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null | https://openreview.net/forum?id=dhFHO90INk | @inproceedings{
tagasovska2024implicitly,
title={Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient},
author={Natasa Tagasovska and Vladimir Gligorijevic and Kyunghyun Cho and Andreas Loukas},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dhFHO90INk}
} | Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design use a generative model and a surrogate model (discriminator), requiring large datasets. However, real-world scientific applications often have limited data and complex landscapes, making data-hungry models inefficient or impractical. We propose a new framework, PropEn, inspired by ``matching'', which enables implicit guidance without training a discriminator. By matching each sample with a similar one that has a better property value, we create a larger training dataset that inherently indicates the direction of improvement. Matching, combined with an encoder-decoder architecture, forms a domain-agnostic generative framework for property enhancement. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution, allowing efficient design optimization. Extensive evaluations in toy problems and scientific applications, such as therapeutic protein design and airfoil optimization, demonstrate PropEn's advantages over common baselines. Notably, the protein design results are validated with wet lab experiments, confirming the competitiveness and effectiveness of our approach. Our code is available at https://github.com/prescient-design/propen. | Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient | [
"Natasa Tagasovska",
"Vladimir Gligorijevic",
"Kyunghyun Cho",
"Andreas Loukas"
] | NeurIPS.cc/2024/Conference | 2405.18075 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dg3tI3c2B1 | @inproceedings{
kong2024molecule,
title={Molecule Design by Latent Prompt Transformer},
author={Deqian Kong and Yuhao Huang and Jianwen Xie and Edouardo Honig and Ming Xu and Shuanghong Xue and Pei Lin and Sanping Zhou and Sheng Zhong and Nanning Zheng and Ying Nian Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dg3tI3c2B1}
} | This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constraints serve as conditioning variables.
We propose the Latent Prompt Transformer (LPT), a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution modeled by a neural transformation of Gaussian white noise; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt. LPT can be learned by maximum likelihood estimation on molecule-property pairs. During property optimization, the latent prompt is inferred from target properties and constraints through posterior sampling and then used to guide the autoregressive molecule generation.
After initial training on existing molecules and their properties, we adopt an online learning algorithm to progressively shift the model distribution towards regions that support desired target properties. Experiments demonstrate that LPT not only effectively discovers useful molecules across single-objective, multi-objective, and structure-constrained optimization tasks, but also exhibits strong sample efficiency. | Molecule Design by Latent Prompt Transformer | [
"Deqian Kong",
"Yuhao Huang",
"Jianwen Xie",
"Edouardo Honig",
"Ming Xu",
"Shuanghong Xue",
"Pei Lin",
"Sanping Zhou",
"Sheng Zhong",
"Nanning Zheng",
"Ying Nian Wu"
] | NeurIPS.cc/2024/Conference | 2310.03253 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=dg0hO4M11K | @inproceedings{
liu2024exploring,
title={Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks},
author={Xuyuan Liu and Yinghao Cai and Qihui Yang and Yujun Yan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dg0hO4M11K}
} | Graph Neural Networks (GNNs) have emerged as a dominant approach in graph representation learning, yet they often struggle to capture consistent similarity relationships among graphs. To capture similarity relationships, while graph kernel methods like the Weisfeiler-Lehman subtree (WL-subtree) and Weisfeiler-Lehman optimal assignment (WLOA) perform effectively, they are heavily reliant on predefined kernels and lack sufficient non-linearities. Our work aims to bridge the gap between neural network methods and kernel approaches by enabling GNNs to consistently capture relational structures in their learned representations. Given the analogy between the message-passing process of GNNs and WL algorithms, we thoroughly compare and analyze the properties of WL-subtree and WLOA kernels. We find that the similarities captured by WLOA at different iterations are asymptotically consistent, ensuring that similar graphs remain similar in subsequent iterations, thereby leading to superior performance over the WL-subtree kernel. Inspired by these findings, we conjecture that the consistency in the similarities of graph representations across GNN layers is crucial in capturing relational structures and enhancing graph classification performance. Thus, we propose a loss to enforce the similarity of graph representations to be consistent across different layers. Our empirical analysis verifies our conjecture and shows that our proposed consistency loss can significantly enhance graph classification performance across several GNN backbones on various datasets. | Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks | [
"Xuyuan Liu",
"Yinghao Cai",
"Qihui Yang",
"Yujun Yan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dfqsW38v1X | @inproceedings{
ashkboos2024quarot,
title={QuaRot: Outlier-Free 4-Bit Inference in Rotated {LLM}s},
author={Saleh Ashkboos and Amirkeivan Mohtashami and Maximilian L. Croci and Bo Li and Pashmina Cameron and Martin Jaggi and Dan Alistarh and Torsten Hoefler and James Hensman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dfqsW38v1X}
} | We introduce QuaRot, a new Quantization scheme based on Rotations, which is able to quantize LLMs end-to-end, including all weights, activations, and KV cache in 4 bits. QuaRot rotates LLMs in a way that removes outliers from the hidden state without changing the output, making quantization easier. This computational invariance is applied to the hidden state (residual) of the LLM, as well as to the activations of the feed-forward components, aspects of the attention mechanism, and to the KV cache. The result is a quantized model where all matrix multiplications are performed in 4 bits, without any channels identified for retention in higher precision. Our 4-bit quantized LLAMA2-70B model has losses of at most 0.47 WikiText-2 perplexity and retains 99% of the zero-shot performance. We also show that QuaRot can provide lossless 6 and 8 bit LLAMA-2 models without any calibration data using round-to-nearest quantization. Code is available at github.com/spcl/QuaRot. | QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs | [
"Saleh Ashkboos",
"Amirkeivan Mohtashami",
"Maximilian L. Croci",
"Bo Li",
"Pashmina Cameron",
"Martin Jaggi",
"Dan Alistarh",
"Torsten Hoefler",
"James Hensman"
] | NeurIPS.cc/2024/Conference | 2404.00456 | [
"https://github.com/spcl/quarot"
] | https://huggingface.co/papers/2404.00456 | 5 | 4 | 2 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=dfiXFbECSZ | @inproceedings{
yin2024lofit,
title={LoFiT: Localized Fine-tuning on {LLM} Representations},
author={Fangcong Yin and Xi Ye and Greg Durrett},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dfiXFbECSZ}
} | Recent work in interpretability shows that large language models (LLMs) can be adapted for new tasks in a learning-free way: it is possible to intervene on LLM representations to elicit desired behaviors for alignment. For instance, adding certain bias vectors to the outputs of certain attention heads is reported to boost the truthfulness of models. In this work, we show that localized fine-tuning serves as an effective alternative to such representation intervention methods. We introduce a framework called Localized Fine-Tuning on LLM Representations (LoFiT), which identifies a subset of attention heads that are most important for learning a specific task, then trains offset vectors to add to the model's hidden representations at those selected heads. LoFiT localizes to a sparse set of heads (3%-10%) and learns the offset vectors from limited training data, comparable to the settings used for representation intervention. For truthfulness and reasoning tasks, we find that LoFiT's intervention vectors are more effective for LLM adaptation than vectors from representation intervention methods such as Inference-time Intervention. We also find that the localization step is important: selecting a task-specific set of attention heads can lead to higher performance than intervening on heads selected for a different task. Finally, across 7 tasks we study, LoFiT achieves comparable performance to other parameter-efficient fine-tuning methods such as LoRA, despite modifying 20x-200x fewer parameters than these methods. | LoFiT: Localized Fine-tuning on LLM Representations | [
"Fangcong Yin",
"Xi Ye",
"Greg Durrett"
] | NeurIPS.cc/2024/Conference | 2406.01563 | [
"https://github.com/fc2869/lo-fit"
] | https://huggingface.co/papers/2406.01563 | 1 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=deZpmEfmTo | @inproceedings{
jiang2024domain,
title={Domain Adaptation for Large-Vocabulary Object Detectors},
author={Kai Jiang and Jiaxing Huang and Weiying Xie and Jie Lei and Yunsong Li and Ling Shao and Shijian Lu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=deZpmEfmTo}
} | Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data. However, LVDs often struggle in recognizing the located objects due to domain discrepancy in data distribution and object vocabulary. At the other end, recent vision-language foundation models such as CLIP demonstrate superior open-vocabulary recognition capability.
This paper presents KGD, a Knowledge Graph Distillation technique that exploits the implicit knowledge graphs (KG) in CLIP for effectively adapting LVDs to various downstream domains.
KGD consists of two consecutive stages: 1) KG extraction that employs CLIP to encode downstream domain data as nodes and their feature distances as edges, constructing KG that inherits the rich semantic relations in CLIP explicitly;
and 2) KG encapsulation that transfers the extracted KG into LVDs to enable accurate cross-domain object classification.
In addition, KGD can extract both visual and textual KG independently, providing complementary vision and language knowledge for object localization and object classification in detection tasks over various downstream domains.
Experiments over multiple widely adopted detection benchmarks show that KGD outperforms the state-of-the-art consistently by large margins.
Codes will be released. | Domain Adaptation for Large-Vocabulary Object Detectors | [
"Kai Jiang",
"Jiaxing Huang",
"Weiying Xie",
"Jie Lei",
"Yunsong Li",
"Ling Shao",
"Shijian Lu"
] | NeurIPS.cc/2024/Conference | 2401.06969 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dc4xbVfdzy | @inproceedings{
lv2024decision,
title={Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline {RL}},
author={Qi Lv and Xiang Deng and Gongwei Chen and Michael Y Wang and Liqiang Nie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dc4xbVfdzy}
} | While the conditional sequence modeling with the transformer architecture has demonstrated its effectiveness in dealing with offline reinforcement learning (RL) tasks, it is struggle to handle out-of-distribution states and actions.
Existing work attempts to address this issue by data augmentation with the learned policy or adding extra constraints with the value-based RL algorithm. However, these studies still fail to overcome the following challenges: (1) insufficiently utilizing the historical temporal information among inter-steps, (2) overlooking the local intra-step relationships among states, actions and return-to-gos (RTGs), (3) overfitting suboptimal trajectories with noisy labels. To address these challenges, we propose $\textbf{D}$ecision $\textbf{M}$amba ($\textbf{DM}$), a novel multi-grained state space model (SSM) with a self-evolving policy learning strategy.
DM explicitly models the historical hidden state to extract the temporal information by using the mamba architecture. To capture the relationship among state-action-RTG triplets, a fine-grained SSM module is designed and integrated into the original coarse-grained SSM in mamba, resulting in a novel mamba architecture tailored for offline RL. Finally, to mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations. Extensive experiments on various tasks show that DM outperforms other baselines substantially. | Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL | [
"Qi Lv",
"Xiang Deng",
"Gongwei Chen",
"Michael Y Wang",
"Liqiang Nie"
] | NeurIPS.cc/2024/Conference | 2406.05427 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dbnEf790Kv | @inproceedings{
lingsch2024fuse,
title={{FUSE}: Fast Unified Simulation and Estimation for {PDE}s},
author={Levi E. Lingsch and Dana Grund and Siddhartha Mishra and Georgios Kissas},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dbnEf790Kv}
} | The joint prediction of continuous fields and statistical estimation of the underlying discrete parameters is a common problem for many physical systems, governed by PDEs. Hitherto, it has been separately addressed by employing operator learning surrogates for field prediction while using simulation-based inference (and its variants) for statistical parameter determination. Here, we argue that solving both problems within the same framework can lead to consistent gains in accuracy and robustness. To this end, we propose a novel and flexible formulation of the operator learning problem that jointly predicts continuous quantities and infers distributions of discrete parameters, thereby amortizing the cost of both the inverse and the surrogate models to a joint pre-training step. We present the capabilities of the proposed methodology for predicting continuous and discrete biomarkers in full-body haemodynamics simulations under different levels of missing information. We also consider a test case for atmospheric large-eddy simulation of a two-dimensional dry cold bubble, where we infer both continuous time-series and information about the system's conditions. We present comparisons against different baselines to showcase significantly increased accuracy in both the inverse and the surrogate tasks. | FUSE: Fast Unified Simulation and Estimation for PDEs | [
"Levi E. Lingsch",
"Dana Grund",
"Siddhartha Mishra",
"Georgios Kissas"
] | NeurIPS.cc/2024/Conference | 2405.14558 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dao67XTSPd | @inproceedings{
yan2024deltadock,
title={DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking},
author={Jiaxian Yan and ZAIXI ZHANG and Jintao Zhu and Kai Zhang and Jianfeng Pei and Qi Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dao67XTSPd}
} | Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods, particularly those leveraging geometric deep learning (GDL), have demonstrated significant efficiency and accuracy advantages over traditional sampling methods. Despite these advancements, current methods are often tailored for specific docking settings, and limitations such as the neglect of protein side-chain structures, difficulties in handling large binding pockets, and challenges in predicting physically valid structures exist. To accommodate various docking settings and achieve accurate, efficient, and physically reliable docking, we propose a novel two-stage docking framework, DeltaDock, consisting of pocket prediction and site-specific docking. We innovatively reframe the pocket prediction task as a pocket-ligand alignment problem rather than direct prediction in the first stage. Then we follow a bi-level coarse-to-fine iterative refinement process to perform site-specific docking. Comprehensive experiments demonstrate the superior performance of DeltaDock. Notably, in the blind docking setting, DeltaDock achieves a 31\% relative improvement over the docking success rate compared with the previous state-of-the-art GDL model
DiffDock. With the consideration of physical validity, this improvement increases to about 300\%. | DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking | [
"Jiaxian Yan",
"ZAIXI ZHANG",
"Jintao Zhu",
"Kai Zhang",
"Jianfeng Pei",
"Qi Liu"
] | NeurIPS.cc/2024/Conference | 2410.11224 | [
"https://github.com/jiaxianyan/DeltaDock"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=da0ZJatRCN | @inproceedings{
belakaria2024active,
title={Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes},
author={Syrine Belakaria and Benjamin Letham and Jana Doppa and Barbara E Engelhardt and Stefano Ermon and Eytan Bakshy},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=da0ZJatRCN}
} | We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input variables, e.g., in vehicle safety experimentation, we study the impact of the thickness of various components on safety objectives. Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value. We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models.
We showcase the first application of active learning directly to DGSMs, and develop tractable uncertainty reduction and information gain acquisition functions for these measures. Through comprehensive evaluation on synthetic and real-world problems, our study demonstrates how these active learning acquisition strategies substantially enhance the sample efficiency of DGSM estimation, particularly with limited evaluation budgets. Our work paves the way for more efficient and accurate sensitivity analysis in various scientific and engineering applications. | Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes | [
"Syrine Belakaria",
"Benjamin Letham",
"Jana Doppa",
"Barbara E Engelhardt",
"Stefano Ermon",
"Eytan Bakshy"
] | NeurIPS.cc/2024/Conference | 2407.09739 | [
"https://github.com/belakaria/al-gsa-dgsms"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dYIqAZXQNV | @inproceedings{
nkansah2024generalizing,
title={Generalizing {CNN}s to graphs with learnable neighborhood quantization},
author={Isaac Osafo Nkansah and Neil Gallagher and Ruchi Sandilya and Conor Liston and Logan Grosenick},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dYIqAZXQNV}
} | Convolutional neural networks (CNNs) have led to a revolution in analyzing array data. However, many important sources of data, such as biological and social networks, are naturally structured as graphs rather than arrays, making the design of graph neural network (GNN) architectures that retain the strengths of CNNs an active and exciting area of research. Here, we introduce Quantized Graph Convolution Networks (QGCNs), the first framework for GNNs that formally and directly extends CNNs to graphs. QGCNs do this by decomposing the convolution operation into non-overlapping sub-kernels, allowing them to fit graph data while reducing to a 2D CNN layer on array data. We generalize this approach to graphs of arbitrary size and dimension by approaching sub-kernel assignment as a learnable multinomial assignment problem. Integrating this approach into a residual network architecture, we demonstrate performance that matches or exceeds other state-of-the-art GNNs on benchmark graph datasets and for predicting properties of nonlinear dynamics on a new finite element graph dataset. In summary, QGCNs are a novel GNN framework that generalizes CNNs and their strengths to graph data, allowing for more accurate and expressive models. | Generalizing CNNs to graphs with learnable neighborhood quantization | [
"Isaac Osafo Nkansah",
"Neil Gallagher",
"Ruchi Sandilya",
"Conor Liston",
"Logan Grosenick"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dY4YGqvfgW | @inproceedings{
saad2024on,
title={On Weak Regret Analysis for Dueling Bandits},
author={El Mehdi Saad and Alexandra Carpentier and Tom{\'a}{\v{s}} Koc{\'a}k and Nicolas Verzelen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dY4YGqvfgW}
} | We consider the problem of $K$-armed dueling bandits in the stochastic setting, under the sole assumption of the existence of a Condorcet winner. We study the objective of weak regret minimization, where the learner doesn't incur any loss if one of the selected arms is a Condorcet winner—unlike strong regret minimization, where the learner has to select the Condorcet winner twice to incur no loss. This study is particularly motivated by practical scenarios such as content recommendation and online advertising, where frequently only one optimal choice out of the two presented options is necessary to achieve user satisfaction or engagement. This necessitates the development of strategies with more exploration. While existing literature introduces strategies for weak regret with constant bounds (that do not depend on the time horizon), the optimality of these strategies remains an unresolved question. This problem turns out to be really challenging as the optimal regret should heavily depend on the full structure of the dueling problem at hand, and in particular on whether the Condorcet winner has a large minimal optimality gap with the other arms. Our contribution is threefold: first, when said optimality gap is not negligible compared to other properties of the gap matrix, we characterize the optimal budget as a function of $K$ and the optimality gap. Second, we propose a new strategy called \wrtinf that achieves this optimal regret and improves over the state-of-the-art both in $K$ and the optimality gap. When the optimality gap is negligible, we propose another algorithm that outperforms our first algorithm, highlighting the subtlety of this dueling bandit problem. Finally, we provide numerical simulations to assess our theoretical findings. | On Weak Regret Analysis for Dueling Bandits | [
"El Mehdi Saad",
"Alexandra Carpentier",
"Tomáš Kocák",
"Nicolas Verzelen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dWwin2uGYE | @inproceedings{
vandermeulen2024breaking,
title={Breaking the curse of dimensionality in structured density estimation},
author={Robert A. Vandermeulen and Wai Ming Tai and Bryon Aragam},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dWwin2uGYE}
} | We consider the problem of estimating a structured multivariate density, subject to Markov conditions implied by an undirected graph. In the worst case, without Markovian assumptions, this problem suffers from the curse of dimensionality. Our main result shows how the curse of dimensionality can be avoided or greatly alleviated under the Markov property, and applies to arbitrary graphs. While existing results along these lines focus on sparsity or manifold assumptions, we introduce a new graphical quantity called ``graph resilience'' and show that it dictates the optimal sample complexity. Surprisingly, although one might expect the sample complexity of this problem to scale with local graph parameters such as the degree, this turns out not to be the case. Through explicit examples, we compute uniform deviation bounds and illustrate how the curse of dimensionality in density estimation can thus be circumvented. Notable examples where the rate improves substantially include sequential, hierarchical, and spatial data. | Breaking the curse of dimensionality in structured density estimation | [
"Robert A. Vandermeulen",
"Wai Ming Tai",
"Bryon Aragam"
] | NeurIPS.cc/2024/Conference | 2410.07685 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dVqZ0a7LdP | @inproceedings{
dey2024remap,
title={Re{MAP}: Neural Model Reprogramming with Network Inversion and Retrieval-Augmented Mapping for Adaptive Motion Forecasting},
author={Sharmita Dey and Sarath Ravindran Nair},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dVqZ0a7LdP}
} | Mobility impairment caused by limb loss, aging, stroke, and other movement deficiencies is a significant challenge faced by millions of individuals worldwide. Advanced assistive technologies, such as prostheses and orthoses, have the potential to greatly improve the quality of life for such individuals. A critical component in the design of these technologies is the accurate forecasting of reference joint motion for impaired limbs, which is hindered by the scarcity of joint locomotion data available for these patients. To address this, we propose ReMAP, a novel model repurposing strategy that leverages deep learning's reprogramming property, incorporating network inversion principles and retrieval-augmented mapping. Our approach adapts models originally designed for able-bodied individuals to forecast joint motion in limb-impaired patients without altering model parameters. We demonstrate the efficacy of ReMAP through extensive empirical studies on data from below-knee amputated patients, showcasing significant improvements over traditional transfer learning and fine-tuning methods. These findings have significant implications for advancing assistive technology and mobility for patients with amputations, stroke, or aging. | ReMAP: Neural Model Reprogramming with Network Inversion and Retrieval-Augmented Mapping for Adaptive Motion Forecasting | [
"Sharmita Dey",
"Sarath Ravindran Nair"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dRJJt0Ji48 | @inproceedings{
liu2024retrievalaugmented,
title={Retrieval-Augmented Diffusion Models for Time Series Forecasting},
author={Jingwei Liu and Ling Yang and Hongyan Li and Shenda Hong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dRJJt0Ji48}
} | While time series diffusion models have received considerable focus from many recent works, the performance of existing models remains highly unstable. Factors limiting time series diffusion models include insufficient time series datasets and the absence of guidance. To address these limitations, we propose a Retrieval-Augmented Time series Diffusion model (RATD). The framework of RATD consists of two parts: an embedding-based retrieval process and a reference-guided diffusion model. In the first part, RATD retrieves the time series that are most relevant to historical time series from the database as references. The references are utilized to guide the denoising process in the second part. Our approach allows leveraging meaningful samples within the database to aid in sampling, thus maximizing the utilization of datasets. Meanwhile, this reference-guided mechanism also compensates for the deficiencies of existing time series diffusion models in terms of guidance. Experiments and visualizations on multiple datasets demonstrate the effectiveness of our approach, particularly in complicated prediction tasks. Our code is available at https://github.com/stanliu96/RATD | Retrieval-Augmented Diffusion Models for Time Series Forecasting | [
"Jingwei Liu",
"Ling Yang",
"Hongyan Li",
"Shenda Hong"
] | NeurIPS.cc/2024/Conference | 2410.18712 | [
"https://github.com/stanliu96/RATD"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dQ9ji8e9qQ | @inproceedings{
zhang2024adversarial,
title={Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models},
author={Yihao Zhang and Zeming Wei and Jun Sun and Meng Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dQ9ji8e9qQ}
} | Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an \textbf{A}dversarial \textbf{R}epresentation \textbf{E}ngineering (\textbf{ARE}) framework to provide a unified and interpretable approach for conceptual model editing without compromising baseline performance. Experiments on multiple tasks demonstrate the effectiveness of ARE in various model editing scenarios. Our code and data are available at \url{https://github.com/Zhang-Yihao/Adversarial-Representation-Engineering}. | Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models | [
"Yihao Zhang",
"Zeming Wei",
"Jun Sun",
"Meng Sun"
] | NeurIPS.cc/2024/Conference | 2404.13752 | [
"https://github.com/zhang-yihao/adversarial-representation-engineering"
] | https://huggingface.co/papers/2404.13752 | 0 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=dOJ6CqWDf1 | @inproceedings{
zhou2024weaktostrong,
title={Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models},
author={Zhanhui Zhou and Zhixuan Liu and Jie Liu and Zhichen Dong and Chao Yang and Yu Qiao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dOJ6CqWDf1}
} | Large language models are usually fine-tuned to align with human preferences. However, fine-tuning a large language model can be challenging. In this work, we introduce $\textit{weak-to-strong search}$, framing the alignment of a large language model as a test-time greedy search to maximize the log-probability difference between small tuned and untuned models while sampling from the frozen large model. This method serves both as (1) a compute-efficient model up-scaling strategy that avoids directly tuning the large model and as (2) an instance of weak-to-strong generalization that enhances a strong model with weak test-time guidance.
Empirically, we demonstrate the flexibility of weak-to-strong search across different tasks. In controlled-sentiment generation and summarization, we use tuned and untuned $\texttt{gpt2}$s to improve the alignment of large models without additional training. Crucially, in a more difficult instruction-following benchmark, AlpacaEval 2.0, we show that reusing off-the-shelf small models (e.g., $\texttt{zephyr-7b-beta}$ and its untuned version) can improve the length-controlled win rates of both white-box and black-box large models against $\texttt{gpt-4-turbo}$ (e.g., $34.4\% \rightarrow 37.9\%$ for $\texttt{Llama-3-70B-Instruct}$ and $16.0\% \rightarrow 20.1\%$ for $\texttt{gpt-3.5-turbo-instruct}$), despite the small models' low win rates $\approx 10.0\%$. | Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models | [
"Zhanhui Zhou",
"Zhixuan Liu",
"Jie Liu",
"Zhichen Dong",
"Chao Yang",
"Yu Qiao"
] | NeurIPS.cc/2024/Conference | 2405.19262 | [
"https://github.com/zhziszz/weak-to-strong-search"
] | https://huggingface.co/papers/2405.19262 | 0 | 0 | 0 | 6 | [] | [
"ZHZisZZ/imdb_preference"
] | [] | [] | [
"ZHZisZZ/imdb_preference"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=dLr4H7Uj4H | @inproceedings{
zhang2024learning,
title={Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression},
author={Xi Zhang and Xiaolin Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dLr4H7Uj4H}
} | It is customary to deploy uniform scalar quantization in the end-to-end optimized Neural image compression methods, instead of more powerful vector quantization, due to the high complexity of the latter. Lattice vector quantization (LVQ), on the other hand, presents a compelling alternative, which can exploit inter-feature dependencies more effectively while keeping computational efficiency almost the same as scalar quantization. However, traditional LVQ structures are designed/optimized for uniform source distributions, hence nonadaptive and suboptimal for real source distributions of latent code space for Neural image compression tasks. In this paper, we propose a novel learning method to overcome this weakness by designing the rate-distortion optimal lattice vector quantization (OLVQ) codebooks with respect to the sample statistics of the latent features to be compressed. By being able to better fit the LVQ structures to any given latent sample distribution, the proposed OLVQ method improves the rate-distortion performances of the existing quantization schemes in neural image compression significantly, while retaining the amenability of uniform scalar quantization. | Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression | [
"Xi Zhang",
"Xiaolin Wu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dLnduWGTB4 | @inproceedings{
faria2024quest,
title={{QUEST}: Quality-Aware Metropolis-Hastings Sampling for Machine Translation},
author={Gon{\c{c}}alo Faria and Sweta Agrawal and Ant{\'o}nio Farinhas and Ricardo Rei and Jos{\'e} G. C. de Souza and Andre Martins},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dLnduWGTB4}
} | An important challenge in machine translation (MT) is to generate high-quality and diverse translations.
Prior work has shown that the estimated likelihood from the MT model correlates poorly with translation quality.
In contrast, quality evaluation metrics (such as COMET or BLEURT) exhibit high correlations with human judgments, which has motivated their use as rerankers (such as quality-aware and minimum Bayes risk decoding). However, relying on a single translation with high estimated quality increases the chances of "gaming the metric''.
In this paper, we address the problem of sampling a set of high-quality and diverse translations.
We provide a simple and effective way to avoid over-reliance on noisy quality estimates by using them as the energy function of a Gibbs distribution. Instead of looking for a mode in the distribution, we generate multiple samples from high-density areas through the Metropolis-Hastings algorithm, a simple Markov chain Monte Carlo approach.
The results show that our proposed method leads to high-quality and diverse outputs across multiple language pairs (English$\leftrightarrow$\{German, Russian\}) with two strong decoder-only LLMs (Alma-7b, Tower-7b). | QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation | [
"Gonçalo Faria",
"Sweta Agrawal",
"António Farinhas",
"Ricardo Rei",
"José G. C. de Souza",
"Andre Martins"
] | NeurIPS.cc/2024/Conference | 2406.00049 | [
"https://github.com/deep-spin/quest-decoding"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dJUb9XRoZI | @inproceedings{
huang2024constrained,
title={Constrained Diffusion with Trust Sampling},
author={William Huang and Yifeng Jiang and Tom Van Wouwe and Karen Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dJUb9XRoZI}
} | Diffusion models have demonstrated significant promise in various generative tasks; however, they often struggle to satisfy challenging constraints. Our approach addresses this limitation by rethinking training-free loss-guided diffusion from an optimization perspective. We formulate a series of constrained optimizations throughout the inference process of a diffusion model. In each optimization, we allow the sample to take multiple steps along the gradient of the proxy constraint function until we can no longer trust the proxy, according to the variance at each diffusion level. Additionally, we estimate the state manifold of diffusion model to allow for early termination when the sample starts to wander away from the state manifold at each diffusion step. Trust sampling effectively balances between following the unconditional diffusion model and adhering to the loss guidance, enabling more flexible and accurate constrained generation. We demonstrate the efficacy of our method through extensive experiments on complex tasks, and in drastically different domains of images and 3D motion generation, showing significant improvements over existing methods in terms of generation quality. Our implementation is available at https://github.com/will-s-h/trust-sampling. | Constrained Diffusion with Trust Sampling | [
"William Huang",
"Yifeng Jiang",
"Tom Van Wouwe",
"Karen Liu"
] | NeurIPS.cc/2024/Conference | 2411.10932 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dJ9KzkQ0oH | @inproceedings{
giacobbe2024neural,
title={Neural Model Checking},
author={Mirco Giacobbe and Daniel Kroening and Abhinandan Pal and Michael Tautschnig},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dJ9KzkQ0oH}
} | We introduce a machine learning approach to model checking temporal logic, with application to formal hardware verification. Model checking answers the question of whether every execution of a given system satisfies a desired temporal logic specification. Unlike testing, model checking provides formal guarantees. Its application is expected standard in silicon design and the EDA industry has invested decades into the development of performant symbolic model checking algorithms. Our new approach combines machine learning and symbolic reasoning by using neural networks as formal proof certificates for linear temporal logic. We train our neural certificates from randomly generated executions of the system and we then symbolically check their validity using satisfiability solving which, upon the affirmative answer, establishes that the system provably satisfies the specification. We leverage the expressive power of neural networks to represent proof certificates as well as the fact that checking a certificate is much simpler than finding one. As a result, our machine learning procedure for model checking is entirely unsupervised, formally sound, and practically effective. We experimentally demonstrate that our method outperforms the state-of-the-art academic and commercial model checkers on a set of standard hardware designs written in SystemVerilog. | Neural Model Checking | [
"Mirco Giacobbe",
"Daniel Kroening",
"Abhinandan Pal",
"Michael Tautschnig"
] | NeurIPS.cc/2024/Conference | 2410.23790 | [
"https://github.com/aiverification/neuralmc"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dIktpSgK4F | @inproceedings{
pan2024dissecting,
title={Dissecting Query-Key Interaction in Vision Transformers},
author={Xu Pan and Aaron Philip and Ziqian Xie and Odelia Schwartz},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dIktpSgK4F}
} | Self-attention in vision transformers is often thought to perform perceptual grouping where tokens attend to other tokens with similar embeddings, which could correspond to semantically similar features of an object. However, attending to dissimilar tokens can be beneficial by providing contextual information. We propose to analyze the query-key interaction by the singular value decomposition of the interaction matrix (i.e. ${\textbf{W}_q}^\top\textbf{W}_k$). We find that in many ViTs, especially those with classification training objectives, early layers attend more to similar tokens, while late layers show increased attention to dissimilar tokens, providing evidence corresponding to perceptual grouping and contextualization, respectively. Many of these interactions between features represented by singular vectors are interpretable and semantic, such as attention between relevant objects, between parts of an object, or between the foreground and background. This offers a novel perspective on interpreting the attention mechanism, which contributes to understanding how transformer models utilize context and salient features when processing images. | Dissecting Query-Key Interaction in Vision Transformers | [
"Xu Pan",
"Aaron Philip",
"Ziqian Xie",
"Odelia Schwartz"
] | NeurIPS.cc/2024/Conference | 2405.14880 | [
"https://github.com/schwartz-cnl/DissectingViT"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=dIVb5C0QFf | @inproceedings{
yang2024metaaligner,
title={MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models},
author={Kailai Yang and Zhiwei Liu and Qianqian Xie and Jimin Huang and Tianlin Zhang and Sophia Ananiadou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dIVb5C0QFf}
} | Recent advancements in large language models (LLMs) focus on aligning to heterogeneous human expectations and values via multi-objective preference alignment. However, existing methods are dependent on the policy model parameters, which require high-cost repetition of their alignment algorithms for each new policy model, and they cannot expand to unseen objectives due to their static alignment objectives. In this work, we propose Meta-Objective Aligner (MetaAligner), the first policy-agnostic and generalizable method for multi-objective preference alignment.
MetaAligner models multi-objective alignment into three stages: (1) dynamic objectives reformulation algorithm reorganizes traditional alignment datasets to supervise the model on performing flexible alignment across different objectives; (2) conditional weak-to-strong correction paradigm aligns the weak outputs of fixed policy models to approach strong outputs with higher preferences in the corresponding alignment objectives, enabling plug-and-play inferences on any policy models, which significantly reduces training costs and facilitates alignment on close-source policy models; (3) generalizable inference method flexibly adjusts target objectives by updating their text descriptions in the prompts, facilitating generalizable alignment to unseen objectives.
Experimental results show that MetaAligner achieves significant and balanced improvements in multi-objective alignments on 10 state-of-the-art policy models, and saves up to 93.63% of GPU training hours compared to previous alignment methods. The model also effectively aligns unseen objectives, marking the first step towards generalizable multi-objective preference alignment. | MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models | [
"Kailai Yang",
"Zhiwei Liu",
"Qianqian Xie",
"Jimin Huang",
"Tianlin Zhang",
"Sophia Ananiadou"
] | NeurIPS.cc/2024/Conference | 2403.17141 | [
"https://github.com/stevekgyang/metaaligner"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dIHXwKjXRE | @inproceedings{
ren2024towards,
title={Towards the Dynamics of a {DNN} Learning Symbolic Interactions},
author={Qihan Ren and Junpeng Zhang and Yang Xu and Yue Xin and Dongrui Liu and Quanshi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dIHXwKjXRE}
} | This study proves the two-phase dynamics of a deep neural network (DNN) learning interactions. Despite the long disappointing view of the faithfulness of post-hoc explanation of a DNN, a series of theorems have been proven [27] in recent years to show that for a given input sample, a small set of interactions between input variables can be considered as primitive inference patterns that faithfully represent a DNN's detailed inference logic on that sample. Particularly, Zhang et al. [41] have observed that various DNNs all learn interactions of different complexities in two distinct phases, and this two-phase dynamics well explains how a DNN changes from under-fitting to over-fitting. Therefore, in this study, we mathematically prove the two-phase dynamics of interactions, providing a theoretical mechanism for how the generalization power of a DNN changes during the training process. Experiments show that our theory well predicts the real dynamics of interactions on different DNNs trained for various tasks. | Towards the Dynamics of a DNN Learning Symbolic Interactions | [
"Qihan Ren",
"Junpeng Zhang",
"Yang Xu",
"Yue Xin",
"Dongrui Liu",
"Quanshi Zhang"
] | NeurIPS.cc/2024/Conference | 2407.19198 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dHIKahbV6G | @inproceedings{
liang2024umfc,
title={{UMFC}: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models},
author={Jiachen Liang and RuiBing Hou and Minyang Hu and Hong Chang and Shiguang Shan and Xilin Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dHIKahbV6G}
} | Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data to adapt to downstream tasks, which could be costly. In this work, we aim to leverage unlabeled data that naturally spans multiple domains to enhance the transferability of vision-language models. Under this unsupervised multi-domain setting, we have identified inherent model bias within CLIP, notably in its visual and text encoders. Specifically, we observe that CLIP’s visual encoder tends to prioritize encoding domain over discriminative category information, meanwhile its text encoder exhibits a preference for domain-relevant classes. To mitigate this model bias, we propose a training-free and label-free feature calibration method, Unsupervised Multi-domain Feature Calibration (UMFC). UMFC estimates image-level biases from domain-specific features and text-level biases from the direction of domain transition. These biases are subsequently subtracted from original image and text features separately, to render them domain-invariant. We evaluate our method on multiple settings including transductive learning and test-time adaptation. Extensive experiments show that our method outperforms CLIP and performs on par with the state-of-the-arts that need additional annotations or optimization.
Our code is available at https://github.com/GIT-LJc/UMFC. | UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models | [
"Jiachen Liang",
"RuiBing Hou",
"Minyang Hu",
"Hong Chang",
"Shiguang Shan",
"Xilin Chen"
] | NeurIPS.cc/2024/Conference | 2411.06921 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dGaMSMeeF8 | @inproceedings{
pedramfar2024from,
title={From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary {DR}-submodular Optimization},
author={Mohammad Pedramfar and Vaneet Aggarwal},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dGaMSMeeF8}
} | This paper introduces the notion of upper-linearizable/quadratizable functions, a class that extends concavity and DR-submodularity in various settings, including monotone and non-monotone cases over different types of convex sets. A general meta-algorithm is devised to convert algorithms for linear/quadratic maximization into ones that optimize upper-linearizable/quadratizable functions, offering a unified approach to tackling concave and DR-submodular optimization problems. The paper extends these results to multiple feedback settings, facilitating conversions between semi-bandit/first-order feedback and bandit/zeroth-order feedback, as well as between first/zeroth-order feedback and semi-bandit/bandit feedback. Leveraging this framework, new algorithms are derived using existing results as base algorithms for convex optimization, improving upon state-of-the-art results in various cases. Dynamic and adaptive regret guarantees are obtained for DR-submodular maximization, marking the first algorithms to achieve such guarantees in these settings. Notably, the paper achieves these advancements with fewer assumptions compared to existing state-of-the-art results, underscoring its broad applicability and theoretical contributions to non-convex optimization. | From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization | [
"Mohammad Pedramfar",
"Vaneet Aggarwal"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dGQtja9X2C | @inproceedings{
panchal2024thinking,
title={Thinking Forward: Memory-Efficient Federated Finetuning of Language Models},
author={Kunjal Panchal and Nisarg Parikh and Sunav Choudhary and Lijun Zhang and Yuriy Brun and Hui Guan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dGQtja9X2C}
} | Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using private data. However, finetuning LLMs using backpropagation requires excessive memory (especially from intermediate activations) for resource-constrained devices. While Forward-mode Auto-Differentiation (AD) can significantly reduce memory footprint from activations, we observe that directly applying it to LLM finetuning results in slow convergence and poor accuracy. In this paper, we introduce Spry, an FL algorithm that splits trainable weights of an LLM among participating clients, such that each client computes gradients using forward-mode AD that are closer estimations of the true gradients. Spry achieves a low memory footprint, high accuracy, and fast convergence. We formally prove that the global gradients in Spry are unbiased estimators of true global gradients for homogeneous data distributions across clients, while heterogeneity increases bias of the estimates. We also derive Spry's convergence rate, showing that the gradients decrease inversely proportional to the number of FL rounds, indicating the convergence up to the limits of heterogeneity. Empirically, Spry reduces the memory footprint during training by 1.4-7.1$\times$ in contrast to backpropagation, while reaching comparable accuracy, across a wide range of language tasks, models, and FL settings.
Spry reduces the convergence time by 1.2-20.3$\times$ and achieves 5.2-13.5\% higher accuracy against state-of-the-art zero-order methods. When finetuning Llama2-7B with LoRA, compared to the peak memory consumption of 33.9GB of backpropagation, Spry only consumes 6.2GB of peak memory. For OPT13B, the reduction is from 76.5GB to 10.8GB. Spry makes feasible previously impossible FL deployments on commodity mobile and edge devices. Our source code is available for replication at https://github.com/Astuary/Spry. | Thinking Forward: Memory-Efficient Federated Finetuning of Language Models | [
"Kunjal Panchal",
"Nisarg Parikh",
"Sunav Choudhary",
"Lijun Zhang",
"Yuriy Brun",
"Hui Guan"
] | NeurIPS.cc/2024/Conference | 2405.15551 | [
"https://github.com/astuary/spry"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dG1HwKMYbC | @inproceedings{
yu2024fincon,
title={FinCon: A Synthesized {LLM} Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making},
author={Yangyang Yu and Zhiyuan Yao and Haohang Li and Zhiyang Deng and Yuechen Jiang and Yupeng Cao and Zhi Chen and Jordan W. Suchow and Zhenyu Cui and Rong Liu and Zhaozhuo Xu and Denghui Zhang and Koduvayur Subbalakshmi and GUOJUN XIONG and Yueru He and Jimin Huang and Dong Li and Qianqian Xie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dG1HwKMYbC}
} | Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks require multiple interactions with a volatile environment for every decision, demanding sufficient intelligence to maximize returns and manage risks. Although LLMs have been used to develop agent systems that surpass human teams and yield impressive investment returns, opportunities to enhance multi-source information synthesis and optimize decision-making outcomes through timely experience refinement remain unexplored. Here, we introduce FinCon, an LLM-based multi-agent framework tailored for diverse financial tasks. Inspired by effective real-world investment firm organizational structures, FinCon utilizes a manager-analyst communication hierarchy. This structure allows for synchronized cross-functional agent collaboration towards unified goals through natural language interactions and equips each agent with greater memory capacity than humans. Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs. The conceptualized beliefs serve as verbal reinforcement for the future agent’s behavior and can be selectively propagated to the appropriate node that requires knowledge updates. This feature significantly improves performance while reducing unnecessary peer-to-peer communication costs. Moreover, FinCon demonstrates strong generalization capabilities in various financial tasks, including stock trading and portfolio management. | FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making | [
"Yangyang Yu",
"Zhiyuan Yao",
"Haohang Li",
"Zhiyang Deng",
"Yuechen Jiang",
"Yupeng Cao",
"Zhi Chen",
"Jordan W. Suchow",
"Zhenyu Cui",
"Rong Liu",
"Zhaozhuo Xu",
"Denghui Zhang",
"Koduvayur Subbalakshmi",
"GUOJUN XIONG",
"Yueru He",
"Jimin Huang",
"Dong Li",
"Qianqian Xie"
] | NeurIPS.cc/2024/Conference | 2407.06567 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dE1bTyyC9A | @inproceedings{
xu2024a,
title={A Unified Framework for 3D Scene Understanding},
author={Wei Xu and Chunsheng Shi and Sifan Tu and Xin Zhou and Dingkang Liang and Xiang Bai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dE1bTyyC9A}
} | We propose UniSeg3D, a unified 3D segmentation framework that achieves panoptic, semantic, instance, interactive, referring, and open-vocabulary segmentation tasks within a single model. Most previous 3D segmentation approaches are typically tailored to a specific task, limiting their understanding of 3D scenes to a task-specific perspective. In contrast, the proposed method unifies six tasks into unified representations processed by the same Transformer. It facilitates inter-task knowledge sharing, thereby promoting comprehensive 3D scene understanding. To take advantage of multi-task unification, we enhance performance by leveraging inter-task connections. Specifically, we design knowledge distillation and contrastive learning methods to transfer task-specific knowledge across different tasks. Benefiting from extensive inter-task knowledge sharing, our UniSeg3D becomes more powerful. Experiments on three benchmarks, including ScanNet20, ScanRefer, and ScanNet200, demonstrate that the UniSeg3D consistently outperforms current SOTA methods, even those specialized for individual tasks. We hope UniSeg3D can serve as a solid unified baseline and inspire future work. Code and models are available at \url{https://dk-liang.github.io/UniSeg3D/}. | A Unified Framework for 3D Scene Understanding | [
"Wei Xu",
"Chunsheng Shi",
"Sifan Tu",
"Xin Zhou",
"Dingkang Liang",
"Xiang Bai"
] | NeurIPS.cc/2024/Conference | 2407.03263 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dDDc3iNZA7 | @inproceedings{
wu2024unidseg,
title={Uni{DS}eg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior},
author={Yao Wu and Mingwei Xing and Yachao Zhang and Xiaotong Luo and Yuan Xie and Yanyun Qu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dDDc3iNZA7}
} | 3D semantic segmentation using an adapting model trained from a source domain with or without accessing unlabeled target-domain data is the fundamental task in computer vision, containing domain adaptation and domain generalization.
The essence of simultaneously solving cross-domain tasks is to enhance the generalizability of the encoder.
In light of this, we propose a groundbreaking universal method with the help of off-the-shelf Visual Foundation Models (VFMs) to boost the adaptability and generalizability of cross-domain 3D semantic segmentation, dubbed $\textbf{UniDSeg}$.
Our method explores the VFMs prior and how to harness them, aiming to inherit the recognition ability of VFMs.
Specifically, this method introduces layer-wise learnable blocks to the VFMs, which hinges on alternately learning two representations during training: (i) Learning visual prompt. The 3D-to-2D transitional prior and task-shared knowledge is captured from the prompt space, and then (ii) Learning deep query. Spatial Tunability is constructed to the representation of distinct instances driven by prompts in the query space.
Integrating these representations into a cross-modal learning framework, UniDSeg efficiently mitigates the domain gap between 2D and 3D modalities, achieving unified cross-domain 3D semantic segmentation.
Extensive experiments demonstrate the effectiveness of our method across widely recognized tasks and datasets, all achieving superior performance over state-of-the-art methods. Remarkably, UniDSeg achieves 57.5\%/54.4\% mIoU on ``A2D2/sKITTI'' for domain adaptive/generalized tasks. Code is available at https://github.com/Barcaaaa/UniDSeg. | UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior | [
"Yao Wu",
"Mingwei Xing",
"Yachao Zhang",
"Xiaotong Luo",
"Yuan Xie",
"Yanyun Qu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dCgbyvmlwL | @inproceedings{
zheng2024udc,
title={{UDC}: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems},
author={Zhi Zheng and Changliang Zhou and Tong Xialiang and Mingxuan Yuan and Zhenkun Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dCgbyvmlwL}
} | Single-stage neural combinatorial optimization solvers have achieved near-optimal results on various small-scale combinatorial optimization (CO) problems without requiring expert knowledge. However, these solvers exhibit significant performance degradation when applied to large-scale CO problems. Recently, two-stage neural methods motivated by divide-and-conquer strategies have shown efficiency in addressing large-scale CO problems. Nevertheless, the performance of these methods highly relies on problem-specific heuristics in either the dividing or the conquering procedure, which limits their applicability to general CO problems. Moreover, these methods employ separate training schemes and ignore the interdependencies between the dividing and conquering strategies, often leading to sub-optimal solutions. To tackle these drawbacks, this article develops a unified neural divide-and-conquer framework (i.e., UDC) for solving general large-scale CO problems. UDC offers a Divide-Conquer-Reunion (DCR) training method to eliminate the negative impact of a sub-optimal dividing policy. Employing a high-efficiency Graph Neural Network (GNN) for global instance dividing and a fixed-length sub-path solver for conquering divided sub-problems, the proposed UDC framework demonstrates extensive applicability, achieving superior performance in 10 representative large-scale CO problems. The code is available at https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/UDC-Large-scale-CO-master | UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems | [
"Zhi Zheng",
"Changliang Zhou",
"Tong Xialiang",
"Mingxuan Yuan",
"Zhenkun Wang"
] | NeurIPS.cc/2024/Conference | 2407.00312 | [
"https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/UDC-Large-scale-CO-master"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dBynjEbAt0 | @inproceedings{
wang2024probabilistic,
title={Probabilistic size-and-shape functional mixed models},
author={Fangyi Wang and Karthik Bharath and Oksana Chkrebtii and Sebastian Kurtek},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dBynjEbAt0}
} | The reliable recovery and uncertainty quantification of a fixed effect function $\mu$ in a functional mixed model, for modeling population- and object-level variability in noisily observed functional data, is a notoriously challenging task: variations along the $x$ and $y$ axes are confounded with additive measurement error, and cannot in general be disentangled. The question then as to what properties of $\mu$ may be reliably recovered becomes important. We demonstrate that it is possible to recover the size-and-shape of a square-integrable $\mu$ under a Bayesian functional mixed model. The size-and-shape of $\mu$ is a geometric property invariant to a family of space-time unitary transformations, viewed as rotations of the Hilbert space, that jointly transform the $x$ and $y$ axes. A random object-level unitary transformation then captures size-and-shape preserving deviations of $\mu$ from an individual function, while a random linear term and measurement error capture size-and-shape altering deviations. The model is regularized by appropriate priors on the unitary transformations, posterior summaries of which may then be suitably interpreted as optimal data-driven rotations of a fixed orthonormal basis for the Hilbert space. Our numerical experiments demonstrate utility of the proposed model, and superiority over the current state-of-the-art. | Probabilistic size-and-shape functional mixed models | [
"Fangyi Wang",
"Karthik Bharath",
"Oksana Chkrebtii",
"Sebastian Kurtek"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dBE8KHdMFs | @inproceedings{
mei2024controlsynth,
title={ControlSynth Neural {ODE}s: Modeling Dynamical Systems with Guaranteed Convergence},
author={Wenjie Mei and Dongzhe Zheng and Shihua Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dBE8KHdMFs}
} | Neural ODEs (NODEs) are continuous-time neural networks (NNs) that can process data without the limitation of time intervals. They have advantages in learning and understanding the evolution of complex real dynamics. Many previous works have focused on NODEs in concise forms, while numerous physical systems taking straightforward forms in fact belong to their more complex quasi-classes, thus appealing to a class of general NODEs with high scalability and flexibility to model those systems. This however may result in intricate nonlinear properties. In this paper, we introduce ControlSynth Neural ODEs (CSODEs). We show that despite their highly nonlinear nature, convergence can be guaranteed via tractable linear inequalities. In the composition of CSODEs, we introduce an extra control term for learning the potential simultaneous capture of dynamics at different scales, which could be particularly useful for partial differential equation-formulated systems. Finally, we compare several representative NNs with CSODEs on important physical dynamics under the inductive biases of CSODEs, and illustrate that CSODEs have better learning and predictive abilities in these settings. | ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence | [
"Wenjie Mei",
"Dongzhe Zheng",
"Shihua Li"
] | NeurIPS.cc/2024/Conference | 2411.02292 | [
"https://github.com/continuumcoder/controlsynth-neural-ode"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dB99jjwx3h | @inproceedings{
jin2024learning,
title={Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity},
author={Jikai Jin and Vasilis Syrgkanis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dB99jjwx3h}
} | We study causal representation learning, the task of recovering high-level latent variables and their causal relationships in the form of a causal graph from low-level observed data (such as text and images), assuming access to observations generated from multiple environments. Prior results on the identifiability of causal representations typically assume access to single-node interventions which is rather unrealistic in practice, since the latent variables are unknown in the first place. In this work, we consider the task of learning causal representation learning with data collected from general environments. We show that even when the causal model and the mixing function are both linear, there exists a surrounded-node ambiguity (SNA) [Varici et al. 2023] which is basically unavoidable in our setting. On the other hand, in the same linear case, we show that identification up to SNA is possible under mild conditions, and propose an algorithm, LiNGCReL which provably achieves such identifiability guarantee. We conduct extensive experiments on synthetic data and demonstrate the effectiveness of LiNGCReL in the finite-sample regime. | Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity | [
"Jikai Jin",
"Vasilis Syrgkanis"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=dB6gwSDXKL | @inproceedings{
ren2024towards,
title={Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens},
author={Ruifeng Ren and Yong Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dB6gwSDXKL}
} | Pre-trained large language models based on Transformers have demonstrated remarkable in-context learning (ICL) abilities. With just a few demonstration examples, the models can implement new tasks without any parameter updates. However, it is still an open question to understand the mechanism of ICL. In this paper, we attempt to explore the ICL process in Transformers through a lens of representation learning. Initially, leveraging kernel methods, we figure out a dual model for one softmax attention layer. The ICL inference process of the attention layer aligns with the training procedure of its dual model, generating token representation predictions that are equivalent to the dual model's test outputs. We delve into the training process of this dual model from a representation learning standpoint and further derive a generalization error bound related to the quantity of demonstration tokens. Subsequently, we extend our theoretical conclusions to more complicated scenarios, including one Transformer layer and multiple attention layers. Furthermore, drawing inspiration from existing representation learning methods especially contrastive learning, we propose potential modifications for the attention layer. Finally, experiments are designed to support our findings. | Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens | [
"Ruifeng Ren",
"Yong Liu"
] | NeurIPS.cc/2024/Conference | 2310.13220 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dAXuir2ets | @inproceedings{
kim2024spafl,
title={Spa{FL}: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead},
author={Minsu Kim and Walid Saad and Merouane Abdelkader DEBBAH and Choong Seon Hong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dAXuir2ets}
} | The large communication and computation overhead of federated learning (FL) is one of the main challenges facing its practical deployment over resource-constrained clients and systems. In this work, SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. In SpaFL, a trainable threshold is defined for each filter/neuron to prune its all connected
parameters, thereby leading to structured sparsity. To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters, thereby learning how to prune. Further, global thresholds are used to update model parameters by extracting aggregated parameter importance. The generalization bound of SpaFL is also derived, thereby proving key insights on the relation between sparsity and performance. Experimental results show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines. The code is available at https://github.com/news-vt/SpaFL_NeruIPS_2024 | SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead | [
"Minsu Kim",
"Walid Saad",
"Merouane Abdelkader DEBBAH",
"Choong Seon Hong"
] | NeurIPS.cc/2024/Conference | 2406.00431 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=dA7hUm4css | @inproceedings{
huang2024oneshot,
title={One-Shot Safety Alignment for Large Language Models via Optimal Dualization},
author={Xinmeng Huang and Shuo Li and Edgar Dobriban and Osbert Bastani and Hamed Hassani and Dongsheng Ding},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dA7hUm4css}
} | The growing safety concerns surrounding large language models raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safety. A promising approach is to enforce safety constraints through Reinforcement Learning from Human Feedback (RLHF). For such constrained RLHF, typical Lagrangian-based primal-dual policy optimization methods are computationally expensive and often unstable. This paper presents a perspective of dualization that reduces constrained alignment to an equivalent unconstrained alignment problem. We do so by pre-optimizing a smooth and convex dual function that has a closed form. This shortcut eliminates the need for cumbersome primal-dual policy iterations, greatly reducing the computational burden and improving training stability. Our strategy leads to two practical algorithms in model-based and preference-based settings (MoCAN and PeCAN, respectively). A broad range of experiments demonstrate the effectiveness and merits of our algorithms. | One-Shot Safety Alignment for Large Language Models via Optimal Dualization | [
"Xinmeng Huang",
"Shuo Li",
"Edgar Dobriban",
"Osbert Bastani",
"Hamed Hassani",
"Dongsheng Ding"
] | NeurIPS.cc/2024/Conference | 2405.19544 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=d99yCfOnwK | @inproceedings{
yadin2024classification,
title={Classification Diffusion Models: Revitalizing Density Ratio Estimation},
author={Shahar Yadin and Noam Elata and Tomer Michaeli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=d99yCfOnwK}
} | A prominent family of methods for learning data distributions relies on density ratio estimation (DRE), where a model is trained to *classify* between data samples and samples from some reference distribution. DRE-based models can directly output the likelihood for any given input, a highly desired property that is lacking in most generative techniques. Nevertheless, to date, DRE methods have failed in accurately capturing the distributions of complex high-dimensional data, like images, and have thus been drawing reduced research attention in recent years.
In this work we present *classification diffusion models* (CDMs), a DRE-based generative method that adopts the formalism of denoising diffusion models (DDMs) while making use of a classifier that predicts the level of noise added to a clean signal. Our method is based on an analytical connection that we derive between the MSE-optimal denoiser for removing white Gaussian noise and the cross-entropy-optimal classifier for predicting the noise level. Our method is the first DRE-based technique that can successfully generate images beyond the MNIST dataset. Furthermore, it can output the likelihood of any input in a single forward pass, achieving state-of-the-art negative log likelihood (NLL) among methods with this property. | Classification Diffusion Models: Revitalizing Density Ratio Estimation | [
"Shahar Yadin",
"Noam Elata",
"Tomer Michaeli"
] | NeurIPS.cc/2024/Conference | 2402.10095 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=d75qCZb7TX | @inproceedings{
rauba2024contextaware,
title={Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models},
author={Paulius Rauba and Nabeel Seedat and Max Ruiz Luyten and Mihaela van der Schaar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=d75qCZb7TX}
} | The predominant *de facto* paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups. However, such *data-only testing* methods operate under the restrictive assumption that the available empirical data is the sole input for testing ML models, disregarding valuable contextual information that could guide model testing. In this paper, we challenge the go-to approach of *data-only testing* and introduce *Context-Aware Testing* (CAT) which uses context as an inductive bias to guide the search for meaningful model failures. We instantiate the first CAT system, *SMART Testing*, which employs large language models to hypothesize relevant and likely failures, which are evaluated on data using a *self-falsification mechanism*. Through empirical evaluations in diverse settings, we show that SMART automatically identifies more relevant and impactful failures than alternatives, demonstrating the potential of CAT as a testing paradigm. | Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models | [
"Paulius Rauba",
"Nabeel Seedat",
"Max Ruiz Luyten",
"Mihaela van der Schaar"
] | NeurIPS.cc/2024/Conference | 2410.24005 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=d5cKDHCrFJ | @inproceedings{
kim2024epic,
title={{EPIC}: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models},
author={Jinhee Kim and Taesung Kim and Jaegul Choo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=d5cKDHCrFJ}
} | Large language models (LLMs) have demonstrated remarkable in-context learning capabilities across diverse applications. In this work, we explore the effectiveness of LLMs for generating realistic synthetic tabular data, identifying key prompt design elements to optimize performance. We introduce EPIC, a novel approach that leverages balanced, grouped data samples and consistent formatting with unique variable mapping to guide LLMs in generating accurate synthetic data across all classes, even for imbalanced datasets. Evaluations on real-world datasets show that EPIC achieves state-of-the-art machine learning classification performance, significantly improving generation efficiency. These findings highlight the effectiveness of EPIC for synthetic tabular data generation, particularly in addressing class imbalance. | EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models | [
"Jinhee Kim",
"Taesung Kim",
"Jaegul Choo"
] | NeurIPS.cc/2024/Conference | 2404.12404 | [
"https://github.com/seharanul17/synthetic-tabular-LLM"
] | https://huggingface.co/papers/2404.12404 | 0 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=d2lPM1Aczs | @inproceedings{
huang2024rankup,
title={RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier},
author={Pin-Yen Huang and Szu-Wei Fu and Yu Tsao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=d2lPM1Aczs}
} | State-of-the-art (SOTA) semi-supervised learning techniques, such as FixMatch and it's variants, have demonstrated impressive performance in classification tasks. However, these methods are not directly applicable to regression tasks. In this paper, we present RankUp, a simple yet effective approach that adapts existing semi-supervised classification techniques to enhance the performance of regression tasks. RankUp achieves this by converting the original regression task into a ranking problem and training it concurrently with the original regression objective. This auxiliary ranking classifier outputs a classification result, thus enabling integration with existing semi-supervised classification methods. Moreover, we introduce regression distribution alignment (RDA), a complementary technique that further enhances RankUp's performance by refining pseudo-labels through distribution alignment. Despite its simplicity, RankUp, with or without RDA, achieves SOTA results in across a range of regression benchmarks, including computer vision, audio, and natural language processing tasks. Our code and log data are open-sourced at [https://github.com/pm25/semi-supervised-regression](https://github.com/pm25/semi-supervised-regression). | RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier | [
"Pin-Yen Huang",
"Szu-Wei Fu",
"Yu Tsao"
] | NeurIPS.cc/2024/Conference | 2410.22124 | [
"https://github.com/pm25/semi-supervised-regression"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=d226uyWYUo | @inproceedings{
xiao2024knowledge,
title={Knowledge Graph Completion by Intermediate Variables Regularization},
author={Changyi Xiao and Yixin Cao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=d226uyWYUo}
} | Knowledge graph completion (KGC) can be framed as a 3-order binary tensor completion task. Tensor decomposition-based (TDB) models have demonstrated strong performance in KGC. In this paper, we provide a summary of existing TDB models and derive a general form for them, serving as a foundation for further exploration of TDB models. Despite the expressiveness of TDB models, they are prone to overfitting. Existing regularization methods merely minimize the norms of embeddings to regularize the model, leading to suboptimal performance. Therefore, we propose a novel regularization method for TDB models that addresses this limitation. The regularization is applicable to most TDB models and ensures tractable computation. Our method minimizes the norms of intermediate variables involved in the different ways of computing the predicted tensor. To support our regularization method, we provide a theoretical analysis that proves its effect in promoting low trace norm of the predicted tensor to reduce overfitting. Finally, we conduct experiments to verify the effectiveness of our regularization technique as well as the reliability of our theoretical analysis. The code is available at https://github.com/changyi7231/IVR. | Knowledge Graph Completion by Intermediate Variables Regularization | [
"Changyi Xiao",
"Yixin Cao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=d1XrZ4EINV | @inproceedings{
jiang2024ledex,
title={LeDex: Training {LLM}s to Better Self-Debug and Explain Code},
author={Nan Jiang and Xiaopeng Li and Shiqi Wang and Qiang Zhou and Soneya Binta Hossain and Baishakhi Ray and Varun Kumar and Xiaofei Ma and Anoop Deoras},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=d1XrZ4EINV}
} | In the domain of code generation, self-debugging is crucial. It allows LLMs to refine their generated code based on execution feedback. This is particularly important because generating correct solutions in one attempt proves challenging for complex tasks. Prior works on self-debugging mostly focus on prompting methods by providing LLMs with few-shot examples, which work poorly on small open-sourced LLMs. In this work, we propose LeDex, a training framework that significantly improves the self-debugging capability of LLMs. Intuitively, we observe that a chain of explanations on the wrong code followed by code refinement helps LLMs better analyze the wrong code and do refinement. We thus propose an automated pipeline to collect a high-quality dataset for code explanation and refinement by generating a number of explanations and refinement trajectories from the LLM itself or a larger teacher model and filtering via execution verification. We perform supervised fine-tuning (SFT) and further reinforcement learning (RL) on both success and failure trajectories with a novel reward design considering code explanation and refinement quality. SFT improves the pass@1 by up to 15.92\% and pass@10 by 9.30\% over four benchmarks. RL training brings additional up to 3.54\% improvement on pass@1 and 2.55\% improvement on pass@10. The trained LLMs show iterative refinement ability and can keep refining code continuously. Lastly, our human evaluation shows that the LLMs trained with our framework generate more useful code explanations and help developers better understand bugs in source code. | LeDex: Training LLMs to Better Self-Debug and Explain Code | [
"Nan Jiang",
"Xiaopeng Li",
"Shiqi Wang",
"Qiang Zhou",
"Soneya Binta Hossain",
"Baishakhi Ray",
"Varun Kumar",
"Xiaofei Ma",
"Anoop Deoras"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cyv0LkIaoH | @inproceedings{
ferbach2024selfconsuming,
title={Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences},
author={Damien Ferbach and Quentin Bertrand and Joey Bose and Gauthier Gidel},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cyv0LkIaoH}
} | The rapid progress in generative models has resulted in impressive leaps in generation quality, blurring the lines between synthetic and real data. Web-scale datasets are now prone to the inevitable contamination by synthetic data, directly impacting the training of future generated models.
Already, some theoretical results on self-consuming generative models (a.k.a., iterative retraining) have emerged in the literature, showcasing that either model collapse or stability could be possible depending on the fraction of generated data used at each retraining step.
However, in practice, synthetic data is often subject to human feedback and curated by users before being used and uploaded online. For instance, many interfaces of popular text-to-image generative models, such as Stable Diffusion or Midjourney, produce several variations of an image for a given query which can eventually be curated by the users.
In this paper, we theoretically study the impact of data curation on iterated retraining of generative models and show that it can be seen as an implicit preference optimization mechanism. However, unlike standard preference optimization, the generative model does not have access to the reward function or negative samples needed for pairwise comparisons. Moreover, our study doesn't require access to the density function, only to samples. We prove that, if the data is curated according to a reward model, then the expected reward of the iterative retraining procedure is maximized. We further provide theoretical results on the stability of the retraining loop when using a positive fraction of real data at each step. Finally, we conduct illustrative experiments on both synthetic datasets and on CIFAR10 showing that such a procedure amplifies biases of the reward model. | Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences | [
"Damien Ferbach",
"Quentin Bertrand",
"Joey Bose",
"Gauthier Gidel"
] | NeurIPS.cc/2024/Conference | 2407.09499 | [
""
] | https://huggingface.co/papers/2407.09499 | 0 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=cyJxphdw3B | @inproceedings{
furuya2024can,
title={Can neural operators always be continuously discretized?},
author={Takashi Furuya and Michael Anthony Puthawala and Matti Lassas and Maarten V. de Hoop},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cyJxphdw3B}
} | In this work we consider the problem of discretization of neural operators in a general setting. Using category theory, we give a no-go theorem that shows that diffeomorphisms between Hilbert spaces may not admit any continuous approximations by diffeomorphisms on finite spaces, even if the discretization is non-linear. This shows how infinite-dimensional Hilbert spaces and finite-dimensional vector spaces fundamentally differ. A key take-away is that to obtain discretization invariance, considerable effort is needed to ensure that finite-dimensional approximations of neural operator converge not only as sequences of functions, but that their representations converge in a suitable sense as well. With this perspective, we give several positive results. We first show that strongly monotone diffeomorphism operators always admit finite-dimensional strongly monotone diffeomorphisms. Next we show that bilipschitz neural operators may always be written via the repeated alternating composition of strongly monotone neural operators and invertible linear maps. We also show that such operators may be inverted locally via iteration provided that such inverse exists. Finally, we conclude by showing how our framework may be used `out of the box' to prove quantitative approximation results for discretization of neural operators. | Can neural operators always be continuously discretized? | [
"Takashi Furuya",
"Michael Anthony Puthawala",
"Matti Lassas",
"Maarten V. de Hoop"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cw5mgd71jW | @inproceedings{
anil2024manyshot,
title={Many-shot Jailbreaking},
author={Cem Anil and Esin DURMUS and Nina Rimsky and Mrinank Sharma and Joe Benton and Sandipan Kundu and Joshua Batson and Meg Tong and Jesse Mu and Daniel J Ford and Francesco Mosconi and Rajashree Agrawal and Rylan Schaeffer and Naomi Bashkansky and Samuel Svenningsen and Mike Lambert and Ansh Radhakrishnan and Carson Denison and Evan J Hubinger and Yuntao Bai and Trenton Bricken and Timothy Maxwell and Nicholas Schiefer and James Sully and Alex Tamkin and Tamera Lanham and Karina Nguyen and Tomasz Korbak and Jared Kaplan and Deep Ganguli and Samuel R. Bowman and Ethan Perez and Roger Baker Grosse and David Duvenaud},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cw5mgd71jW}
} | We investigate a family of simple long-context attacks on large language models: prompting with hundreds of demonstrations of undesirable behavior. This attack is newly feasible with the larger context windows recently deployed by language model providers like Google DeepMind, OpenAI and Anthropic. We find that in diverse, realistic circumstances, the effectiveness of this attack follows a power law, up to hundreds of shots. We demonstrate the success of this attack on the most widely used state-of-the-art closed-weight models, and across various tasks. Our results suggest very long contexts present a rich new attack surface for LLMs. | Many-shot Jailbreaking | [
"Cem Anil",
"Esin DURMUS",
"Nina Rimsky",
"Mrinank Sharma",
"Joe Benton",
"Sandipan Kundu",
"Joshua Batson",
"Meg Tong",
"Jesse Mu",
"Daniel J Ford",
"Francesco Mosconi",
"Rajashree Agrawal",
"Rylan Schaeffer",
"Naomi Bashkansky",
"Samuel Svenningsen",
"Mike Lambert",
"Ansh Radhakrishnan",
"Carson Denison",
"Evan J Hubinger",
"Yuntao Bai",
"Trenton Bricken",
"Timothy Maxwell",
"Nicholas Schiefer",
"James Sully",
"Alex Tamkin",
"Tamera Lanham",
"Karina Nguyen",
"Tomasz Korbak",
"Jared Kaplan",
"Deep Ganguli",
"Samuel R. Bowman",
"Ethan Perez",
"Roger Baker Grosse",
"David Duvenaud"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cvaSru8LeO | @inproceedings{
wang2024is,
title={Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models},
author={Jiayu Wang and Yifei Ming and Zhenmei Shi and Vibhav Vineet and Xin Wang and Yixuan Li and Neel Joshi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cvaSru8LeO}
} | Large language models (LLMs) and vision-language models (VLMs) have demonstrated remarkable performance across a wide range of tasks and domains. Despite this promise, spatial understanding and reasoning—a fundamental component of human cognition—remains under-explored. We propose SpatialEval, a novel benchmark that covers diverse aspects of spatial reasoning such as relationship understanding, navigation, and counting. We conduct a comprehensive evaluation of competitive language and vision-language models. Our findings reveal several counter-intuitive insights that have been overlooked in the literature: (1) Spatial reasoning poses significant challenges where competitive models can fall behind random guessing; (2) Despite additional visual input, VLMs often under-perform compared to their LLM counterparts; (3) When both textual and visual information is available, multi-modal language models become less reliant on visual information if sufficient textual clues are provided. Additionally, we demonstrate that leveraging redundancy between vision and text can significantly enhance model performance. We hope our study will inform the development of multimodal models to improve spatial intelligence and further close the gap with human intelligence. Our code is available at https://github.com/jiayuww/SpatialEval. | Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models | [
"Jiayu Wang",
"Yifei Ming",
"Zhenmei Shi",
"Vibhav Vineet",
"Xin Wang",
"Yixuan Li",
"Neel Joshi"
] | NeurIPS.cc/2024/Conference | 2406.14852 | [
"https://github.com/jiayuww/SpatialEval"
] | https://huggingface.co/papers/2406.14852 | 3 | 0 | 0 | 6 | [] | [
"microsoft/VISION_LANGUAGE"
] | [] | [] | [
"microsoft/VISION_LANGUAGE"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=cuWsR25bbI | @inproceedings{
nam2024an,
title={An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem},
author={Yoonsoo Nam and Nayara Fonseca and Seok Hyeong Lee and Chris Mingard and Ard A. Louis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cuWsR25bbI}
} | Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time, training data, or model size increases, a phenomenon known as emergence. In this paper, we present a framework where each new ability (a skill) is represented as a basis function. We solve a simple multi-linear model in this skill-basis, finding analytic expressions for the emergence of new skills, as well as for scaling laws of the loss with training time, data size, model size, and optimal compute. We compare our detailed calculations to direct simulations of a two-layer neural network trained on multitask sparse parity, where the tasks in the dataset are distributed according to a power-law. Our simple model captures, using a single fit parameter, the sigmoidal emergence of multiple new skills as training time, data size or model size increases in the neural network. | An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem | [
"Yoonsoo Nam",
"Nayara Fonseca",
"Seok Hyeong Lee",
"Chris Mingard",
"Ard A. Louis"
] | NeurIPS.cc/2024/Conference | 2404.17563 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cuO0DenqMl | @inproceedings{
matsubara2024wasserstein,
title={Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning},
author={Takuo Matsubara},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cuO0DenqMl}
} | Gradient boosting is a sequential ensemble method that fits a new weaker learner to pseudo residuals at each iteration. We propose Wasserstein gradient boosting, a novel extension of gradient boosting, which fits a new weak learner to alternative pseudo residuals that are Wasserstein gradients of loss functionals of probability distributions assigned at each input. It solves distribution-valued supervised learning, where the output values of the training dataset are probability distributions. In classification and regression, a model typically returns, for each input, a point estimate of a parameter of a noise distribution specified for a response variable, such as the class probability parameter of a categorical distribution specified for a response label. A main application of Wasserstein gradient boosting in this paper is tree-based evidential learning, which returns a distributional estimate of the response parameter for each input. We empirically demonstrate the competitive performance of the probabilistic prediction by Wasserstein gradient boosting in comparison with existing uncertainty quantification methods. | Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning | [
"Takuo Matsubara"
] | NeurIPS.cc/2024/Conference | 2405.09536 | [
"https://github.com/takuomatsubara/WGBoost"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ctxtY3VGGq | @inproceedings{
levy2024online,
title={Online Weighted Paging with Unknown Weights},
author={Orin Levy and Noam Touitou and Aviv Rosenberg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ctxtY3VGGq}
} | Online paging is a fundamental problem in the field of online algorithms, in which one maintains a cache of $k$ slots as requests for fetching pages arrive online.
In the weighted variant of this problem, each page has its own fetching cost; a substantial line of work on this problem culminated in an (optimal) $O(\log k)$-competitive randomized algorithm, due to Bansal, Buchbinder and Naor (FOCS'07).
Existing work for weighted paging assumes that page weights are known in advance, which is not always the case in practice.
For example, in multi-level caching architectures, the expected cost of fetching a memory block is a function of its probability of being in a mid-level cache rather than the main memory.
This complex property cannot be predicted in advance; over time, however, one may glean information about page weights through sampling their fetching cost multiple times.
We present the first algorithm for online weighted paging that does not know page weights in advance, but rather learns from weight samples.
In terms of techniques, this requires providing (integral) samples to a fractional solver, requiring a delicate interface between this solver and the randomized rounding scheme; we believe that our work can inspire online algorithms to other problems that involve cost sampling. | Online Weighted Paging with Unknown Weights | [
"Orin Levy",
"Noam Touitou",
"Aviv Rosenberg"
] | NeurIPS.cc/2024/Conference | 2410.21266 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ctXYOoAgRy | @inproceedings{
zhao2024how,
title={How do Large Language Models Handle Multilingualism?},
author={Yiran Zhao and Wenxuan Zhang and Guizhen Chen and Kenji Kawaguchi and Lidong Bing},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ctXYOoAgRy}
} | Large language models (LLMs) have demonstrated impressive capabilities across diverse languages. This study explores how LLMs handle multilingualism. Based on observed language ratio shifts among layers and the relationships between network structures and certain capabilities, we hypothesize the LLM's multilingual workflow ($\texttt{MWork}$): LLMs initially understand the query, converting multilingual inputs into English for task-solving. In the intermediate layers, they employ English for thinking and incorporate multilingual knowledge with self-attention and feed-forward structures, respectively. In the final layers, LLMs generate responses aligned with the original language of the query.
To verify $\texttt{MWork}$, we introduce Parallel Language-specific Neuron Detection ($\texttt{PLND}$) to identify activated neurons for inputs in different languages without any labeled data. Using $\texttt{PLND}$, we validate $\texttt{MWork}$ through extensive experiments involving the deactivation of language-specific neurons across various layers and structures.
Moreover, $\texttt{MWork}$ allows fine-tuning of language-specific neurons with a small dataset, enhancing multilingual abilities in a specific language without compromising others. This approach results in an average improvement of $3.6\%$ for high-resource languages and $2.3\%$ for low-resource languages across all tasks with just $400$ documents. | How do Large Language Models Handle Multilingualism? | [
"Yiran Zhao",
"Wenxuan Zhang",
"Guizhen Chen",
"Kenji Kawaguchi",
"Lidong Bing"
] | NeurIPS.cc/2024/Conference | 2402.18815 | [
"https://github.com/damo-nlp-sg/multilingual_analysis"
] | https://huggingface.co/papers/2402.18815 | 3 | 0 | 0 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=cs1HISJkLU | @inproceedings{
kim2024a,
title={A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation},
author={Gwanghyun Kim and Alonso Martinez and Yu-Chuan Su and Brendan Jou and Jose Lezama and Agrim Gupta and Lijun Yu and Lu Jiang and Aren Jansen and Jacob C Walker and Krishna Somandepalli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cs1HISJkLU}
} | Training diffusion models for audiovisual sequences allows for a range of generation tasks by learning conditional distributions of various input-output combinations of the two modalities. Nevertheless, this strategy often requires training a separate model for each task which is expensive. Here, we propose a novel training approach to effectively learn arbitrary conditional distributions in the audiovisual space. Our key contribution lies in how we parameterize the diffusion timestep in the forward diffusion process. Instead of the standard fixed diffusion timestep, we propose applying variable diffusion timesteps across the temporal dimension and across modalities of the inputs. This formulation offers flexibility to introduce variable noise levels for various portions of the input, hence the term mixture of noise levels. We propose a transformer-based audiovisual latent diffusion model and show that it can be trained in a task-agnostic fashion using our approach to enable a variety of audiovisual generation tasks at inference time. Experiments demonstrate the versatility of our method in tackling cross-modal and multimodal interpolation tasks in the audiovisual space. Notably, our proposed approach surpasses baselines in generating temporally and perceptually consistent samples conditioned on the input. Project page: neurips13025.github.io | A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation | [
"Gwanghyun Kim",
"Alonso Martinez",
"Yu-Chuan Su",
"Brendan Jou",
"Jose Lezama",
"Agrim Gupta",
"Lijun Yu",
"Lu Jiang",
"Aren Jansen",
"Jacob C Walker",
"Krishna Somandepalli"
] | NeurIPS.cc/2024/Conference | 2405.13762 | [
""
] | https://huggingface.co/papers/2405.13762 | 0 | 0 | 0 | 11 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=crlvDzDPgM | @inproceedings{
du2024customized,
title={Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction},
author={Haotong Du and Quanming Yao and Juzheng Zhang and Yang Liu and Zhen Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=crlvDzDPgM}
} | Subgraph-based methods have proven to be effective and interpretable in predicting drug-drug interactions (DDIs),
which are essential for medical practice and drug development.
Subgraph selection and encoding are critical stages in these methods,
yet customizing these components remains underexplored due to the high cost of manual adjustments.
In this study,
inspired by the success of neural architecture search (NAS),
we propose a method to search for data-specific components within subgraph-based frameworks.
Specifically,
we introduce extensive subgraph selection and encoding spaces that account for the diverse contexts of drug interactions in DDI prediction.
To address the challenge of large search spaces and high sampling costs,
we design a relaxation mechanism that uses an approximation strategy to efficiently explore optimal subgraph configurations. This approach allows for robust exploration of the search space.
Extensive experiments demonstrate the effectiveness and superiority of the proposed method,
with the discovered subgraphs and encoding functions highlighting the model’s adaptability. | Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction | [
"Haotong Du",
"Quanming Yao",
"Juzheng Zhang",
"Yang Liu",
"Zhen Wang"
] | NeurIPS.cc/2024/Conference | 2411.01535 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cr5EQRJlRn | @inproceedings{
ping2024deltacome,
title={Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models},
author={Bowen Ping and Shuo Wang and Hanqing Wang and Xu Han and Yuzhuang Xu and Yukun Yan and Yun Chen and Baobao Chang and Zhiyuan Liu and Maosong Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cr5EQRJlRn}
} | Fine-tuning is a crucial process for adapting large language models (LLMs) to diverse applications. In certain scenarios, such as multi-tenant serving, deploying multiple LLMs becomes necessary to meet complex demands. Recent studies suggest decomposing a fine-tuned LLM into a base model and corresponding delta weights, which are then compressed using low-rank or low-bit approaches to reduce costs. In this work, we observe that existing low-rank and low-bit compression methods can significantly harm the model performance for task-specific fine-tuned LLMs (e.g., WizardMath for math problems). Motivated by the long-tail distribution of singular values in the delta weights, we propose a delta quantization approach using mixed-precision. This method employs higher-bit representation for singular vectors corresponding to larger singular values. We evaluate our approach on various fine-tuned LLMs, including math LLMs, code LLMs, chat LLMs, and even VLMs. Experimental results demonstrate that our approach performs comparably to full fine-tuned LLMs, surpassing both low-rank and low-bit baselines by a considerable margin. Additionally, we show that our method is compatible with various backbone LLMs, such as Llama-2, Llama-3, and Mistral, highlighting its generalizability. | Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models | [
"Bowen Ping",
"Shuo Wang",
"Hanqing Wang",
"Xu Han",
"Yuzhuang Xu",
"Yukun Yan",
"Yun Chen",
"Baobao Chang",
"Zhiyuan Liu",
"Maosong Sun"
] | NeurIPS.cc/2024/Conference | 2406.08903 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cqfE9eYMdP | @inproceedings{
luo2024neural,
title={Neural Krylov Iteration for Accelerating Linear System Solving},
author={Jian Luo and Jie Wang and Hong Wang and huanshuo dong and Zijie Geng and Hanzhu Chen and Yufei Kuang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cqfE9eYMdP}
} | Solving large-scale sparse linear systems is essential in fields like mathematics, science, and engineering. Traditional numerical solvers, mainly based on the Krylov subspace iteration algorithm, suffer from the low-efficiency problem, which primarily arises from the less-than-ideal iteration. To tackle this problem, we propose a novel method, namely **Neur**al **K**rylov **It**era**t**ion (**NeurKItt**), for accelerating linear system solving.
Specifically, NeurKItt employs a neural operator to predict the invariant subspace of the linear system and then leverages the predicted subspace to accelerate linear system solving. To enhance the subspace prediction accuracy, we utilize QR decomposition for the neural operator outputs and introduce a novel projection loss function for training. NeurKItt benefits the solving by using the predicted subspace to guide the iteration process, significantly reducing the number of iterations.
We provide extensive experiments and comprehensive theoretical analyses to demonstrate the feasibility and efficiency of NeurKItt. In our main experiments, NeurKItt accelerates the solving of linear systems across various settings and datasets, achieving up to a 5.5× speedup in computation time and a 16.1× speedup in the number of iterations. | Neural Krylov Iteration for Accelerating Linear System Solving | [
"Jian Luo",
"Jie Wang",
"Hong Wang",
"huanshuo dong",
"Zijie Geng",
"Hanzhu Chen",
"Yufei Kuang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=cqRgoDFaGN | @inproceedings{
yao2024fasterdit,
title={FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification},
author={Jingfeng Yao and Cheng Wang and Wenyu Liu and Xinggang Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cqRgoDFaGN}
} | Diffusion Transformers (DiT) have attracted significant attention in research. However, they suffer from a slow convergence rate. In this paper, we aim to accelerate DiT training without any architectural modification. We identify the following issues in the training process: firstly, certain training strategies do not consistently perform well across different data. Secondly, the effectiveness of supervision at specific timesteps is limited. In response, we propose the following contributions: (1) We introduce a new perspective for interpreting the failure of the strategies. Specifically, we slightly extend the definition of Signal-to-Noise Ratio (SNR) and suggest observing the Probability Density Function (PDF) of SNR to understand the essence of the data robustness of the strategy. (2) We conduct numerous experiments and report over one hundred experimental results to empirically summarize a unified accelerating strategy from the perspective of PDF. (3) We develop a new supervision method that further accelerates the training process of DiT. Based on them, we propose FasterDiT, an exceedingly simple and practicable design strategy. With few lines of code modifications, it achieves 2.30 FID on ImageNet at 256x256 resolution with 1000 iterations, which is comparable to DiT (2.27 FID) but 7 times faster in training. | FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification | [
"Jingfeng Yao",
"Cheng Wang",
"Wenyu Liu",
"Xinggang Wang"
] | NeurIPS.cc/2024/Conference | 2410.10356 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cpklMJqZDE | @inproceedings{
karan2024unrolled,
title={Unrolled denoising networks provably learn to perform optimal Bayesian inference},
author={Aayush Karan and Kulin Shah and Sitan Chen and Yonina C. Eldar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cpklMJqZDE}
} | Much of Bayesian inference centers around the design of estimators for inverse problems which are optimal assuming the data comes from a known prior. But what do these optimality guarantees mean if the prior is unknown? In recent years, algorithm unrolling has emerged as deep learning's answer to this age-old question: design a neural network whose layers can in principle simulate iterations of inference algorithms and train on data generated by the unknown prior. Despite its empirical success, however, it has remained unclear whether this method can provably recover the performance of its optimal, prior-aware counterparts.
In this work, we prove the first rigorous learning guarantees for neural networks based on unrolling approximate message passing (AMP). For compressed sensing, we prove that when trained on data drawn from a product prior, the layers of the network approximately converge to the same denoisers used in Bayes AMP. We also provide extensive numerical experiments for compressed sensing and rank-one matrix estimation demonstrating the advantages of our unrolled architecture \--- in addition to being able to obliviously adapt to general priors, it exhibits improvements over Bayes AMP in more general settings of low dimensions, non-Gaussian designs, and non-product priors. | Unrolled denoising networks provably learn to perform optimal Bayesian inference | [
"Aayush Karan",
"Kulin Shah",
"Sitan Chen",
"Yonina C. Eldar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=co8KZws1YK | @inproceedings{
gazdieva2024light,
title={Light Unbalanced Optimal Transport},
author={Milena Gazdieva and Arip Asadulaev 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=co8KZws1YK}
} | While the continuous Entropic Optimal Transport (EOT) field has been actively developing in recent years, it became evident that the classic EOT problem is prone to different issues like the sensitivity to outliers and imbalance of classes in the source and target measures. This fact inspired the development of solvers that deal with the *unbalanced* EOT (UEOT) problem $-$ the generalization of EOT allowing for mitigating the mentioned issues by relaxing the marginal constraints. Surprisingly, it turns out that the existing solvers are either based on heuristic principles or heavy-weighted with complex optimization objectives involving several neural networks. We address this challenge and propose a novel theoretically-justified, lightweight, unbalanced EOT solver. Our advancement consists of developing a novel view on the optimization of the UEOT problem yielding tractable and a non-minimax optimization objective. We show that combined with a light parametrization recently proposed in the field our objective leads to a fast, simple, and effective solver which allows solving the continuous UEOT problem in minutes on CPU. We prove that our solver provides a universal approximation of UEOT solutions and obtain its generalization bounds. We give illustrative examples of the solver's performance. | Light Unbalanced Optimal Transport | [
"Milena Gazdieva",
"Arip Asadulaev",
"Evgeny Burnaev",
"Alexander Korotin"
] | NeurIPS.cc/2024/Conference | 2303.07988 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=co7DsOwcop | @inproceedings{
chen2024structured,
title={Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics},
author={Xiaodan Chen and Xiucheng Li and Xinyang Chen and Zhijun Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=co7DsOwcop}
} | Multivariate time series forecasting is of central importance in modern intelligent decision systems. The dynamics of multivariate time series are jointly characterized by temporal dependencies and spatial correlations. Hence, it is equally important to build the forecasting models from both perspectives. The real-world multivariate time series data often presents spatial correlations that show structures and evolve dynamically. To capture such dynamic spatial structures, the existing forecasting approaches often rely on a two-stage learning process (learning dynamic series representations and then generating spatial structures), which is sensitive to the small time-window input data and has high variance. To address this, we propose a novel forecasting model with a structured matrix basis. At its core is a dynamic spatial structure generation function whose output space is well-constrained and the generated structures have lower variance, meanwhile, it is more expressive and can offer interpretable dynamics. This is achieved via a novel structured parameterization and imposing structure regularization on the matrix basis. The resulting forecasting model can achieve up to $8.5\%$ improvements over the existing methods on six benchmark datasets, and meanwhile, it enables us to gain insights into the dynamics of underlying systems. | Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics | [
"Xiaodan Chen",
"Xiucheng Li",
"Xinyang Chen",
"Zhijun Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cnpR4e2HCQ | @inproceedings{
davison2024community,
title={Community Detection Guarantees using Embeddings Learned by Node2Vec},
author={Andrew Davison and Samuel Carlyle Morgan and Owen G. Ward},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cnpR4e2HCQ}
} | Embedding the nodes of a large network into an Euclidean space is a common objective in modern
machine learning, with a variety of tools available. These embeddings can then be used as features for
tasks such as community detection/node clustering or link prediction, where they achieve state of the art
performance. With the exception of spectral clustering methods, there is little theoretical understanding
for commonly used approaches to learning embeddings. In this work we examine the theoretical
properties of the embeddings learned by node2vec. Our main result shows that the use of k-means
clustering on the embedding vectors produced by node2vec gives weakly consistent community recovery
for the nodes in (degree corrected) stochastic block models. We also discuss the use of these embeddings
for node and link prediction tasks. We demonstrate this result empirically for both
real and simulated networks, and examine how this relates
to other embedding tools for network data. | Community Detection Guarantees using Embeddings Learned by Node2Vec | [
"Andrew Davison",
"Samuel Carlyle Morgan",
"Owen G. Ward"
] | NeurIPS.cc/2024/Conference | 2310.17712 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cmSNX47aEH | @inproceedings{
shindo2024deisam,
title={Dei{SAM}: Segment Anything with Deictic Prompting},
author={Hikaru Shindo and Manuel Brack and Gopika Sudhakaran and Devendra Singh Dhami and Patrick Schramowski and Kristian Kersting},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cmSNX47aEH}
} | Large-scale, pre-trained neural networks have demonstrated strong capabilities in various tasks, including zero-shot image segmentation. To identify concrete objects in complex scenes, humans instinctively rely on deictic descriptions in natural language, i.e., referring to something depending on the context such as "The object that is on the desk and behind the cup.". However, deep learning approaches cannot reliably interpret such deictic representations due to their lack of reasoning capabilities in complex scenarios. To remedy this issue, we propose DeiSAM — a combination of large pre-trained neural networks with differentiable logic reasoners — for deictic promptable segmentation. Given a complex, textual segmentation description, DeiSAM leverages Large Language Models (LLMs) to generate first-order logic rules and performs differentiable forward reasoning on generated scene graphs. Subsequently, DeiSAM segments objects by matching them to the logically inferred image regions. As part of our evaluation, we propose the Deictic Visual Genome (DeiVG) dataset, containing paired visual input and complex, deictic textual prompts. Our empirical results demonstrate that DeiSAM is a substantial improvement over purely data-driven baselines for deictic promptable segmentation. | DeiSAM: Segment Anything with Deictic Prompting | [
"Hikaru Shindo",
"Manuel Brack",
"Gopika Sudhakaran",
"Devendra Singh Dhami",
"Patrick Schramowski",
"Kristian Kersting"
] | NeurIPS.cc/2024/Conference | 2402.14123 | [
"https://github.com/ml-research/deictic-segment-anything"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cmBjkpRuvw | @inproceedings{
ge2024axioms,
title={Axioms for {AI} Alignment from Human Feedback},
author={Luise Ge and Daniel Halpern and Evi Micha and Ariel D. Procaccia and Itai Shapira and Yevgeniy Vorobeychik and Junlin Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cmBjkpRuvw}
} | In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on pairwise comparisons made by humans. The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory. From this perspective, we can evaluate different aggregation methods via established axioms, examining whether these methods meet or fail well-known standards. We demonstrate that both the Bradley-Terry-Luce Model and its broad generalizations fail to meet basic axioms. In response, we develop novel rules for learning reward functions with strong axiomatic guarantees. A key innovation from the standpoint of social choice is that our problem has a *linear* structure, which greatly restricts the space of feasible rules and leads to a new paradigm that we call *linear social choice*. | Axioms for AI Alignment from Human Feedback | [
"Luise Ge",
"Daniel Halpern",
"Evi Micha",
"Ariel D. Procaccia",
"Itai Shapira",
"Yevgeniy Vorobeychik",
"Junlin Wu"
] | NeurIPS.cc/2024/Conference | 2405.14758 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=clqX9cVDKV | @inproceedings{
gao2024uniif,
title={Uni{IF}: Unified Molecule Inverse Folding},
author={Zhangyang Gao and Jue Wang and Cheng Tan and Lirong Wu and Yufei Huang and Siyuan Li and Zhirui Ye and Stan Z. Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=clqX9cVDKV}
} | Molecule inverse folding has been a long-standing challenge in chemistry and biology, with the potential to revolutionize drug discovery and material science. Despite specified models have been proposed for different small- or macro-molecules, few have attempted to unify the learning process, resulting in redundant efforts. Complementary to recent advancements in molecular structure prediction, such as RoseTTAFold All-Atom and AlphaFold3, we propose the unified model UniIF for the inverse folding of all molecules. We do such unification in two levels: 1) Data-Level: We propose a unified block graph data form for all molecules, including the local frame building and geometric feature initialization. 2) Model-Level: We introduce a geometric block attention network, comprising a geometric interaction, interactive attention and virtual long-term dependency modules, to capture the 3D interactions of all molecules. Through comprehensive evaluations across various tasks such as protein design, RNA design, and material design, we demonstrate that our proposed method surpasses state-of-the-art methods on all tasks. UniIF offers a versatile and effective solution for general molecule inverse folding. | UniIF: Unified Molecule Inverse Folding | [
"Zhangyang Gao",
"Jue Wang",
"Cheng Tan",
"Lirong Wu",
"Yufei Huang",
"Siyuan Li",
"Zhirui Ye",
"Stan Z. Li"
] | NeurIPS.cc/2024/Conference | 2405.18968 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=clTa4JFBML | @inproceedings{
li2024return,
title={Return of Unconditional Generation: A Self-supervised Representation Generation Method},
author={Tianhong Li and Dina Katabi and Kaiming He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=clTa4JFBML}
} | Unconditional generation -- the problem of modeling data distribution without relying on human-annotated labels -- is a long-standing and fundamental challenge in generative models, creating a potential of learning from large-scale unlabeled data. In the literature, the generation quality of an unconditional method has been much worse than that of its conditional counterpart. This gap can be attributed to the lack of semantic information provided by labels. In this work, we show that one can close this gap by generating semantic representations in the representation space produced by a self-supervised encoder. These representations can be used to condition the image generator. This framework, called Representation-Conditioned Generation (RCG), provides an effective solution to the unconditional generation problem without using labels. Through comprehensive experiments, we observe that RCG significantly improves unconditional generation quality: e.g., it achieves a new state-of-the-art FID of 2.15 on ImageNet 256x256, largely reducing the previous best of 5.91 by a relative 64%. Our unconditional results are situated in the same tier as the leading class-conditional ones. We hope these encouraging observations will attract the community's attention to the fundamental problem of unconditional generation. Code is available at [https://github.com/LTH14/rcg](https://github.com/LTH14/rcg). | Return of Unconditional Generation: A Self-supervised Representation Generation Method | [
"Tianhong Li",
"Dina Katabi",
"Kaiming He"
] | NeurIPS.cc/2024/Conference | 2312.03701 | [
"https://github.com/LTH14/rcg"
] | https://huggingface.co/papers/2312.03701 | 1 | 7 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=clQdPtooRD | @inproceedings{
kumar2024ojas,
title={Oja's Algorithm for Streaming Sparse {PCA}},
author={Syamantak Kumar and Purnamrita Sarkar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=clQdPtooRD}
} | Oja's algorithm for Streaming Principal Component Analysis (PCA) for $n$ data-points in a $d$ dimensional space achieves the same sin-squared error $O(r_{\mathsf{eff}}/n)$ as the offline algorithm in $O(d)$ space and $O(nd)$ time and a single pass through the datapoints. Here $r_{\mathsf{eff}}$ is the effective rank (ratio of the trace and the principal eigenvalue of the population covariance matrix $\Sigma$). Under this computational budget, we consider the problem of sparse PCA, where the principal eigenvector of $\Sigma$ is $s$-sparse, and $r_{\mathsf{eff}}$ can be large. In this setting, to our knowledge, *there are no known single-pass algorithms* that achieve the minimax error bound in $O(d)$ space and $O(nd)$ time without either requiring strong initialization conditions or assuming further structure (e.g., spiked) of the covariance matrix.
We show that a simple single-pass procedure that thresholds the output of Oja's algorithm (the Oja vector) can achieve the minimax error bound under some regularity conditions in $O(d)$ space and $O(nd)$ time.
We present a nontrivial and novel analysis of the entries of the unnormalized Oja vector, which involves the projection of a product of independent random matrices on a random initial vector. This is completely different from previous analyses of Oja's algorithm and matrix products, which have been done when the $r_{\mathsf{eff}}$ is bounded. | Oja's Algorithm for Streaming Sparse PCA | [
"Syamantak Kumar",
"Purnamrita Sarkar"
] | NeurIPS.cc/2024/Conference | 2402.07240 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=clDGHpx2la | @inproceedings{
huang2024inversionview,
title={InversionView: A General-Purpose Method for Reading Information from Neural Activations},
author={Xinting Huang and Madhur Panwar and Navin Goyal and Michael Hahn},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=clDGHpx2la}
} | The inner workings of neural networks can be better understood if we can fully decipher the information encoded in neural activations. In this paper, we argue that this information is embodied by the subset of inputs that give rise to similar activations. We propose InversionView, which allows us to practically inspect this subset by sampling from a trained decoder model conditioned on activations. This helps uncover the information content of activation vectors, and facilitates understanding of the algorithms implemented by transformer models. We present four case studies where we investigate models ranging from small transformers to GPT-2. In these studies, we show that InversionView can reveal clear information contained in activations, including basic information about tokens appearing in the context, as well as more complex information, such as the count of certain tokens, their relative positions, and abstract knowledge about the subject. We also provide causally verified circuits to confirm the decoded information. | InversionView: A General-Purpose Method for Reading Information from Neural Activations | [
"Xinting Huang",
"Madhur Panwar",
"Navin Goyal",
"Michael Hahn"
] | NeurIPS.cc/2024/Conference | 2405.17653 | [
"https://github.com/huangxt39/inversionview"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=clBiQUgj4w | @inproceedings{
lin2024cyclenet,
title={CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns},
author={Shengsheng Lin and Weiwei Lin and Xinyi HU and Wentai Wu and Ruichao Mo and Haocheng Zhong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=clBiQUgj4w}
} | The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (LTSF) tasks. Specifically, we introduce the Residual Cycle Forecasting (RCF) technique, which utilizes learnable recurrent cycles to model the inherent periodic patterns within sequences, and then performs predictions on the residual components of the modeled cycles. Combining RCF with a Linear layer or a shallow MLP forms the simple yet powerful method proposed in this paper, called CycleNet. CycleNet achieves state-of-the-art prediction accuracy in multiple domains including electricity, weather, and energy, while offering significant efficiency advantages by reducing over 90% of the required parameter quantity. Furthermore, as a novel plug-and-play technique, the RCF can also significantly improve the prediction accuracy of existing models, including PatchTST and iTransformer. The source code is available at: https://github.com/ACAT-SCUT/CycleNet. | CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns | [
"Shengsheng Lin",
"Weiwei Lin",
"Xinyi HU",
"Wentai Wu",
"Ruichao Mo",
"Haocheng Zhong"
] | NeurIPS.cc/2024/Conference | 2409.18479 | [
"https://github.com/ACAT-SCUT/CycleNet"
] | https://huggingface.co/papers/2409.18479 | 0 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=clAOSSzT6v | @inproceedings{
zarzar2024splitnerf,
title={SplitNe{RF}: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation},
author={Jesus Zarzar and Bernard Ghanem},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=clAOSSzT6v}
} | We present a novel approach for digitizing real-world objects by estimating their geometry, material properties, and environmental lighting from a set of posed images with fixed lighting. Our method incorporates into Neural Radiance Field (NeRF) pipelines the split sum approximation used with image-based lighting for real-time physically based rendering. We propose modeling the scene's lighting with a single scene-specific MLP representing pre-integrated image-based lighting at arbitrary resolutions. We accurately model pre-integrated lighting by exploiting a novel regularizer based on efficient Monte Carlo sampling. Additionally, we propose a new method of supervising self-occlusion predictions by exploiting a similar regularizer based on Monte Carlo sampling. Experimental results demonstrate the efficiency and effectiveness of our approach in estimating scene geometry, material properties, and lighting. Our method attains state-of-the-art relighting quality after only ${\sim}1$ hour of training in a single NVIDIA A100 GPU. | SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation | [
"Jesus Zarzar",
"Bernard Ghanem"
] | NeurIPS.cc/2024/Conference | 2311.16671 | [
"https://github.com/zarzarj/SplitNeRF"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=clAFYReaYE | @inproceedings{
alabdulmohsin2024fractal,
title={Fractal Patterns May Illuminate the Success of Next-Token Prediction},
author={Ibrahim Alabdulmohsin and Vinh Q. Tran and Mostafa Dehghani},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=clAFYReaYE}
} | We study the fractal structure of language, aiming to provide a precise formalism for quantifying properties that may have been previously suspected but not formally shown. We establish that language is: (1) self-similar, exhibiting complexities at all levels of granularity, with no particular characteristic context length, and (2) long-range dependent (LRD), with a Hurst parameter of approximately 0.7.
Based on these findings, we argue that short-term patterns/dependencies in language, such as in paragraphs, mirror the patterns/dependencies over larger scopes, like entire documents. This may shed some light on how next-token prediction can capture the structure of text across multiple levels of granularity, from words and clauses to broader contexts and intents. In addition, we carry out an extensive analysis across different domains and architectures, showing that fractal parameters are robust.
Finally, we demonstrate that the tiny variations in fractal parameters seen across LLMs improve upon perplexity-based bits-per-byte (BPB) in predicting their downstream performance. We hope these findings offer a fresh perspective on language and the mechanisms underlying the success of LLMs. | Fractal Patterns May Illuminate the Success of Next-Token Prediction | [
"Ibrahim Alabdulmohsin",
"Vinh Q. Tran",
"Mostafa Dehghani"
] | NeurIPS.cc/2024/Conference | 2402.01825 | [
""
] | https://huggingface.co/papers/2402.01825 | 0 | 2 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=cknAewsBhD | @inproceedings{
wu2024egsst,
title={{EGSST}: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection},
author={Sheng Wu and Hang Sheng and Hui Feng and Bo Hu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cknAewsBhD}
} | Event cameras provide exceptionally high temporal resolution in dynamic vision systems due to their unique event-driven mechanism. However, the sparse and asynchronous nature of event data makes frame-based visual processing methods inappropriate. This study proposes a novel framework, Event-based Graph Spatiotemporal Sensitive Transformer (EGSST), for the exploitation of spatial and temporal properties of event data. Firstly, a well-designed graph structure is employed to model event data, which not only preserves the original temporal data but also captures spatial details. Furthermore, inspired by the phenomenon that human eyes pay more attention to objects that produce significant dynamic changes, we design a Spatiotemporal Sensitivity Module (SSM) and an adaptive Temporal Activation Controller (TAC). Through these two modules, our framework can mimic the response of the human eyes in dynamic environments by selectively activating the temporal attention mechanism based on the relative dynamics of event data, thereby effectively conserving computational resources. In addition, the integration of a lightweight, multi-scale Linear Vision Transformer (LViT) markedly enhances processing efficiency. Our research proposes a fully event-driven approach, effectively exploiting the temporal precision of event data and optimising the allocation of computational resources by intelligently distinguishing the dynamics within the event data. The framework provides a lightweight, fast, accurate, and fully event-based solution for object detection tasks in complex dynamic environments, demonstrating significant practicality and potential for application. | EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection | [
"Sheng Wu",
"Hang Sheng",
"Hui Feng",
"Bo Hu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cjM2bhLOiC | @inproceedings{
wang2024improving,
title={Improving Generalization and Convergence by Enhancing Implicit Regularization},
author={Mingze Wang and Jinbo Wang and Haotian He and Zilin Wang and Guanhua Huang and Feiyu Xiong and Zhiyu li and Weinan E and Lei Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cjM2bhLOiC}
} | In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence.
Specifically, IRE decouples the dynamics of flat and sharp directions, which boosts the sharpness reduction along flat directions while maintaining the training stability in sharp directions. We show that IRE can be practically incorporated with *generic base optimizers* without introducing significant computational overload. Experiments show that IRE consistently improves the generalization performance for image classification tasks across a variety of benchmark datasets (CIFAR-10/100, ImageNet) and models (ResNets and ViTs).
Surprisingly, IRE also achieves a $2\times$ *speed-up* compared to AdamW in the pre-training of Llama models (of sizes ranging from 60M to 229M) on datasets including Wikitext-103, Minipile, and Openwebtext. Moreover, we provide theoretical guarantees, showing that IRE can substantially accelerate the convergence towards flat minima in Sharpness-aware Minimization (SAM). | Improving Generalization and Convergence by Enhancing Implicit Regularization | [
"Mingze Wang",
"Jinbo Wang",
"Haotian He",
"Zilin Wang",
"Guanhua Huang",
"Feiyu Xiong",
"Zhiyu li",
"Weinan E",
"Lei Wu"
] | NeurIPS.cc/2024/Conference | 2405.20763 | [
"https://github.com/wmz9/ire-algorithm-framework"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cjH0Qsgd0D | @inproceedings{
chen2024learning,
title={Learning Macroscopic Dynamics from Partial Microscopic Observations},
author={Mengyi Chen and Qianxiao Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cjH0Qsgd0D}
} | Macroscopic observables of a system are of keen interest in real applications such as the design of novel materials. Current methods rely on microscopic trajectory simulations, where the forces on all microscopic coordinates need to be computed or measured. However, this can be computationally prohibitive for realistic systems. In this paper, we propose a method to learn macroscopic dynamics requiring only force computations on a subset of the microscopic coordinates. Our method relies on a sparsity assumption: the force on each microscopic coordinate relies only on a small number of other coordinates. The main idea of our approach is to map the training procedure on the macroscopic coordinates back to the microscopic coordinates, on which partial force computations can be used as stochastic estimation to update model parameters. We provide a theoretical justification of this under suitable conditions. We demonstrate the accuracy, force computation efficiency, and robustness of our method on learning macroscopic closure models from a variety of microscopic systems, including those modeled by partial differential equations or molecular dynamics simulations. | Learning Macroscopic Dynamics from Partial Microscopic Observations | [
"Mengyi Chen",
"Qianxiao Li"
] | NeurIPS.cc/2024/Conference | 2410.23938 | [
"https://github.com/MLDS-NUS/Learn-Partial"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ciwOcmo8CC | @inproceedings{
chen2024iffont,
title={{IF}-Font: Ideographic Description Sequence-Following Font Generation},
author={Xinping Chen and Xiao Ke and Wenzhong Guo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ciwOcmo8CC}
} | Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at [https://github.com/Stareven233/IF-Font](https://github.com/Stareven233/IF-Font). | IF-Font: Ideographic Description Sequence-Following Font Generation | [
"Xinping Chen",
"Xiao Ke",
"Wenzhong Guo"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=chnJT8Nj8X | @inproceedings{
hu2024transformer,
title={Transformer Doctor: Diagnosing and Treating Vision Transformers},
author={Jiacong Hu and Hao Chen and Kejia Chen and Yang Gao and Jingwen Ye and Xingen Wang and Mingli Song and Zunlei Feng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=chnJT8Nj8X}
} | Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance. | Transformer Doctor: Diagnosing and Treating Vision Transformers | [
"Jiacong Hu",
"Hao Chen",
"Kejia Chen",
"Yang Gao",
"Jingwen Ye",
"Xingen Wang",
"Mingli Song",
"Zunlei Feng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=chLoLUHnai | @inproceedings{
cai2024large,
title={Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization},
author={Yuhang Cai and Jingfeng Wu and Song Mei and Michael Lindsey and Peter Bartlett},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=chLoLUHnai}
} | The typical training of neural networks using large stepsize gradient descent (GD) under the logistic loss often involves two distinct phases, where the empirical risk oscillates in the first phase but decreases monotonically in the second phase. We investigate this phenomenon in two-layer networks that satisfy a near-homogeneity condition. We show that the second phase begins once the empirical risk falls below a certain threshold, dependent on the stepsize. Additionally, we show that the normalized margin grows nearly monotonically in the second phase, demonstrating an implicit bias of GD in training non-homogeneous predictors. If the dataset is linearly separable and the derivative of the activation function is bounded away from zero, we show that the average empirical risk decreases, implying that the first phase must stop in finite steps. Finally, we demonstrate that by choosing a suitably large stepsize, GD that undergoes this phase transition is more efficient than GD that monotonically decreases the risk. Our analysis applies to networks of any width, beyond the well-known neural tangent kernel and mean-field regimes. | Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization | [
"Yuhang Cai",
"Jingfeng Wu",
"Song Mei",
"Michael Lindsey",
"Peter Bartlett"
] | NeurIPS.cc/2024/Conference | 2406.08654 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cgiOX8lfwG | @inproceedings{
chen2024discretely,
title={Discretely beyond \$1/e\$: Guided Combinatorial Algortihms for Submodular Maximization},
author={Yixin Chen and Ankur Nath and Chunli Peng and Alan Kuhnle},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cgiOX8lfwG}
} | For constrained, not necessarily monotone submodular maximization, all known approximation algorithms with ratio greater than $1/e$ require continuous ideas, such as queries to the multilinear extension of a submodular function and its gradient, which are typically expensive to simulate with the original set function. For combinatorial algorithms, the best known approximation ratios for both size and matroid constraint are obtained by a simple randomized greedy algorithm of Buchbinder et al. [9]: $1/e \approx 0.367$ for size constraint and $0.281$ for the matroid constraint in $\mathcal O (kn)$ queries, where $k$ is the rank of the matroid. In this work, we develop the first combinatorial algorithms to break the $1/e$ barrier: we obtain approximation ratio of $0.385$ in $\mathcal O (kn)$ queries to the submodular set function for size constraint, and $0.305$ for a general matroid constraint. These are achieved by guiding the randomized greedy algorithm with a fast local search algorithm. Further, we develop deterministic versions of these algorithms, maintaining the same ratio and asymptotic time complexity. Finally, we develop a deterministic, nearly linear time algorithm with ratio $0.377$. | Discretely beyond 1/e: Guided Combinatorial Algortihms for Submodular Maximization | [
"Yixin Chen",
"Ankur Nath",
"Chunli Peng",
"Alan Kuhnle"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cg1vwt5Xou | @inproceedings{
benomar2024lookback,
title={Lookback Prophet Inequalities},
author={Ziyad Benomar and Dorian Baudry and Vianney Perchet},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cg1vwt5Xou}
} | Prophet inequalities are fundamental optimal stopping problems, where a decision-maker observes sequentially items with values sampled independently from known distributions, and must decide at each new observation to either stop and gain the current value or reject it irrevocably and move to the next step. This model is often too pessimistic and does not adequately represent real-world online selection processes. Potentially, rejectesd items can be revisited and a fraction of their value can be recovered. To analyze this problem, we consider general decay functions $D_1,D_2,\ldots$, quantifying the value to be recovered from a rejected item, depending on how far it has been observed in the past. We analyze how lookback improves, or not, the competitive ratio in prophet inequalities in different order models.
We show that, under mild monotonicity assumptions on the decay functions, the problem can be reduced to the case where all the decay functions are equal to the same function $x \mapsto \gamma x$, where $\gamma = \inf_{x>0} \inf_{j \geq 1} D_j(x)/x$. Consequently, we focus on this setting and refine the analyses of the competitive ratios, with upper and lower bounds expressed as increasing functions of $\gamma$. | Lookback Prophet Inequalities | [
"Ziyad Benomar",
"Dorian Baudry",
"Vianney Perchet"
] | NeurIPS.cc/2024/Conference | 2406.06805 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cfrDLD1wfO | @inproceedings{
liu2024graph,
title={Graph Diffusion Transformers for Multi-Conditional Molecular Generation},
author={Gang Liu and Jiaxin Xu and Tengfei Luo and Meng Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cfrDLD1wfO}
} | Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecule generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We present the Graph Diffusion Transformer (Graph DiT) for multi-conditional molecular generation. Graph DiT has a condition encoder to learn the representation of numerical and categorical properties and utilizes a Transformer-based graph denoiser to achieve molecular graph denoising under conditions. Unlike previous graph diffusion models that add noise separately on the atoms and bonds in the forward diffusion process, we propose a graph-dependent noise model for training Graph DiT, designed to accurately estimate graph-related noise in molecules. We extensively validate the Graph DiT for multi-conditional polymer and small molecule generation. Results demonstrate our superiority across metrics from distribution learning to condition control for molecular properties. A polymer inverse design task for gas separation with feedback from domain experts further demonstrates its practical utility. The code is available at https://github.com/liugangcode/Graph-DiT. | Graph Diffusion Transformers for Multi-Conditional Molecular Generation | [
"Gang Liu",
"Jiaxin Xu",
"Tengfei Luo",
"Meng Jiang"
] | NeurIPS.cc/2024/Conference | 2401.13858 | [
"https://github.com/liugangcode/MCD"
] | https://huggingface.co/papers/2401.13858 | 1 | 0 | 0 | 4 | [] | [] | [
"liuganghuggingface/Polymer-Design-With-GraphDiT"
] | [] | [] | [
"liuganghuggingface/Polymer-Design-With-GraphDiT"
] | 1 | oral |
null | https://openreview.net/forum?id=cesWi7mMLY | @inproceedings{
miao2024longtailed,
title={Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation},
author={Wenjun Miao and Guansong Pang and Jin Zheng and Xiao Bai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cesWi7mMLY}
} | One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers ($\textit{i.e.}$, pseudo OOD samples) to train OOD detectors.
However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in Long-Tailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, $\textit{i.e.}$, the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers.
In this work, we propose a novel approach, namely $\textit{normalized outlier distribution adaptation}$ (AdaptOD), to tackle this distribution shift problem.
One of its key components is $\textit{dynamic outlier distribution adaptation}$ that effectively adapts a vanilla outlier distribution based on the outlier samples to the true OOD distribution by utilizing the OOD knowledge in the predicted OOD samples during inference.
Further, to obtain a more reliable set of predicted OOD samples on long-tailed ID data, a novel $\textit{dual-normalized energy loss}$ is introduced in AdaptOD, which leverages class- and sample-wise normalized energy to enforce a more balanced prediction energy on imbalanced ID samples. This helps avoid bias toward the head samples and learn a substantially better vanilla outlier distribution than existing energy losses during training. It also eliminates the need of manually tuning the sensitive margin hyperparameters in energy losses.
Empirical results on three popular benchmarks for OOD detection in LTR show the superior performance of AdaptOD over state-of-the-art methods.
Code is available at https://github.com/mala-lab/AdaptOD. | Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation | [
"Wenjun Miao",
"Guansong Pang",
"Jin Zheng",
"Xiao Bai"
] | NeurIPS.cc/2024/Conference | 2410.20807 | [
"https://github.com/mala-lab/adaptod"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ceIO1w0PmT | @inproceedings{
wang2024omnijarvis,
title={Omni{JARVIS}: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents},
author={Zihao Wang and Shaofei Cai and Zhancun Mu and Haowei Lin and Ceyao Zhang and Xuejie Liu and Qing Li and Anji Liu and Xiaojian Ma and Yitao Liang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ceIO1w0PmT}
} | This paper presents OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in Minecraft. Compared to prior works that either emit textual goals to separate controllers or produce the control command directly, OmniJARVIS seeks a different path to ensure both strong reasoning and efficient decision-making capabilities via unified tokenization of multimodal interaction data. First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories $\tau = \{o_0, a_0, \dots\}$ and an imitation learning policy decoder conditioned on these tokens. These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models. With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc into unified token sequences and model them with autoregressive transformers. Thanks to the semantically meaningful behavior tokens, the resulting VLA model, OmniJARVIS, can reason (by producing chain-of-thoughts), plan, answer questions, and act (by producing behavior tokens for the imitation learning policy decoder). OmniJARVIS demonstrates excellent performances on a comprehensive collection of atomic, programmatic, and open-ended tasks in open-world Minecraft. Our analysis further unveils the crucial design principles in interaction data formation, unified tokenization, and its scaling potentials. The dataset, models, and code will be released at https://craftjarvis.org/OmniJARVIS. | OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents | [
"Zihao Wang",
"Shaofei Cai",
"Zhancun Mu",
"Haowei Lin",
"Ceyao Zhang",
"Xuejie Liu",
"Qing Li",
"Anji Liu",
"Xiaojian Ma",
"Yitao Liang"
] | NeurIPS.cc/2024/Conference | 2407.00114 | [
""
] | https://huggingface.co/papers/2407.00114 | 4 | 12 | 4 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=cdTTTJfJe3 | @inproceedings{
guo2024detective,
title={DeTeCtive: Detecting {AI}-generated Text via Multi-Level Contrastive Learning},
author={Xun Guo and Yongxin He and Shan Zhang and Ting Zhang and Wanquan Feng and Haibin Huang and Chongyang Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cdTTTJfJe3}
} | Current techniques for detecting AI-generated text are largely confined to manual feature crafting and supervised binary classification paradigms. These methodologies typically lead to performance bottlenecks and unsatisfactory generalizability. Consequently, these methods are often inapplicable for out-of-distribution (OOD) data and newly emerged large language models (LLMs). In this paper, we revisit the task of AI-generated text detection. We argue that the key to accomplishing this task lies in distinguishing writing styles of different authors, rather than simply classifying the text into human-written or AI-generated text. To this end, we propose DeTeCtive, a multi-task auxiliary, multi-level contrastive learning framework. DeTeCtive is designed to facilitate the learning of distinct writing styles, combined with a dense information retrieval pipeline for AI-generated text detection. Our method is compatible with a range of text encoders. Extensive experiments demonstrate that our method enhances the ability of various text encoders in detecting AI-generated text across multiple benchmarks and achieves state-of-the-art results. Notably, in OOD zero-shot evaluation, our method outperforms existing approaches by a large margin. Moreover, we find our method boasts a Training-Free Incremental Adaptation (TFIA) capability towards OOD data, further enhancing its efficacy in OOD detection scenarios. We will open-source our code and models in hopes that our work will spark new thoughts in the field of AI-generated text detection, ensuring safe application of LLMs and enhancing compliance. | DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning | [
"Xun Guo",
"Yongxin He",
"Shan Zhang",
"Ting Zhang",
"Wanquan Feng",
"Haibin Huang",
"Chongyang Ma"
] | NeurIPS.cc/2024/Conference | 2410.20964 | [
"https://github.com/heyongxin233/detective"
] | https://huggingface.co/papers/2410.20964 | 1 | 0 | 0 | 7 | [
"heyongxin233/DeTeCtive"
] | [] | [] | [
"heyongxin233/DeTeCtive"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=ccQ4fmwLDb | @inproceedings{
wang2024belm,
title={{BELM}: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models},
author={Fangyikang Wang and Hubery Yin and Yue-Jiang Dong and Huminhao Zhu and Chao Zhang and Hanbin Zhao and Hui Qian and Chen Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ccQ4fmwLDb}
} | The inversion of diffusion model sampling, which aims to find the corresponding initial noise of a sample, plays a critical role in various tasks.
Recently, several heuristic exact inversion samplers have been proposed to address the inexact inversion issue in a training-free manner.
However, the theoretical properties of these heuristic samplers remain unknown and they often exhibit mediocre sampling quality.
In this paper, we introduce a generic formulation, \emph{Bidirectional Explicit Linear Multi-step} (BELM) samplers, of the exact inversion samplers, which includes all previously proposed heuristic exact inversion samplers as special cases.
The BELM formulation is derived from the variable-stepsize-variable-formula linear multi-step method via integrating a bidirectional explicit constraint. We highlight this bidirectional explicit constraint is the key of mathematically exact inversion.
We systematically investigate the Local Truncation Error (LTE) within the BELM framework and show that the existing heuristic designs of exact inversion samplers yield sub-optimal LTE.
Consequently, we propose the Optimal BELM (O-BELM) sampler through the LTE minimization approach.
We conduct additional analysis to substantiate the theoretical stability and global convergence property of the proposed optimal sampler.
Comprehensive experiments demonstrate our O-BELM sampler establishes the exact inversion property while achieving high-quality sampling.
Additional experiments in image editing and image interpolation highlight the extensive potential of applying O-BELM in varying applications. | BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models | [
"Fangyikang Wang",
"Hubery Yin",
"Yue-Jiang Dong",
"Huminhao Zhu",
"Chao Zhang",
"Hanbin Zhao",
"Hui Qian",
"Chen Li"
] | NeurIPS.cc/2024/Conference | 2410.07273 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cbkJBYIkID | @inproceedings{
wei2024mitigating,
title={Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor},
author={Shaokui Wei and Hongyuan Zha and Baoyuan Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cbkJBYIkID}
} | Data-poisoning backdoor attacks are serious security threats to machine learning models, where an adversary can manipulate the training dataset to inject backdoors into models. In this paper, we focus on in-training backdoor defense, aiming to train a clean model even when the dataset may be potentially poisoned. Unlike most existing methods that primarily detect and remove/unlearn suspicious samples to mitigate malicious backdoor attacks, we propose a novel defense approach called PDB (Proactive Defensive Backdoor). Specifically, PDB leverages the “home field” advantage of defenders by proactively injecting a defensive backdoor into the model during training. Taking advantage of controlling the training process, the defensive backdoor is designed to suppress the malicious backdoor effectively while remaining secret to attackers. In addition, we introduce a reversible mapping to determine the defensive target label. During inference, PDB embeds a defensive trigger in the inputs and reverses the model’s prediction, suppressing malicious backdoor and ensuring the model's utility on the original task. Experimental results across various datasets and models demonstrate that our approach achieves state-of-the-art defense performance against a wide range of backdoor attacks. The code is available at https://github.com/shawkui/Proactive_Defensive_Backdoor. | Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor | [
"Shaokui Wei",
"Hongyuan Zha",
"Baoyuan Wu"
] | NeurIPS.cc/2024/Conference | 2405.16112 | [
"https://github.com/sclbd/backdoorbench"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ca2mABGV6p | @inproceedings{
li2024faster,
title={Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference},
author={Senmao Li and taihang Hu and Joost van de Weijer and Fahad Khan and Tao Liu and Linxuan Li and Shiqi Yang and Yaxing Wang and Ming-Ming Cheng and jian Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ca2mABGV6p}
} | One of the main drawback of diffusion models is the slow inference time for image generation. Among the most successful approaches to addressing this problem are distillation methods. However, these methods require considerable computational resources. In this paper, we take another approach to diffusion model acceleration. We conduct a comprehensive study of the UNet encoder and empirically analyze the encoder features. This provides insights regarding their changes during the inference process. In particular, we find that encoder features change minimally, whereas the decoder features exhibit substantial variations across different time-steps. This insight motivates us to omit encoder computation at certain adjacent time-steps and reuse encoder features of previous time-steps as input to the decoder in multiple time-steps. Importantly, this allows us to perform decoder computation in parallel, further accelerating the denoising process. Additionally, we introduce a prior noise injection method to improve the texture details in the generated image. Besides the standard text-to-image task, we also validate our approach on other tasks: text-to-video, personalized generation and reference-guided generation. Without utilizing any knowledge distillation technique, our approach accelerates both the Stable Diffusion (SD) and DeepFloyd-IF model sampling by 41$\%$ and 24$\%$ respectively, and DiT model sampling by 34$\%$, while maintaining high-quality generation performance. Our code will be publicly released. | Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference | [
"Senmao Li",
"taihang Hu",
"Joost van de Weijer",
"Fahad Khan",
"Tao Liu",
"Linxuan Li",
"Shiqi Yang",
"Yaxing Wang",
"Ming-Ming Cheng",
"jian Yang"
] | NeurIPS.cc/2024/Conference | 2312.09608 | [
"https://github.com/hutaihang/faster-diffusion"
] | https://huggingface.co/papers/2312.09608 | 5 | 13 | 1 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=cYZibc2gKf | @inproceedings{
chaudhari2024abstract,
title={Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation},
author={Shreyas Chaudhari and Ameet Deshpande and Bruno Castro da Silva and Philip S. Thomas},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cYZibc2gKf}
} | Evaluating policies using off-policy data is crucial for applying reinforcement learning to real-world problems such as healthcare and autonomous driving. Previous methods for *off-policy evaluation* (OPE) generally suffer from high variance or irreducible bias, leading to unacceptably high prediction errors. In this work, we introduce STAR, a framework for OPE that encompasses a broad range of estimators -- which include existing OPE methods as special cases -- that achieve lower mean squared prediction errors. STAR leverages state abstraction to distill complex, potentially continuous problems into compact, discrete models which we call *abstract reward processes* (ARPs). Predictions from ARPs estimated from off-policy data are provably consistent (asymptotically correct). Rather than proposing a specific estimator, we present a new framework for OPE and empirically demonstrate that estimators within STAR outperform existing methods. The best STAR estimator outperforms baselines in all twelve cases studied, and even the median STAR estimator surpasses the baselines in seven out of the twelve cases. | Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation | [
"Shreyas Chaudhari",
"Ameet Deshpande",
"Bruno Castro da Silva",
"Philip S. Thomas"
] | NeurIPS.cc/2024/Conference | 2410.02172 | [
"https://github.com/shreyasc-13/star"
] | https://huggingface.co/papers/2410.02172 | 0 | 1 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=cV4fcjcwmz | @inproceedings{
huang2024extracting,
title={Extracting Training Data from Molecular Pre-trained Models},
author={Renhong Huang and Jiarong Xu and Zhiming Yang and Xiang Si and Xin Jiang and Hanyang Yuan and Chunping Wang and Yang Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cV4fcjcwmz}
} | Graph Neural Networks (GNNs) have significantly advanced the field of drug discovery, enhancing the speed and efficiency of molecular identification. However, training these GNNs demands vast amounts of molecular data, which has spurred the emergence of collaborative model-sharing initiatives. These initiatives facilitate the sharing of molecular pre-trained models among organizations without exposing proprietary training data. Despite the benefits, these molecular pre-trained models may still pose privacy risks. For example, malicious adversaries could perform data extraction attack to recover private training data, thereby threatening commercial secrets and collaborative trust. This work, for the first time, explores the risks of extracting private training molecular data from molecular pre-trained models. This task is nontrivial as the molecular pre-trained models are non-generative and exhibit a diversity of model architectures, which differs significantly from language and image models. To address these issues, we introduce a molecule generation approach and propose a novel, model-independent scoring function for selecting promising molecules. To efficiently reduce the search space of potential molecules, we further introduce a Molecule Extraction Policy Network for molecule extraction. Our experiments demonstrate that even with only query access to molecular pre-trained models, there is a considerable risk of extracting training data, challenging the assumption that model sharing alone provides adequate protection against data extraction attacks. Our codes are publicly available at: \url{https://github.com/renH2/Molextract}. | Extracting Training Data from Molecular Pre-trained Models | [
"Renhong Huang",
"Jiarong Xu",
"Zhiming Yang",
"Xiang Si",
"Xin Jiang",
"Hanyang Yuan",
"Chunping Wang",
"Yang Yang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cV2LKBdlz4 | @inproceedings{
hu2024on,
title={On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)},
author={Jerry Yao-Chieh Hu and Weimin Wu and Zhuoru Li and Sophia Pi and Zhao Song and Han Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cV2LKBdlz4}
} | We investigate the statistical and computational limits of latent **Di**ffusion **T**ransformers (**DiTs**) under the low-dimensional linear latent space assumption. Statistically, we study the universal approximation and sample complexity of the DiTs score function, as well as the distribution recovery property of the initial data. Specifically, under mild data assumptions, we derive an approximation error bound for the score network of latent DiTs, which is sub-linear in the latent space dimension. Additionally, we derive the corresponding sample complexity bound and show that the data distribution generated from the estimated score function converges toward a proximate area of the original one.
Computationally, we characterize the hardness of both forward inference and backward computation of latent DiTs, assuming the Strong Exponential Time Hypothesis (SETH). For forward inference, we identify efficient criteria for all possible latent DiTs inference algorithms and showcase our theory by pushing the efficiency toward almost-linear time inference. For backward computation, we leverage the low-rank structure within the gradient computation of DiTs training for possible algorithmic speedup. Specifically, we show that such speedup achieves almost-linear time latent DiTs training by casting the DiTs gradient as a series of chained low-rank approximations with bounded error.
Under the low-dimensional assumption, we show that the statistical rates and the computational efficiency are all dominated by the dimension of the subspace, suggesting that latent DiTs have the potential to bypass the challenges associated with the high dimensionality of initial data. | On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs) | [
"Jerry Yao-Chieh Hu",
"Weimin Wu",
"Zhuoru Li",
"Sophia Pi",
"Zhao Song",
"Han Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cUcvlgkQxP | @inproceedings{
jang2024are,
title={Are Multiple Instance Learning Algorithms Learnable for Instances?},
author={Jaeseok Jang and HYUK-YOON KWON},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cUcvlgkQxP}
} | Multiple Instance Learning (MIL) has been increasingly adopted to mitigate the high costs and complexity associated with labeling individual instances, learning instead from bags of instances labeled at the bag level and enabling instance-level labeling. While existing research has primarily focused on the learnability of MIL at the bag level, there is an absence of theoretical exploration to check if a given MIL algorithm is learnable at the instance level. This paper proposes a theoretical framework based on probably approximately correct (PAC) learning theory to assess the instance-level learnability of deep multiple instance learning (Deep MIL) algorithms. Our analysis exposes significant gaps between current Deep MIL algorithms, highlighting the theoretical conditions that must be satisfied by MIL algorithms to ensure instance-level learnability. With these conditions, we interpret the learnability of the representative Deep MIL algorithms and validate them through empirical studies. | Are Multiple Instance Learning Algorithms Learnable for Instances? | [
"Jaeseok Jang",
"HYUK-YOON KWON"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cUGf2HaNcs | @inproceedings{
ghasemabadi2024learning,
title={Learning Truncated Causal History Model for Video Restoration},
author={Amirhosein Ghasemabadi and Muhammad Kamran Janjua and Mohammad Salameh and Di Niu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cUGf2HaNcs}
} | One key challenge to video restoration is to model the transition dynamics of video frames governed by motion. In this work, we propose Turtle to learn the truncated causal history model for efficient and high-performing video restoration. Unlike traditional methods that process a range of contextual frames in parallel, Turtle enhances efficiency by storing and summarizing a truncated history of the input frame latent representation into an evolving historical state. This is achieved through a sophisticated similarity-based retrieval mechanism that implicitly accounts for inter-frame motion and alignment. The causal design in Turtle enables recurrence in inference through state-memorized historical features while allowing parallel training by sampling truncated video clips. We report new state-of-the-art results on a multitude of video restoration benchmark tasks, including video desnowing, nighttime video deraining, video raindrops and rain streak removal, video super-resolution, real-world and synthetic video deblurring, and blind video denoising while reducing the computational cost compared to existing best contextual methods on all these tasks. | Learning Truncated Causal History Model for Video Restoration | [
"Amirhosein Ghasemabadi",
"Muhammad Kamran Janjua",
"Mohammad Salameh",
"Di Niu"
] | NeurIPS.cc/2024/Conference | 2410.03936 | [
"https://github.com/Ascend-Research/Turtle"
] | https://huggingface.co/papers/2410.03936 | 1 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=cU8d7LeOyx | @inproceedings{
g{\"u}nther2024causal,
title={Causal discovery with endogenous context variables},
author={Wiebke G{\"u}nther and Oana-Iuliana Popescu and Martin Rabel and Urmi Ninad and Andreas Gerhardus and Jakob Runge},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cU8d7LeOyx}
} | Systems with variations of the underlying generating mechanism between different contexts, i.e., different environments or internal states in which the system operates, are common in the real world, such as soil moisture regimes in Earth science. Besides understanding the shared properties of the system, in practice, the question of context-specific properties, i.e., the change in causal relationships between contexts, arises. For real-world data, contexts are often driven by system variables, e.g., precipitation highly influences soil moisture. Nevertheless, this setup needs to be studied more. To account for such endogenous contexts in causal discovery, our work proposes a constraint-based method that can efficiently discover context-specific causal graphs using an adaptive testing approach. Our approach tests conditional independence on the pooled datasets to infer the dependence between system variables, including the context, to avoid introducing selection bias. To yield context-specific insights, conditional independence is tested on context-specific data. We work out the theoretical framework for this adaptive testing approach and give a detailed discussion of the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. A simulation study to evaluate numerical properties shows that our approach behaves as expected, but also leads to a further understanding of current limitations and viable extensions. | Causal discovery with endogenous context variables | [
"Wiebke Günther",
"Oana-Iuliana Popescu",
"Martin Rabel",
"Urmi Ninad",
"Andreas Gerhardus",
"Jakob Runge"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cSfxzCozPU | @inproceedings{
clement2024distributional,
title={Distributional regression: {CRPS}-error bounds for model fitting, model selection and convex aggregation},
author={Dombry Clement and Ahmed Zaoui},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cSfxzCozPU}
} | Distributional regression aims at estimating the conditional distribution of a target variable given explanatory co-variates. It is a crucial tool for forecasting when a precise uncertainty quantification is required. A popular methodology consists in fitting a parametric model via empirical risk minimization where the risk is measured by the Continuous Rank Probability Score (CRPS). For independent and identically distributed observations, we provide a concentration result for the estimation error and an upper bound for its expectation. Furthermore, we consider model selection performed by minimization of the validation error and provide a concentration bound for the regret. A similar result is proved for convex aggregation of models. Finally, we show that our results may be applied to various models such as EMOS, distributional regression networks, distributional nearest neighbours or distributional random forests and we illustrate our findings on two data sets (QSAR aquatic toxicity and Airfoil self-noise). | Distributional regression: CRPS-error bounds for model fitting, model selection and convex aggregation | [
"Dombry Clement",
"Ahmed Zaoui"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cS63YtJ49A | @inproceedings{
ye2024trainingfree,
title={Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy},
author={Hancheng Ye and Jiakang Yuan and Renqiu Xia and Xiangchao Yan and Tao Chen and Junchi Yan and Botian Shi and Bo Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cS63YtJ49A}
} | Diffusion models have recently achieved great success in the synthesis of high-quality images and videos. However, the existing denoising techniques in diffusion models are commonly based on step-by-step noise predictions, which suffers from high computation cost, resulting in a prohibitive latency for interactive applications. In this paper, we propose AdaptiveDiffusion to relieve this bottleneck by adaptively reducing the noise prediction steps during the denoising process. Our method considers the potential of skipping as many noise prediction steps as possible while keeping the final denoised results identical to the original full-step ones. Specifically, the skipping strategy is guided by the third-order latent difference that indicates the stability between timesteps during the denoising process, which benefits the reusing of previous noise prediction results. Extensive experiments on image and video diffusion models demonstrate that our method can significantly speed up the denoising process while generating identical results to the original process, achieving up to an average 2-5x speedup without quality degradation. The code is available at https://github.com/UniModal4Reasoning/AdaptiveDiffusion | Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy | [
"Hancheng Ye",
"Jiakang Yuan",
"Renqiu Xia",
"Xiangchao Yan",
"Tao Chen",
"Junchi Yan",
"Botian Shi",
"Bo Zhang"
] | NeurIPS.cc/2024/Conference | 2410.09873 | [
"https://github.com/unimodal4reasoning/adaptivediffusion"
] | https://huggingface.co/papers/2410.09873 | 1 | 0 | 0 | 8 | [] | [
"HankYe/Sampled_AIGCBench_text2image_ar_0.625"
] | [] | [] | [
"HankYe/Sampled_AIGCBench_text2image_ar_0.625"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=cRlQHncjwT | @inproceedings{
nock2024generative,
title={Generative Forests},
author={Richard Nock and Mathieu Guillame-Bert},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cRlQHncjwT}
} | We focus on generative AI for a type of data that still represent one of the most prevalent form of data: tabular data. We introduce a new powerful class of forest-based models fit for such tasks and a simple training algorithm with strong convergence guarantees in a boosting model that parallels that of the original weak / strong supervised learning setting. This algorithm can be implemented by a few tweaks to the most popular induction scheme for decision tree induction (*i.e. supervised learning*) with two classes. Experiments on the quality of generated data display substantial improvements compared to the state of the art. The losses our algorithm minimize and the structure of our models make them practical for related tasks that require fast estimation of a density given a generative model and an observation (even partially specified): such tasks include missing data imputation and density estimation. Additional experiments on these tasks reveal that our models can be notably good contenders to diverse state of the art methods, relying on models as diverse as (or mixing elements of) trees, neural nets, kernels or graphical models. | Generative Forests | [
"Richard Nock",
"Mathieu Guillame-Bert"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/AlCorreia/GeFs"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=cRLFvSOrzt | @inproceedings{
livni2024credit,
title={Credit Attribution and Stable Compression},
author={Roi Livni and Shay Moran and Kobbi Nissim and Chirag Pabbaraju},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cRLFvSOrzt}
} | Credit attribution is crucial across various fields. In academic research, proper citation acknowledges prior work and establishes original contributions. Similarly, in generative models, such as those trained on existing artworks or music, it is important to ensure that any generated content influenced by these works appropriately credits the original creators.
We study credit attribution by machine learning algorithms. We propose new definitions--relaxations of Differential Privacy--that weaken the stability guarantees for a designated subset of $k$ datapoints. These $k$ datapoints can be used non-stably with permission from their owners, potentially in exchange for compensation. Meanwhile, the remaining datapoints are guaranteed to have no significant influence on the algorithm's output.
Our framework extends well-studied notions of stability, including Differential Privacy ($k = 0$), differentially private learning with public data (where the $k$ public datapoints are fixed in advance),
and stable sample compression (where the $k$ datapoints are selected adaptively by the algorithm).
We examine the expressive power of these stability notions within the PAC learning framework, provide a comprehensive characterization of learnability for algorithms adhering to these principles, and propose directions and questions for future research. | Credit Attribution and Stable Compression | [
"Roi Livni",
"Shay Moran",
"Kobbi Nissim",
"Chirag Pabbaraju"
] | NeurIPS.cc/2024/Conference | 2406.15916 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cR2QDzdpEv | @inproceedings{
bukharin2024robust,
title={Robust Reinforcement Learning from Corrupted Human Feedback},
author={Alexander Bukharin and Ilgee Hong and Haoming Jiang and Zichong Li and Qingru Zhang and Zixuan Zhang and Tuo Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cR2QDzdpEv}
} | Reinforcement learning from human feedback (RLHF) provides a principled framework for aligning AI systems with human preference data. For various reasons, e.g., personal bias, context ambiguity, lack of training, etc, human annotators may give incorrect or inconsistent preference labels. To tackle this challenge, we propose a robust RLHF approach -- $R^3M$, which models the potentially corrupted preference label as sparse outliers. Accordingly, we formulate the robust reward learning as an $\ell_1$-regularized maximum likelihood estimation problem. Computationally, we develop an efficient alternating optimization algorithm, which only incurs negligible computational overhead compared with the standard RLHF approach. Theoretically, we prove that under proper regularity conditions, $R^3M$ can consistently learn the underlying reward and identify outliers, provided that the number of outlier labels scales sublinearly with the preference sample size. Furthermore, we remark that $R^3M$ is versatile and can be extended to various preference optimization methods, including direct preference optimization (DPO). Our experiments on robotic control and natural language generation with large language models (LLMs) show that $R^3M$ improves robustness of the reward against several types of perturbations to the preference data. | Robust Reinforcement Learning from Corrupted Human Feedback | [
"Alexander Bukharin",
"Ilgee Hong",
"Haoming Jiang",
"Zichong Li",
"Qingru Zhang",
"Zixuan Zhang",
"Tuo Zhao"
] | NeurIPS.cc/2024/Conference | 2406.15568 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cQoAgPBARc | @inproceedings{
tang2024improving,
title={Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn},
author={Hongyao Tang and Glen Berseth},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cQoAgPBARc}
} | Deep neural networks provide Reinforcement Learning (RL) powerful function approximators to address large-scale decision-making problems. However, these approximators introduce challenges due to the non-stationary nature of RL training. One source of the challenges in RL is that output predictions can churn, leading to uncontrolled changes after each batch update for states not included in the batch. Although such a churn phenomenon exists in each step of network training, it remains under-explored on how churn occurs and impacts RL. In this work, we start by characterizing churn in a view of Generalized Policy Iteration with function approximation, and we discover a chain effect of churn that leads to a cycle where the churns in value estimation and policy improvement compound and bias the learning dynamics throughout the iteration. Further, we concretize the study and focus on the learning issues caused by the chain effect in different settings, including greedy action deviation in value-based methods, trust region violation in proximal policy optimization, and dual bias of policy value in actor-critic methods. We then propose a method to reduce the chain effect across different settings, called Churn Approximated ReductIoN (CHAIN), which can be easily plugged into most existing DRL algorithms. Our experiments demonstrate the effectiveness of our method in both reducing churn and improving learning performance across online and offline, value-based and policy-based RL settings. | Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn | [
"Hongyao Tang",
"Glen Berseth"
] | NeurIPS.cc/2024/Conference | 2409.04792 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cPzjN7KABv | @inproceedings{
haghifam2024private,
title={Private Geometric Median},
author={Mahdi Haghifam and Thomas Steinke and Jonathan Ullman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cPzjN7KABv}
} | In this paper, we study differentially private (DP) algorithms for computing the geometric median (GM) of a dataset: Given $n$ points, $x_1,\dots,x_n$ in $\mathbb{R}^d$, the goal is to find a point $\theta$ that minimizes the sum of the Euclidean distances to these points, i.e., $\sum_{i=1}^{n} \lVert|\theta - x_i\rVert_2$. Off-the-shelf methods, such as DP-GD, require strong a priori knowledge locating the data within a ball of radius $R$, and the excess risk of the algorithm depends linearly on $R$. In this paper, we ask: can we design an efficient and private algorithm with an excess error guarantee that scales with the (unknown) radius containing the majority of the datapoints? Our main contribution is a pair of polynomial-time DP algorithms for the task of private GM with an excess error guarantee that scales with the effective diameter of the datapoints. Additionally, we propose an inefficient algorithm based on the inverse smooth sensitivity mechanism, which satisfies the more restrictive notion of pure DP. We complement our results with a lower bound and demonstrate the optimality of our polynomial-time algorithms in terms of sample complexity. | Private Geometric Median | [
"Mahdi Haghifam",
"Thomas Steinke",
"Jonathan Ullman"
] | NeurIPS.cc/2024/Conference | 2406.07407 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cOw65A9FGf | @inproceedings{
yu2024textguided,
title={Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models},
author={Lu Yu and Haiyang Zhang and Changsheng Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cOw65A9FGf}
} | Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g. CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in text-guided attention. Building upon this observation, we propose a simple yet effective strategy: Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR). This framework incorporates two components: the Attention Refinement module and the Attention-based Model Constraint module. Our goal is to maintain the generalization of the CLIP model and enhance its adversarial robustness: The Attention Refinement module aligns the text-guided attention obtained from the target model via adversarial examples with the text-guided attention acquired from the original model via clean examples. This alignment enhances the model’s robustness. Additionally, the Attention-based Model Constraint module acquires text-guided attention from both the target and original models using clean examples. Its objective is to maintain model performance on clean samples while enhancing overall robustness. The experiments validate that our method yields a 9.58% enhancement in zero-shot robust accuracy over the current state-of-the-art techniques across 16 datasets. Our code is available at https://github.com/zhyblue424/TGA-ZSR. | Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models | [
"Lu Yu",
"Haiyang Zhang",
"Changsheng Xu"
] | NeurIPS.cc/2024/Conference | 2410.21802 | [
"https://github.com/zhyblue424/tga-zsr"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cOuLbPhOT1 | @inproceedings{
ni2024pace,
title={{PACE}: marrying the generalization of {PA}rameter-efficient fine-tuning with Consistency rEgularization},
author={Yao Ni and Shan Zhang and Piotr Koniusz},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cOuLbPhOT1}
} | Parameter-Efficient Fine-Tuning (PEFT) effectively adapts pre-trained transformers to downstream tasks. However, the optimization for tasks performance often comes at the cost of generalizability in fine-tuned models. To address this issue, we theoretically connect smaller weight gradient norms during training and larger datasets to the improved model generalization. Motivated by this connection, we propose reducing gradient norms for enhanced generalization and aligning fine-tuned model with the pre-trained counterpart to retain knowledge from large-scale pre-training data. Yet, naive alignment does not guarantee gradient reduction and can potentially cause gradient explosion, complicating efforts to manage gradients. To address such issues, we propose PACE, marrying generalization of PArameter-efficient fine-tuning with Consistency rEgularization. We perturb features learned from the adapter with the multiplicative noise and ensure the fine-tuned model remains consistent for same sample under different perturbations. Theoretical analysis shows that PACE not only implicitly regularizes gradients for enhanced generalization, but also implicitly aligns the fine-tuned and pre-trained models to retain knowledge. Experimental evidence supports our theories. PACE surpasses existing PEFT methods in visual adaptation tasks (VTAB-1k, FGVC, few-shot learning, domain adaptation) and showing potential for resource-efficient fine-tuning. It also improves LoRA in text classification (GLUE) and mathematical reasoning (GSM-8K). | PACE: marrying the generalization of PArameter-efficient fine-tuning with Consistency rEgularization | [
"Yao Ni",
"Shan Zhang",
"Piotr Koniusz"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=cMwSoXLCVi | @inproceedings{
hu2024onetomultiple,
title={One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation},
author={Kai Hu and Jinhao Li and Yuan Zhang and Xiongjun Ye and Xieping Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cMwSoXLCVi}
} | In multi-sequence Magnetic Resonance Imaging (MRI), the accurate segmentation of the kidney and tumor based on traditional supervised methods typically necessitates detailed annotation for each sequence, which is both time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) methods can effectively mitigate inter-domain differences by aligning cross-modal features, thereby reducing the annotation burden. However, most existing UDA methods are limited to one-to-one domain adaptation, which tends to be inefficient and resource-intensive when faced with multi-target domain transfer tasks. To address this challenge, we propose a novel and efficient One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adaptive (PSTUDA) framework for kidney and tumor segmentation in multi-sequence MRI. Specifically, we develop a multi-level style dictionary to explicitly store the style information of each target domain at various stages, which alleviates the burden of a single generator in a multi-target transfer task and enables effective decoupling of content and style. Concurrently, we employ multiple cascading style fusion modules that utilize point-wise instance normalization to progressively recombine content and style features, which enhances cross-modal alignment and structural consistency. Experiments conducted on the private MSKT and public KiTS19 datasets demonstrate the superiority of the proposed PSTUDA over comparative methods in multi-sequence kidney and tumor segmentation. The average Dice Similarity Coefficients are increased by at least 1.8% and 3.9%, respectively. Impressively, our PSTUDA not only significantly reduces the floating-point computation by approximately 72% but also reduces the number of model parameters by about 50%, bringing higher efficiency and feasibility to practical clinical applications. | One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation | [
"Kai Hu",
"Jinhao Li",
"Yuan Zhang",
"Xiongjun Ye",
"Xieping Gao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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