than $n_{ta}^{-2/d}$ results. From the optimization perspective, we consider a latent Gaussian special case and prove that the optimization problem has a closed-form minimizer. This means few-shot models can directly obtain an approximated minimizer without a complex optimization process. Furthermore, we also provide the accuracy bound $\widetilde{O}(1/n_{ta}+1/\sqrt{n_s})$ for the empirical solution, which still has better dependence on $n_{ta}$ compared to $n_s$. The results of the real-world experiments also show that the models obtained by only fine-tuning the encoder and decoder specific to the target distribution can produce novel images with the target feature, which supports our theoretical results. | Few-Shot Diffusion Models Escape the Curse of Dimensionality | [
"Ruofeng Yang",
"Bo Jiang",
"Cheng Chen",
"Ruinan Jin",
"Baoxiang Wang",
"Shuai Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JrYdk3HEnc | @inproceedings{
li2024online,
title={Online Control with Adversarial Disturbance for Continuous-time Linear Systems},
author={Jingwei Li and Jing Dong and Can Chang and Baoxiang Wang and Jingzhao Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JrYdk3HEnc}
} | We study online control for continuous-time linear systems with finite sampling rates, where the objective is to design an online procedure that learns under non-stochastic noise and performs comparably to a fixed optimal linear controller.
We present a novel two-level online algorithm, by integrating a higher-level learning strategy and a lower-level feedback control strategy. This method offers a practical and robust solution for online control, which achieves sublinear regret. Our work provides the first nonasymptotic results for controlling continuous-time linear systems with finite number of interactions with the system. Moreover, we examine how to train an agent in domain randomization environments from a non-stochastic control perspective. By applying our method to the SAC (Soft Actor-Critic) algorithm, we achieved improved results in multiple reinforcement learning tasks within domain randomization environments. Our work provides new insights into non-asymptotic analyses of controlling continuous-time systems. Furthermore, our work brings practical intuition into controller learning under non-stochastic environments. | Online Control with Adversarial Disturbance for Continuous-time Linear Systems | [
"Jingwei Li",
"Jing Dong",
"Can Chang",
"Baoxiang Wang",
"Jingzhao Zhang"
] | NeurIPS.cc/2024/Conference | 2306.01952 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JrIPBXWiS8 | @inproceedings{
shi2024resfusion,
title={Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise},
author={Zhenning Shi and Haoshuai Zheng and Chen Xu and Changsheng Dong and Bin Pan and Xie xueshuo and Along He and Tao Li and Huazhu Fu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JrIPBXWiS8}
} | Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degraded images as conditional input to effectively guide the reverse generation process, without modifying the original denoising diffusion process. However, since the degraded images already include low-frequency information, starting from Gaussian white noise will result in increased sampling steps. We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images. The form of our inference process is consistent with the DDPM. We introduced a weighted residual noise, named resnoise, as the prediction target and explicitly provide the quantitative relationship between the residual term and the noise term in resnoise. By leveraging a smooth equivalence transformation, Resfusion determine the optimal acceleration step and maintains the integrity of existing noise schedules, unifying the training and inference processes. The experimental results demonstrate that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps. Furthermore, Resfusion can be easily applied to image generation and emerges with strong versatility. Our code and model are available at https://github.com/nkicsl/Resfusion. | Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise | [
"Zhenning Shi",
"Haoshuai Zheng",
"Chen Xu",
"Changsheng Dong",
"Bin Pan",
"Xie xueshuo",
"Along He",
"Tao Li",
"Huazhu Fu"
] | NeurIPS.cc/2024/Conference | 2311.14900 | [
"https://github.com/nkicsl/resfusion"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JpqEzPTuv6 | @inproceedings{
lv2024what,
title={What Makes Partial-Label Learning Algorithms Effective?},
author={Jiaqi Lv and Yangfan Liu and Shiyu Xia and Ning Xu and Miao Xu and Gang Niu and Min-Ling Zhang and Masashi Sugiyama and Xin Geng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JpqEzPTuv6}
} | A partial label (PL) specifies a set of candidate labels for an instance and partial-label learning (PLL) trains multi-class classifiers with PLs.
Recently, many methods that incorporate techniques from other domains have shown strong potential.
The expectation that stronger techniques would enhance performance has resulted in prominent PLL methods becoming not only highly complicated but also quite different from one another, making it challenging to choose the best direction for future algorithm design.
While it is exciting to see higher performance, this leaves open a fundamental question: what makes a PLL method effective?
We present a comprehensive empirical analysis of this question and summarize the success of PLL so far into some minimal algorithm design principles.
Our findings reveal that high accuracy on benchmark-simulated datasets with PLs can misleadingly amplify the perceived effectiveness of some general techniques, which may improve representation learning but have limited impact on addressing the inherent challenges of PLs.
We further identify the common behavior among successful PLL methods as a progressive transition from uniform to one-hot pseudo-labels, highlighting the critical role of mini-batch PL purification in achieving top performance.
Based on our findings, we introduce a minimal working algorithm that is surprisingly simple yet effective, and propose an improved strategy to implement the design principles, suggesting a promising direction for improvements in PLL. | What Makes Partial-Label Learning Algorithms Effective? | [
"Jiaqi Lv",
"Yangfan Liu",
"Shiyu Xia",
"Ning Xu",
"Miao Xu",
"Gang Niu",
"Min-Ling Zhang",
"Masashi Sugiyama",
"Xin Geng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Jm2aK3sDJD | @inproceedings{
srivastava2024vlgcbm,
title={{VLG}-{CBM}: Training Concept Bottleneck Models with Vision-Language Guidance},
author={Divyansh Srivastava and Ge Yan and Tsui-Wei Weng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Jm2aK3sDJD}
} | Concept Bottleneck Models (CBMs) provide interpretable prediction by introducing an intermediate Concept Bottleneck Layer (CBL), which encodes human-understandable concepts to explain models' decision. Recent works proposed to utilize Large Language Models (LLMs) and pre-trained Vision-Language Models (VLMs) to automate the training of CBMs, making it more scalable and automated. However, existing approaches still fall short in two aspects: First, the concepts predicted by CBL often mismatch the input image, raising doubts about the faithfulness of interpretation. Second, it has been shown that concept values encode unintended information: even a set of random concepts could achieve comparable test accuracy to state-of-the-art CBMs. To address these critical limitations, in this work, we propose a novel framework called Vision-Language-Guided Concept Bottleneck Model (VLG-CBM) to enable faithful interpretability with the benefits of boosted performance. Our method leverages off-the-shelf open-domain grounded object detectors to provide visually grounded concept annotation, which largely enhances the faithfulness of concept prediction while further improving the model performance. In addition, we propose a new metric called Number of Effective Concepts (NEC) to control the information leakage and provide better interpretability. Extensive evaluations across five standard benchmarks show that our method, VLG-CBM, outperforms existing methods by at least 4.27\% and up to 51.09\% on accuracy at NEC=5, and by at least 0.45\% and up to 29.78\% on average accuracy across different NECs, while preserving both faithfulness and interpretability of the learned concepts as demonstrated in extensive experiments. | VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance | [
"Divyansh Srivastava",
"Ge Yan",
"Tsui-Wei Weng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JlWn80mTJi | @inproceedings{
ravi2024the,
title={The Implicit Bias of Gradient Descent on Separable Multiclass Data},
author={Hrithik Ravi and Clayton Scott and Daniel Soudry and Yutong Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JlWn80mTJi}
} | Implicit bias describes the phenomenon where optimization-based training algorithms, without explicit regularization, show a preference for simple estimators even when more complex estimators have equal objective values. Multiple works have developed the theory of implicit bias for binary classification under the assumption that the loss satisfies an *exponential tail property*. However, there is a noticeable gap in analysis for multiclass classification, with only a handful of results which themselves are restricted to the cross-entropy loss. In this work, we employ the framework of Permutation Equivariant and Relative Margin-based (PERM) losses [Wang and Scott, 2024] to introduce a multiclass extension of the exponential tail property. This class of losses includes not only cross-entropy but also other losses. Using this framework, we extend the implicit bias result of Soudry et al. [2018] to multiclass classification. Furthermore, our proof techniques closely mirror those of the binary case, thus illustrating the power of the PERM framework for bridging the binary-multiclass gap. | The Implicit Bias of Gradient Descent on Separable Multiclass Data | [
"Hrithik Ravi",
"Clayton Scott",
"Daniel Soudry",
"Yutong Wang"
] | NeurIPS.cc/2024/Conference | 2411.01350 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Jkt42QYyEH | @inproceedings{
qu2024livescene,
title={LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Control and Rendering},
author={Delin Qu and Qizhi Chen and Pingrui Zhang and Xianqiang Gao and Bin Zhao and Zhigang Wang and Dong Wang and Xuelong Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Jkt42QYyEH}
} | This paper scales object-level reconstruction to complex scenes, advancing interactive scene reconstruction. We introduce two datasets, OmniSim and InterReal, featuring 28 scenes with multiple interactive objects. To tackle the challenge of inaccurate interactive motion recovery in complex scenes, we propose LiveScene, a scene-level language-embedded interactive radiance field that efficiently reconstructs and controls multiple objects. By decomposing the interactive scene into local deformable fields, LiveScene enables separate reconstruction of individual object motions, reducing memory consumption. Additionally, our interaction-aware language embedding localizes individual interactive objects, allowing for arbitrary control using natural language. Our approach demonstrates significant superiority in novel view synthesis, interactive scene control, and language grounding performance through extensive experiments. Project page: https://livescenes.github.io. | LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Control and Rendering | [
"Delin Qu",
"Qizhi Chen",
"Pingrui Zhang",
"Xianqiang Gao",
"Bin Zhao",
"Zhigang Wang",
"Dong Wang",
"Xuelong Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JjQl8hXJAS | @inproceedings{
wagenmaker2024overcoming,
title={Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World {RL}},
author={Andrew Wagenmaker and Kevin Huang and Liyiming Ke and Kevin Jamieson and Abhishek Gupta},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JjQl8hXJAS}
} | In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap, and then deploy this policy in the real world, with the hope that it generalizes effectively. Such \emph{direct sim2real} transfer is not guaranteed to succeed, however, and in cases where it fails, it is unclear how to best utilize the simulator. In this work, we show that in many regimes, while direct sim2real transfer may fail, we can utilize the simulator to learn a set of \emph{exploratory} policies which enable efficient exploration in the real world. In particular, in the setting of low-rank MDPs, we show that coupling these exploratory policies with simple, practical approaches---least-squares regression oracles and naive randomized exploration---yields a polynomial sample complexity in the real world, an exponential improvement over direct sim2real transfer, or learning without access to a simulator. To the best of our knowledge, this is the first evidence that simulation transfer yields a provable gain in reinforcement learning in settings where direct sim2real transfer fails. We validate our theoretical results on several realistic robotic simulators and a real-world robotic sim2real task, demonstrating that transferring exploratory policies can yield substantial gains in practice as well. | Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL | [
"Andrew Wagenmaker",
"Kevin Huang",
"Liyiming Ke",
"Kevin Jamieson",
"Abhishek Gupta"
] | NeurIPS.cc/2024/Conference | 2410.20254 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Jj2PEAZPWk | @inproceedings{
pan2024distribution,
title={Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation},
author={Zhiyi Pan and Wei Gao and Shan Liu and Ge Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Jj2PEAZPWk}
} | Despite alleviating the dependence on dense annotations inherent to fully supervised methods, weakly supervised point cloud semantic segmentation suffers from inadequate supervision signals. In response to this challenge, we introduce a novel perspective that imparts auxiliary constraints by regulating the feature space under weak supervision. Our initial investigation identifies which distributions accurately characterize the feature space, subsequently leveraging this priori to guide the alignment of the weakly supervised embeddings. Specifically, we analyze the superiority of the mixture of von Mises-Fisher distributions (moVMF) among several common distribution candidates. Accordingly, we develop a Distribution Guidance Network (DGNet), which comprises a weakly supervised learning branch and a distribution alignment branch. Leveraging reliable clustering initialization derived from the weakly supervised learning branch, the distribution alignment branch alternately updates the parameters of the moVMF and the network, ensuring alignment with the moVMF-defined latent space. Extensive experiments validate the rationality and effectiveness of our distribution choice and network design. Consequently, DGNet achieves state-of-the-art performance under multiple datasets and various weakly supervised settings. | Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation | [
"Zhiyi Pan",
"Wei Gao",
"Shan Liu",
"Ge Li"
] | NeurIPS.cc/2024/Conference | 2410.08091 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JiRGxrqHh0 | @inproceedings{
bornstein2024fact,
title={{FACT} or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?},
author={Marco Bornstein and Amrit Bedi and Abdirisak Mohamed and Furong Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JiRGxrqHh0}
} | Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model. While prior mechanisms attempt to solve the free-rider dilemma, none have addressed the issue of truthfulness. In practice, adversarial agents can provide false information to the server in order to cheat its way out of contributing to federated training. In an effort to make free-riding-averse federated mechanisms truthful, and consequently less prone to breaking down in practice, we propose FACT. FACT is the first federated mechanism that: (1) eliminates federated free riding by using a penalty system, (2) ensures agents provide truthful information by creating a competitive environment, and (3) encourages agent participation by offering better performance than training alone. Empirically, FACT avoids free-riding when agents are untruthful, and reduces agent loss by over 4x. | FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding? | [
"Marco Bornstein",
"Amrit Bedi",
"Abdirisak Mohamed",
"Furong Huang"
] | NeurIPS.cc/2024/Conference | 2405.13879 | [
"https://github.com/marcobornstein/fact"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JiQXsLvDls | @inproceedings{
butakov2024mutual,
title={Mutual Information Estimation via Normalizing Flows},
author={Ivan Butakov and Alexander Tolmachev and Sofia Malanchuk and Anna Neopryatnaya and Alexey Frolov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JiQXsLvDls}
} | We propose a novel approach to the problem of mutual information (MI) estimation via introducing a family of estimators based on normalizing flows. The estimator maps original data to the target distribution, for which MI is easier to estimate. We additionally explore the target distributions with known closed-form expressions for MI. Theoretical guarantees are provided to demonstrate that our approach yields MI estimates for the original data. Experiments with high-dimensional data are conducted to highlight the practical advantages of the proposed method. | Mutual Information Estimation via Normalizing Flows | [
"Ivan Butakov",
"Alexander Tolmachev",
"Sofia Malanchuk",
"Anna Neopryatnaya",
"Alexey Frolov"
] | NeurIPS.cc/2024/Conference | 2403.02187 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JhqyeppMiD | @inproceedings{
xu2024shadowcast,
title={Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models},
author={Yuancheng Xu and Jiarui Yao and Manli Shu and Yanchao Sun and Zichu Wu and Ning Yu and Tom Goldstein and Furong Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JhqyeppMiD}
} | Vision-Language Models (VLMs) excel in generating textual responses from visual inputs, but their versatility raises security concerns. This study takes the first step in exposing VLMs’ susceptibility to data poisoning attacks that can manipulate responses to innocuous, everyday prompts. We introduce Shadowcast, a stealthy data poisoning attack where poison samples are visually indistinguishable from benign images with matching texts. Shadowcast demonstrates effectiveness in two attack types. The first is a traditional Label Attack, tricking VLMs into misidentifying class labels, such as confusing Donald Trump for Joe Biden. The second is a novel Persuasion Attack, leveraging VLMs’ text generation capabilities to craft persuasive and seemingly rational narratives for misinformation, such as portraying junk food as healthy. We show that Shadowcast effectively achieves the attacker’s intentions using as few as 50 poison samples. Crucially, the poisoned samples demonstrate transferability across different VLM architectures, posing a significant concern in black-box settings. Moreover, Shadowcast remains potent under realistic conditions involving various text prompts, training data augmentation, and image compression techniques. This work reveals how poisoned VLMs can disseminate convincing yet deceptive misinformation to everyday, benign users, emphasizing the importance of data integrity for responsible VLM deployments. Our code is available at: https://github.com/umd-huang-lab/VLM-Poisoning. | Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models | [
"Yuancheng Xu",
"Jiarui Yao",
"Manli Shu",
"Yanchao Sun",
"Zichu Wu",
"Ning Yu",
"Tom Goldstein",
"Furong Huang"
] | NeurIPS.cc/2024/Conference | 2402.06659 | [
"https://github.com/umd-huang-lab/vlm-poisoning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JfxqomOs60 | @inproceedings{
capitaine2024unravelling,
title={Unravelling in Collaborative Learning},
author={Aymeric Capitaine and Etienne Boursier and Antoine Scheid and Eric Moulines and Michael Jordan and El-Mahdi El-Mhamdi and Alain Oliviero Durmus},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JfxqomOs60}
} | Collaborative learning offers a promising avenue for leveraging decentralized data. However, collaboration in groups of strategic learners is not a given. In this work, we consider strategic agents who wish to train a model together but have sampling distributions of different quality. The collaboration is organized by a benevolent aggregator who gathers samples so as to maximize total welfare, but is unaware of data quality. This setting allows us to shed light on the deleterious effect of adverse selection in collaborative learning. More precisely, we demonstrate that when data quality indices are private, the coalition may undergo a phenomenon known as unravelling, wherein it shrinks up to the point that it becomes empty or solely comprised of the worst agent. We show how this issue can be addressed without making use of external transfers, by proposing a novel method inspired by probabilistic verification. This approach makes the grand coalition a Nash equilibrium with high probability despite information asymmetry, thereby breaking unravelling. | Unravelling in Collaborative Learning | [
"Aymeric Capitaine",
"Etienne Boursier",
"Antoine Scheid",
"Eric Moulines",
"Michael Jordan",
"El-Mahdi El-Mhamdi",
"Alain Oliviero Durmus"
] | NeurIPS.cc/2024/Conference | 2407.14332 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Jf40H5pRW0 | @inproceedings{
hanke2024open,
title={Open {LLM}s are Necessary for Current Private Adaptations and Outperform their Closed Alternatives},
author={Vincent Hanke and Tom Blanchard and Franziska Boenisch and Iyiola Emmanuel Olatunji and Michael Backes and Adam Dziedzic},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Jf40H5pRW0}
} | While open Large Language Models (LLMs) have made significant progress, they still fall short of matching the performance of their closed, proprietary counterparts, making the latter attractive even for the use on highly *private* data.
Recently, various new methods have been proposed to adapt closed LLMs to private data without leaking private information to third parties and/or the LLM provider.
In this work, we analyze the privacy protection and performance of the four most recent methods for private adaptation of closed LLMs.
By examining their threat models and thoroughly comparing their performance under different privacy levels according to differential privacy (DP), various LLM architectures, and multiple datasets for classification and generation tasks, we find that: (1) all the methods leak query data, i.e., the (potentially sensitive) user data that is queried at inference time, to the LLM provider, (2) three out of four methods also leak large fractions of private training data to the LLM provider while the method that protects private data requires a local open LLM, (3) all the methods exhibit lower performance compared to three private gradient-based adaptation methods for *local open LLMs*, and (4) the private adaptation methods for closed LLMs incur higher monetary training and query costs than running the alternative methods on local open LLMs.
This yields the conclusion that, to achieve truly *privacy-preserving LLM adaptations* that yield high performance and more privacy at lower costs, taking into account current methods and models, one should use open LLMs. | Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives | [
"Vincent Hanke",
"Tom Blanchard",
"Franziska Boenisch",
"Iyiola Emmanuel Olatunji",
"Michael Backes",
"Adam Dziedzic"
] | NeurIPS.cc/2024/Conference | 2411.05818 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JZHFRLoqDq | @inproceedings{
kolesov2024energyguided,
title={Energy-Guided Continuous Entropic Barycenter Estimation for General Costs},
author={Alexander Kolesov and Petr Mokrov and Igor Udovichenko and Milena Gazdieva and Gudmund Pammer and Anastasis Kratsios and Evgeny Burnaev and Alexander Korotin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JZHFRLoqDq}
} | Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter task is to take the average of a collection of probability distributions w.r.t. given OT discrepancies. We propose a novel algorithm for approximating the continuous Entropic OT (EOT) barycenter for arbitrary OT cost functions. Our approach is built upon the dual reformulation of the EOT problem based on weak OT, which has recently gained the attention of the ML community. Beyond its novelty, our method enjoys several advantageous properties: (i) we establish quality bounds for the recovered solution; (ii) this approach seamlessly interconnects with the Energy-Based Models (EBMs) learning procedure enabling the use of well-tuned algorithms for the problem of interest; (iii) it provides an intuitive optimization scheme avoiding min-max, reinforce and other intricate technical tricks. For validation, we consider several low-dimensional scenarios and image-space setups, including *non-Euclidean* cost functions. Furthermore, we investigate the practical task of learning the barycenter on an image manifold generated by a pretrained generative model, opening up new directions for real-world applications. Our code is available at https://github.com/justkolesov/EnergyGuidedBarycenters. | Energy-Guided Continuous Entropic Barycenter Estimation for General Costs | [
"Alexander Kolesov",
"Petr Mokrov",
"Igor Udovichenko",
"Milena Gazdieva",
"Gudmund Pammer",
"Anastasis Kratsios",
"Evgeny Burnaev",
"Alexander Korotin"
] | NeurIPS.cc/2024/Conference | 2310.01105 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=JXKbf1d4ib | @inproceedings{
rowland2024nearminimaxoptimal,
title={Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model},
author={Mark Rowland and Li Kevin Wenliang and Remi Munos and Clare Lyle and Yunhao Tang and Will Dabney},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JXKbf1d4ib}
} | We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions in the generative model regime (up to logarithmic factors), the first result of this kind for any distributional RL algorithm. Our analysis also provides new theoretical perspectives on categorical approaches to distributional RL, as well as introducing a new distributional Bellman equation, the stochastic categorical CDF Bellman equation, which we expect to be of independent interest. Finally, we provide an experimental study comparing a variety of model-based distributional RL algorithms, with several key takeaways for practitioners. | Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model | [
"Mark Rowland",
"Li Kevin Wenliang",
"Remi Munos",
"Clare Lyle",
"Yunhao Tang",
"Will Dabney"
] | NeurIPS.cc/2024/Conference | 2402.07598 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JWLiK3kKWQ | @inproceedings{
tian2024mind,
title={Mind the Gap Between Prototypes and Images in Cross-domain Finetuning},
author={Hongduan Tian and Feng Liu and Zhanke Zhou and Tongliang Liu and Chengqi Zhang and Bo Han},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JWLiK3kKWQ}
} | In _cross-domain few-shot classification_ (CFC), recent works mainly focus on adapting a simple transformation head on top of a frozen pre-trained backbone with few labeled data to project embeddings into a task-specific metric space where classification can be performed by measuring similarities between image instance and prototype representations. Technically, an _assumption_ implicitly adopted in such a framework is that the prototype and image instance embeddings share the same representation transformation. However, in this paper, we find that there naturally exists a gap, which resembles the modality gap, between the prototype and image instance embeddings extracted from the frozen pre-trained backbone, and simply applying the same transformation during the adaptation phase constrains exploring the optimal representation distributions and shrinks the gap between prototype and image representations. To solve this problem, we propose a simple yet effective method, _contrastive prototype-image adaptation_ (CoPA), to adapt different transformations for prototypes and images similarly to CLIP by treating prototypes as text prompts.
Extensive experiments on Meta-Dataset demonstrate that CoPA achieves the _state-of-the-art_ performance more efficiently. Meanwhile, further analyses also indicate that CoPA can learn better representation clusters, enlarge the gap, and achieve the minimum validation loss at the enlarged gap. | Mind the Gap Between Prototypes and Images in Cross-domain Finetuning | [
"Hongduan Tian",
"Feng Liu",
"Zhanke Zhou",
"Tongliang Liu",
"Chengqi Zhang",
"Bo Han"
] | NeurIPS.cc/2024/Conference | 2410.12474 | [
"https://github.com/tmlr-group/CoPA"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JVKABhr6mP | @inproceedings{
lee2024meteor,
title={Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models},
author={Byung-Kwan Lee and Chae Won Kim and Beomchan Park and Yong Man Ro},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JVKABhr6mP}
} | The rapid development of large language and vision models (LLVMs) has been driven by advances in visual instruction tuning. Recently, open-source LLVMs have curated high-quality visual instruction tuning datasets and utilized additional vision encoders or multiple computer vision models in order to narrow the performance gap with powerful closed-source LLVMs. These advancements are attributed to multifaceted information required for diverse capabilities, including fundamental image understanding, real-world knowledge about common-sense and non-object concepts (e.g., charts, diagrams, symbols, signs, and math problems), and step-by-step procedures for solving complex questions. Drawing from the multifaceted information, we present a new efficient LLVM, Mamba-based traversal of rationales (Meteor), which leverages multifaceted rationale to enhance understanding and answering capabilities. To embed lengthy rationales containing abundant information, we employ the Mamba architecture, capable of processing sequential data with linear time complexity. We introduce a new concept of traversal of rationale that facilitates efficient embedding of rationale. Subsequently, the backbone multimodal language model (MLM) is trained to generate answers with the aid of rationale. Through these steps, Meteor achieves significant improvements in vision language performances across multiple evaluation benchmarks requiring diverse capabilities, without scaling up the model size or employing additional vision encoders and computer vision models. | Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models | [
"Byung-Kwan Lee",
"Chae Won Kim",
"Beomchan Park",
"Yong Man Ro"
] | NeurIPS.cc/2024/Conference | 2405.15574 | [
"https://github.com/byungkwanlee/meteor"
] | https://huggingface.co/papers/2405.15574 | 4 | 53 | 3 | 4 | [
"BK-Lee/Meteor-MLM",
"BK-Lee/Meteor-Mamba"
] | [
"BK-Lee/Meteor"
] | [
"BK-Lee/Meteor"
] | [
"BK-Lee/Meteor-MLM",
"BK-Lee/Meteor-Mamba"
] | [
"BK-Lee/Meteor"
] | [
"BK-Lee/Meteor"
] | 1 | poster |
null | https://openreview.net/forum?id=JRSyMBBJi6 | @inproceedings{
galkin2024a,
title={A Foundation Model for Zero-shot Logical Query Reasoning},
author={Mikhail Galkin and Jincheng Zhou and Bruno Ribeiro and Jian Tang and Zhaocheng Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JRSyMBBJi6}
} | Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations. Existing CLQA methods that learn parameters bound to certain entity or relation vocabularies can only be applied to the graph they are trained on which requires substantial training time before being deployed on a new graph. Here we present UltraQuery, the first foundation model for inductive reasoning that can zero-shot answer logical queries on any KG. The core idea of UltraQuery is to derive both projections and logical operations as vocabulary-independent functions which generalize to new entities and relations in any KG.
With the projection operation initialized from a pre-trained inductive KG completion model, UltraQuery can solve CLQA on any KG after finetuning on a single dataset. Experimenting on 23 datasets, UltraQuery in the zero-shot inference mode shows competitive or better query answering performance than best available baselines and sets a new state of the art on 15 of them. | A Foundation Model for Zero-shot Logical Query Reasoning | [
"Mikhail Galkin",
"Jincheng Zhou",
"Bruno Ribeiro",
"Jian Tang",
"Zhaocheng Zhu"
] | NeurIPS.cc/2024/Conference | 2404.07198 | [
"https://github.com/DeepGraphLearning/ULTRA"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JPtobPtxKT | @inproceedings{
ma2024visual,
title={Visual Perception by Large Language Model{\textquoteright}s Weights},
author={Feipeng Ma and Hongwei Xue and Yizhou Zhou and Guangting Wang and Fengyun Rao and Shilin Yan and Yueyi Zhang and Siying Wu and Mike Zheng Shou and Xiaoyan Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JPtobPtxKT}
} | Existing Multimodal Large Language Models (MLLMs) follow the paradigm that perceives visual information by aligning visual features with the input space of Large Language Models (LLMs) and concatenating visual tokens with text tokens to form a unified sequence input for LLMs. These methods demonstrate promising results on various vision-language tasks but are limited by the high computational effort due to the extended input sequence resulting from the involvement of visual tokens. In this paper, instead of input space alignment, we propose a novel parameter space alignment paradigm that represents visual information as model weights. For each input image, we use a vision encoder to extract visual features, convert features into perceptual weights, and merge the perceptual weights with LLM's weights. In this way, the input of LLM does not require visual tokens, which reduces the length of the input sequence and greatly improves efficiency. Following this paradigm, we propose VLoRA with the perceptual weights generator. The perceptual weights generator is designed to convert visual features to perceptual weights with low-rank property, exhibiting a form similar to LoRA. The experimental results show that our VLoRA achieves comparable performance on various benchmarks for MLLMs, while significantly reducing the computational costs for both training and inference. Code and models are released at \url{https://github.com/FeipengMa6/VLoRA}. | Visual Perception by Large Language Model’s Weights | [
"Feipeng Ma",
"Hongwei Xue",
"Yizhou Zhou",
"Guangting Wang",
"Fengyun Rao",
"Shilin Yan",
"Yueyi Zhang",
"Siying Wu",
"Mike Zheng Shou",
"Xiaoyan Sun"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JO6T4rEJ32 | @inproceedings{
kymn2024binding,
title={Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps},
author={Christopher Kymn and Sonia Mazelet and Anthony Hitchcock Thomas and Denis Kleyko and Edward Paxon Frady and Friedrich Sommer and Bruno Olshausen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JO6T4rEJ32}
} | We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the vectors representing position and the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also be used to bind different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We provide model analysis of scaling, similarity preservation and convergence behavior as well as experiments demonstrating noise robustness, sub-integer resolution in representing position, and path integration. The model formalizes the computations in the cognitive map and makes testable experimental predictions. | Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps | [
"Christopher Kymn",
"Sonia Mazelet",
"Anthony Hitchcock Thomas",
"Denis Kleyko",
"Edward Paxon Frady",
"Friedrich Sommer",
"Bruno Olshausen"
] | NeurIPS.cc/2024/Conference | 2406.18808 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JNl6h3U3oW | @inproceedings{
you2024shiftaddllm,
title={ShiftAdd{LLM}: Accelerating Pretrained {LLM}s via Post-Training Multiplication-Less Reparameterization},
author={Haoran You and Yipin Guo and Yichao Fu and Wei Zhou and Huihong Shi and Xiaofan Zhang and Souvik Kundu and Amir Yazdanbakhsh and Yingyan Celine Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JNl6h3U3oW}
} | Large language models (LLMs) have shown impressive performance on language tasks but face challenges when deployed on resource-constrained devices due to their extensive parameters and reliance on dense multiplications, resulting in high memory demands and latency bottlenecks. Shift-and-add reparameterization offers a promising solution by replacing costly multiplications with hardware-friendly primitives in both the attention and multi-layer perceptron (MLP) layers of an LLM. However, current reparameterization techniques require training from scratch or full parameter fine-tuning to restore accuracy, which is resource-intensive for LLMs. To address this, we propose accelerating pretrained LLMs through post-training shift-and-add reparameterization, creating efficient multiplication-free models, dubbed ShiftAddLLM. Specifically, we quantize each weight matrix into binary matrices paired with group-wise scaling factors. The associated multiplications are reparameterized into (1) shifts between activations and scaling factors and (2) queries and adds according to the binary matrices. To reduce accuracy loss, we present a multi-objective optimization method to minimize both weight and output activation reparameterization errors. Additionally, based on varying sensitivity across layers to reparameterization, we develop an automated bit allocation strategy to further reduce memory usage and latency. Experiments on five LLM families and eight tasks consistently validate the effectiveness of ShiftAddLLM, achieving average perplexity reductions of 5.6 and 22.7 points at comparable or lower latency compared to the most competitive quantized LLMs at 3- and 2-bit precision, respectively, and more than 80% memory and energy reductions over the original LLMs. Codes and models are available at https://github.com/GATECH-EIC/ShiftAddLLM. | ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization | [
"Haoran You",
"Yipin Guo",
"Yichao Fu",
"Wei Zhou",
"Huihong Shi",
"Xiaofan Zhang",
"Souvik Kundu",
"Amir Yazdanbakhsh",
"Yingyan Celine Lin"
] | NeurIPS.cc/2024/Conference | 2406.05981 | [
"https://github.com/gatech-eic/shiftaddllm"
] | https://huggingface.co/papers/2406.05981 | 8 | 12 | 0 | 9 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=JNDcFOczOf | @inproceedings{
zhao2024rapbrl,
title={{RA}-Pb{RL}: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning},
author={Yujie Zhao and Jose Efraim Aguilar Escamilla and Weyl Lu and Huazheng Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JNDcFOczOf}
} | Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware measures are requisite. Traditional risk-aware objectives and algorithms are not applicable in such one-episode-reward settings. To address this, we explore and prove the applicability of two risk-aware objectives to PbRL: nested and static quantile risk objectives. We also introduce Risk-Aware- PbRL (RA-PbRL), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings. Our code is available in https://github.com/aguilarjose11/PbRLNeurips. | RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning | [
"Yujie Zhao",
"Jose Efraim Aguilar Escamilla",
"Weyl Lu",
"Huazheng Wang"
] | NeurIPS.cc/2024/Conference | 2410.23569 | [
"https://github.com/aguilarjose11/pbrlneurips"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JMBWTlazjW | @inproceedings{
ivison2024unpacking,
title={Unpacking {DPO} and {PPO}: Disentangling Best Practices for Learning from Preference Feedback},
author={Hamish Ivison and Yizhong Wang and Jiacheng Liu and Zeqiu Wu and Valentina Pyatkin and Nathan Lambert and Noah A. Smith and Yejin Choi and Hannaneh Hajishirzi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JMBWTlazjW}
} | Learning from preference feedback has emerged as an essential step for improving the generation quality and performance of modern language models (LMs). Despite its widespread use, the way preference-based learning is applied varies wildly, with differing data, learning algorithms, and evaluations used, making disentangling the impact of each aspect difficult. In this work, we identify four core aspects of preference-based learning: preference data, learning algorithm, reward model, and policy training prompts, systematically investigate the impact of these components on downstream model performance, and suggest a recipe for strong learning for preference feedback. Our findings indicate that all aspects are important for performance, with better preference data leading to the largest improvements, followed by the choice of learning algorithm, the use of improved reward models, and finally the use of additional unlabeled prompts for policy training. Notably, PPO outperforms DPO by up to 2.5% in math and 1.2% in general domains. High-quality preference data leads to improvements of up to 8% in instruction following and truthfulness. Despite significant gains of up to 5% in mathematical evaluation when scaling up reward models, we surprisingly observe marginal improvements in other categories. | Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback | [
"Hamish Ivison",
"Yizhong Wang",
"Jiacheng Liu",
"Zeqiu Wu",
"Valentina Pyatkin",
"Nathan Lambert",
"Noah A. Smith",
"Yejin Choi",
"Hannaneh Hajishirzi"
] | NeurIPS.cc/2024/Conference | 2406.09279 | [
"https://github.com/allenai/open-instruct"
] | https://huggingface.co/papers/2406.09279 | 2 | 1 | 0 | 9 | [
"allenai/tulu-v2.5-ppo-13b-uf-mean-70b-uf-rm",
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"allenai/tulu-v2.5-ppo-13b-uf-mean-70b-uf-rm-value",
"RichardErkhov/allenai_-_tulu-v2.5-dpo-13b-uf-mean-gguf",
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"allenai/llama-3-tulu-v2.5-8b-uf-mean-70b-uf-rm",
"RichardErkhov/allenai_-_tulu-v2.5-dpo-13b-argilla-orca-pairs-gguf"
] | [
"allenai/tulu-2.5-preference-data",
"allenai/tulu-2.5-prompts"
] | [] | [
"allenai/tulu-v2.5-ppo-13b-uf-mean-70b-uf-rm",
"allenai/tulu-v2.5-dpo-13b-hh-rlhf",
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"allenai/tulu-v2.5-ppo-13b-uf-mean-70b-uf-rm-value",
"RichardErkhov/allenai_-_tulu-v2.5-dpo-13b-uf-mean-gguf",
"allenai/llama-3.1-tulu-2-8b-uf-mean-rm",
"allenai/llama-3.1-tulu-2-70b-uf-mean-rm",
"allenai/llama-3-tulu-v2.5-8b-uf-mean-8b-uf-rm",
"allenai/llama-3-tulu-v2.5-8b-uf-mean-70b-uf-rm",
"RichardErkhov/allenai_-_tulu-v2.5-dpo-13b-argilla-orca-pairs-gguf"
] | [
"allenai/tulu-2.5-preference-data",
"allenai/tulu-2.5-prompts"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=JM0IQSliol | @inproceedings{
germain2024shape,
title={Shape analysis for time series},
author={Thibaut Germain and Samuel Gruffaz and Charles Truong and Alain Oliviero Durmus and Laurent Oudre},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JM0IQSliol}
} | Analyzing inter-individual variability of physiological functions is particularly appealing in medical and biological contexts to describe or quantify health conditions. Such analysis can be done by comparing individuals to a reference one with time series as biomedical data.
This paper introduces an unsupervised representation learning (URL) algorithm for time series tailored to inter-individual studies. The idea is to represent time series as deformations of a reference time series. The deformations are diffeomorphisms parameterized and learned by our method called TS-LDDMM. Once the deformations and the reference time series are learned, the vector representations of individual time series are given by the parametrization of their corresponding deformation. At the crossroads between URL for time series and shape analysis, the proposed algorithm handles irregularly sampled multivariate time series of variable lengths and provides shape-based representations of temporal data.
In this work, we establish a representation theorem for the graph of a time series and derive its consequences on the LDDMM framework. We showcase the advantages of our representation compared to existing methods using synthetic data and real-world examples motivated by biomedical applications. | Shape analysis for time series | [
"Thibaut Germain",
"Samuel Gruffaz",
"Charles Truong",
"Alain Oliviero Durmus",
"Laurent Oudre"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JL2eMCfDW8 | @inproceedings{
grinwald2024federated,
title={Federated Learning over Connected Modes},
author={Dennis Grinwald and Philipp Wiesner and Shinichi Nakajima},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JL2eMCfDW8}
} | Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local distributions. In this work, we tackle both challenges by leveraging recent advances in \emph{linear mode connectivity} --- identifying a linearly connected low-loss region in the parameter space of neural networks, which we call solution simplex. We propose federated learning over connected modes (\textsc{Floco}), where clients are assigned local subregions in this simplex based on their gradient signals, and together learn the shared global solution simplex. This allows personalization of the client models to fit their local distributions within the degrees of freedom in the solution simplex and homogenizes the update signals for the global simplex training. Our experiments show that \textsc{Floco} accelerates the global training process, and significantly improves the local accuracy with minimal computational overhead in cross-silo federated learning settings. | Federated Learning over Connected Modes | [
"Dennis Grinwald",
"Philipp Wiesner",
"Shinichi Nakajima"
] | NeurIPS.cc/2024/Conference | 2403.03333 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JKEIYQUSUc | @inproceedings{
cheng2024spatialrgpt,
title={Spatial{RGPT}: Grounded Spatial Reasoning in Vision-Language Models},
author={An-Chieh Cheng and Hongxu Yin and Yang Fu and Qiushan Guo and Ruihan Yang and Jan Kautz and Xiaolong Wang and Sifei Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JKEIYQUSUc}
} | Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited. In this work, we introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs’ spatial perception and reasoning capabilities. SpatialRGPT advances VLMs’ spatial understanding through two key innovations: (i) a data curation pipeline that enables effective learning of regional representation from 3D scene graphs, and (ii) a flexible ``plugin'' module for integrating depth information into the visual encoder of existing VLMs. During inference, when provided with user-specified region proposals, SpatialRGPT can accurately perceive their relative directions and distances. Additionally, we propose SpatialRGBT-Bench, a benchmark with ground-truth 3D annotations encompassing indoor, outdoor, and simulated environments, for evaluating 3D spatial cognition in Vision-Language Models (VLMs). Our results demonstrate that SpatialRGPT significantly enhances performance in spatial reasoning tasks, both with and without local region prompts. The model also exhibits strong generalization capabilities, effectively reasoning about complex spatial relations and functioning as a region-aware dense reward annotator for robotic tasks. Code, dataset, and benchmark are released at https://www.anjiecheng.me/SpatialRGPT. | SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models | [
"An-Chieh Cheng",
"Hongxu Yin",
"Yang Fu",
"Qiushan Guo",
"Ruihan Yang",
"Jan Kautz",
"Xiaolong Wang",
"Sifei Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JK728xy8G7 | @inproceedings{
hong2024smoothed,
title={Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention},
author={Susung Hong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JK728xy8G7}
} | Conditional diffusion models have shown remarkable success in visual content generation, producing high-quality samples across various domains, largely due to classifier-free guidance (CFG). Recent attempts to extend guidance to unconditional models have relied on heuristic techniques, resulting in suboptimal generation quality and unintended effects. In this work, we propose Smoothed Energy Guidance (SEG), a novel training- and condition-free approach that leverages the energy-based perspective of the self-attention mechanism to enhance image generation. By defining the energy of self-attention, we introduce a method to reduce the curvature of the energy landscape of attention and use the output as the unconditional prediction. Practically, we control the curvature of the energy landscape by adjusting the Gaussian kernel parameter while keeping the guidance scale parameter fixed. Additionally, we present a query blurring method that is equivalent to blurring the entire attention weights without incurring quadratic complexity in the number of tokens. In our experiments, SEG achieves a Pareto improvement in both quality and the reduction of side effects. The code is available at https://github.com/SusungHong/SEG-SDXL. | Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention | [
"Susung Hong"
] | NeurIPS.cc/2024/Conference | 2408.00760 | [
"https://github.com/susunghong/seg-sdxl"
] | https://huggingface.co/papers/2408.00760 | 1 | 6 | 2 | 1 | [
"nyanko7/sdxl_smoothed_energy_guidance"
] | [] | [
"John6666/text2tag-llm",
"nyanko7/SEG-SDXL"
] | [
"nyanko7/sdxl_smoothed_energy_guidance"
] | [] | [
"John6666/text2tag-llm",
"nyanko7/SEG-SDXL"
] | 1 | poster |
null | https://openreview.net/forum?id=JJGfCvjpTV | @inproceedings{
holderrieth2024hamiltonian,
title={Hamiltonian Score Matching and Generative Flows},
author={Peter Holderrieth and Yilun Xu and Tommi Jaakkola},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JJGfCvjpTV}
} | Classical Hamiltonian mechanics has been widely used in machine learning in the form of Hamiltonian Monte Carlo for applications with predetermined force fields. In this paper, we explore the potential of deliberately designing force fields for Hamiltonian systems, introducing Hamiltonian velocity predictors (HVPs) as a core tool for constructing energy-based and generative models. We present two innovations: Hamiltonian Score Matching (HSM), which utilizes score functions to augment data by simulating Hamiltonian trajectories, and Hamiltonian Generative Flows (HGFs), a novel generative model that encompasses diffusion models and OT-flow matching as HGFs with zero force fields. We showcase the extended design space of force fields by introducing Oscillation HGFs, a generative model inspired by harmonic oscillators. Our experiments demonstrate that HSM and HGFs rival leading score-matching and generative modeling techniques. Overall, our work systematically elucidates the synergy between Hamiltonian dynamics, force fields, and generative models, thereby opening new avenues for applications of machine learning in physical sciences and dynamical systems. | Hamiltonian Score Matching and Generative Flows | [
"Peter Holderrieth",
"Yilun Xu",
"Tommi Jaakkola"
] | NeurIPS.cc/2024/Conference | 2410.20470 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JInTfcxH3Q | @inproceedings{
tu2024powerpm,
title={Power{PM}: Foundation Model for Power Systems},
author={Shihao Tu and Yupeng Zhang and Jing Zhang and Zhendong Fu and Yin Zhang and Yang Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JInTfcxH3Q}
} | The proliferation of abundant electricity time series (ETS) data presents numerous opportunities for various applications within power systems, including demand-side management, grid stability, and consumer behavior analysis. Deep learning models have advanced ETS modeling by effectively capturing sequence dependence. However, learning a generic representation of ETS data for various applications is challenging due to the inherently complex hierarchical structure of ETS data. Moreover, ETS data exhibits intricate temporal dependencies and is susceptible to the influence of exogenous variables. Furthermore, different instances exhibit diverse electricity consumption behavior. In this paper, we propose a foundation model PowerPM for ETS data, providing a large-scale, off-the-shelf model for power systems. PowerPM consists of a temporal encoder and a hierarchical encoder. The temporal encoder captures temporal dependencies within ETS data, taking into account exogenous variables. The hierarchical encoder models correlations between different levels of hierarchy. Furthermore, PowerPM leverages a novel self-supervised pre-training framework consisting of masked ETS modeling and dual-view contrastive learning. This framework enables PowerPM to capture temporal dependency within ETS windows and aware the discrepancy across ETS windows, providing two different perspectives to learn generic representation. Our experiments span five real-world scenario datasets, including both private and public data. Through pre-training on massive ETS data, PowerPM achieves SOTA
performance on diverse downstream tasks within the private dataset. Notably, when transferred to public datasets, PowerPM retains its edge, showcasing its remarkable generalization ability across various tasks and domains. Moreover, ablation studies and few-shot experiments further substantiate the effectiveness of our model. | PowerPM: Foundation Model for Power Systems | [
"Shihao Tu",
"Yupeng Zhang",
"Jing Zhang",
"Zhendong Fu",
"Yin Zhang",
"Yang Yang"
] | NeurIPS.cc/2024/Conference | 2408.04057 | [
"https://github.com/yuqinie98/patchtst"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JHg9eNuw6p | @inproceedings{
wang2024architect,
title={Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting},
author={Yian Wang and Xiaowen Qiu and Jiageng Liu and Zhehuan Chen and Jiting Cai and Yufei Wang and Tsun-Hsuan Wang and Zhou Xian and Chuang Gan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JHg9eNuw6p}
} | Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. However, generating diverse embodied environments with realistic detail and considerable complexity remains a significant challenge. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language model (LLM) guided scene design, are hindered by limitations such as excessive human effort, reliance on predefined rules or training datasets, and limited 3D spatial reasoning ability. Since pre-trained 2D image generative models better capture scene and object configuration than LLMs, we address these challenges by introducing $\textit{Architect}$, a generative framework that creates complex and realistic 3D embodied environments leveraging diffusion-based 2D image inpainting. In detail, we utilize foundation visual perception models to obtain each generated object from the image and leverage pre-trained depth estimation models to lift the generated 2D image to 3D space. While there are still challenges that the camera parameters and scale of depth are still absent in the generated image, we address those problems by ''controlling'' the diffusion model by $\textit{hierarchical inpainting}$. Specifically, having access to ground-truth depth and camera parameters in simulation, we first render a photo-realistic image of only the background. Then, we inpaint the foreground in this image, passing the geometric cues to the inpainting model in the background, which informs the camera parameters.
This process effectively controls the camera parameters and depth scale for the generated image, facilitating the back-projection from 2D image to 3D point clouds. Our pipeline is further extended to a hierarchical and iterative inpainting process to continuously generate the placement of large furniture and small objects to enrich the scene. This iterative structure brings the flexibility for our method to generate or refine scenes from various starting points, such as text, floor plans, or pre-arranged environments. Experimental results demonstrate that $\textit{Architect}$ outperforms existing methods in producing realistic and complex environments, making it highly suitable for Embodied AI and robotics applications. | Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting | [
"Yian Wang",
"Xiaowen Qiu",
"Jiageng Liu",
"Zhehuan Chen",
"Jiting Cai",
"Yufei Wang",
"Tsun-Hsuan Wang",
"Zhou Xian",
"Chuang Gan"
] | NeurIPS.cc/2024/Conference | 2411.09823 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JFUhBY34SC | @inproceedings{
luo2024explicit,
title={Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization},
author={Haocheng Luo and Tuan Truong and Tung Pham and Mehrtash Harandi and Dinh Phung and Trung Le},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JFUhBY34SC}
} | Sharpness-Aware Minimization (SAM) has attracted significant attention for its effectiveness in improving generalization across various tasks. However, its underlying principles remain poorly understood. In this work, we analyze SAM’s training dynamics using the maximum eigenvalue of the Hessian as a measure of sharpness and propose a third-order stochastic differential equation (SDE), which reveals that the dynamics are driven by a complex mixture of second- and third-order terms. We show that alignment between the perturbation vector and the top eigenvector is crucial for SAM’s effectiveness in regularizing sharpness, but find that this alignment is often inadequate in practice, which limits SAM's efficiency. Building on these insights, we introduce Eigen-SAM, an algorithm that explicitly aims to regularize the top Hessian eigenvalue by aligning the perturbation vector with the leading eigenvector. We validate the effectiveness of our theory and the practical advantages of our proposed approach through comprehensive experiments. Code is available at https://github.com/RitianLuo/EigenSAM. | Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization | [
"Haocheng Luo",
"Tuan Truong",
"Tung Pham",
"Mehrtash Harandi",
"Dinh Phung",
"Trung Le"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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