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null | https://openreview.net/forum?id=lWYwZklSvg | @inproceedings{
cheng2024fulldistance,
title={Full-Distance Evasion of Pedestrian Detectors in the Physical World},
author={Zhi Cheng and Zhanhao Hu and Yuqiu Liu and Jianmin Li and Hang Su and Xiaolin Hu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lWYwZklSvg}
} | Many studies have proposed attack methods to generate adversarial patterns for evading pedestrian detection, alarming the computer vision community about the need for more attention to the robustness of detectors. However, adversarial patterns optimized by these methods commonly have limited performance at medium to long distances in the physical world. To overcome this limitation, we identify two main challenges. First, in existing methods, there is commonly an appearance gap between simulated distant adversarial patterns and their physical world counterparts, leading to incorrect optimization. Second, there exists a conflict between adversarial losses at different distances, which causes difficulties in optimization. To overcome these challenges, we introduce a Full Distance Attack (FDA) method. Our physical world experiments demonstrate the effectiveness of our FDA patterns across various detection models like YOLOv5, Deformable-DETR, and Mask RCNN. Codes available at https://github.com/zhicheng2T0/Full-Distance-Attack.git | Full-Distance Evasion of Pedestrian Detectors in the Physical World | [
"Zhi Cheng",
"Zhanhao Hu",
"Yuqiu Liu",
"Jianmin Li",
"Hang Su",
"Xiaolin Hu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lWHe7pmk7C | @inproceedings{
li2024from,
title={From Chaos to Clarity: 3{DGS} in the Dark},
author={Zhihao Li and Yufei Wang and Alex Kot and Bihan Wen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lWHe7pmk7C}
} | Novel view synthesis from raw images provides superior high dynamic range (HDR) information compared to reconstructions from low dynamic range RGB images. However, the inherent noise in unprocessed raw images compromises the accuracy of 3D scene representation. Our study reveals that 3D Gaussian Splatting (3DGS) is particularly susceptible to this noise, leading to numerous elongated Gaussian shapes that overfit the noise, thereby significantly degrading reconstruction quality and reducing inference speed, especially in scenarios with limited views. To address these issues, we introduce a novel self-supervised learning framework designed to reconstruct HDR 3DGS from a limited number of noisy raw images. This framework enhances 3DGS by integrating a noise extractor and employing a noise-robust reconstruction loss that leverages a noise distribution prior. Experimental results show that our method outperforms LDR/HDR 3DGS and previous state-of-the-art (SOTA) self-supervised and supervised pre-trained models in both reconstruction quality and inference speed on the RawNeRF dataset across a broad range of training views. We will release the code upon paper acceptance. | From Chaos to Clarity: 3DGS in the Dark | [
"Zhihao Li",
"Yufei Wang",
"Alex Kot",
"Bihan Wen"
] | NeurIPS.cc/2024/Conference | 2406.08300 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lW2zYQm0ox | @inproceedings{
lotidis2024accelerated,
title={Accelerated Regularized Learning in Finite N-Person Games},
author={Kyriakos Lotidis and Angeliki Giannou and Panayotis Mertikopoulos and Nicholas Bambos},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lW2zYQm0ox}
} | Motivated by the success of Nesterov's accelerated gradient algorithm for convex minimization problems, we examine whether it is possible to achieve similar performance gains in the context of online learning in games.
To that end, we introduce a family of accelerated learning methods, which we call “follow the accelerated leader” (FTXL), and which incorporates the use of momentum within the general framework of regularized learning - and, in particular, the exponential / multiplicative weights algorithm and its variants.
Drawing inspiration and techniques from the continuous-time analysis of Nesterov's algorithm, we show that FTXL converges locally to strict Nash equilibria at a superlinear rate, achieving in this way an exponential speed-up over vanilla regularized learning methods (which, by comparison, converge to strict equilibria at a geometric, linear rate).
Importantly, the FTXL maintains its superlinear convergence rate in a broad range of feedback structures, from deterministic, full information models to stochastic, realization-based ones, and even bandit, payoff-based information, where players are only able to observe their individual realized payoffs. | Accelerated Regularized Learning in Finite N-Person Games | [
"Kyriakos Lotidis",
"Angeliki Giannou",
"Panayotis Mertikopoulos",
"Nicholas Bambos"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lV4kTHTgpJ | @inproceedings{
jang2024model,
title={Model Fusion through Bayesian Optimization in Language Model Fine-Tuning},
author={Chaeyun Jang and Hyungi Lee and Jungtaek Kim and Juho Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lV4kTHTgpJ}
} | Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplicity, fine-tuning entails several troublesome engineering choices, such as selecting hyperparameters and determining checkpoints from an optimization trajectory. To tackle the difficulty of choosing the best model, one effective solution is model fusion, which combines multiple models in a parameter space. However, we observe a large discrepancy between loss and metric landscapes during the fine-tuning of pre-trained language models. Building on this observation, we introduce a novel model fusion technique that optimizes both the desired metric and loss through multi-objective Bayesian optimization. In addition, to effectively select hyperparameters, we establish a two-stage procedure by integrating Bayesian optimization processes into our framework. Experiments across various downstream tasks show considerable performance improvements using our Bayesian optimization-guided method. | Model Fusion through Bayesian Optimization in Language Model Fine-Tuning | [
"Chaeyun Jang",
"Hyungi Lee",
"Jungtaek Kim",
"Juho Lee"
] | NeurIPS.cc/2024/Conference | 2411.06710 | [
"https://github.com/chaeyoon-jang/bomf"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=lV1wGHKd5x | @inproceedings{
paissan2024listenable,
title={Listenable Maps for Zero-Shot Audio Classifiers},
author={Francesco Paissan and Luca Della Libera and Mirco Ravanelli and Cem Subakan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lV1wGHKd5x}
} | Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Zero-Shot Audio Classifiers), which, to the best of our knowledge, is the first decoder-based post-hoc explanation method for explaining the decisions of zero-shot audio classifiers. The proposed method utilizes a novel loss function that aims to closely reproduce the original similarity patterns between text-and-audio pairs in the generated explanations. We provide an extensive evaluation using the Contrastive Language-Audio Pretraining (CLAP) model to showcase that our interpreter remains faithful to the decisions in a zero-shot classification context. Moreover, we qualitatively show that our method produces meaningful explanations that correlate well with different text prompts. | Listenable Maps for Zero-Shot Audio Classifiers | [
"Francesco Paissan",
"Luca Della Libera",
"Mirco Ravanelli",
"Cem Subakan"
] | NeurIPS.cc/2024/Conference | 2405.17615 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lT3oc04mDp | @inproceedings{
liu2024kangaroo,
title={Kangaroo: Lossless Self-Speculative Decoding for Accelerating {LLM}s via Double Early Exiting},
author={Fangcheng Liu and Yehui Tang and Zhenhua Liu and Yunsheng Ni and Duyu Tang and Kai Han and Yunhe Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lT3oc04mDp}
} | Speculative decoding has demonstrated its effectiveness in accelerating the inference of large language models (LLMs) while maintaining an identical sampling distribution. However, the conventional approach of training separate draft model to achieve a satisfactory token acceptance rate can be costly and impractical. In this paper, we propose a novel self-speculative decoding framework \emph{Kangaroo} with \emph{double} early exiting strategy, which leverages the shallow sub-network and the \texttt{LM Head} of the well-trained target LLM to construct a self-drafting model. Then, the self-verification stage only requires computing the remaining layers over the \emph{early-exited} hidden states in parallel. To bridge the representation gap between the sub-network and the full model, we train a lightweight and efficient adapter module on top of the sub-network. One significant challenge that comes with the proposed method is that the inference latency of the self-draft model may no longer be negligible compared to the big model. To boost the token acceptance rate while minimizing the latency of the self-drafting model, we introduce an additional \emph{early exiting} mechanism for both single-sequence and the tree decoding scenarios. Specifically, we dynamically halt the small model's subsequent prediction during the drafting phase once the confidence level for the current step falls below a certain threshold. This approach reduces unnecessary computations and improves overall efficiency. Extensive experiments on multiple benchmarks demonstrate our effectiveness, where Kangaroo achieves walltime speedups up to 2.04$\times$, outperforming Medusa-1 with 88.7\% fewer additional parameters. The code for Kangaroo is available at https://github.com/Equationliu/Kangaroo. | Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting | [
"Fangcheng Liu",
"Yehui Tang",
"Zhenhua Liu",
"Yunsheng Ni",
"Duyu Tang",
"Kai Han",
"Yunhe Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lS9e36lkxG | @inproceedings{
liu2024dr,
title={D2R2: Diffusion-based Representation with Random Distance Matching for Tabular Few-shot Learning},
author={Ruoxue Liu and Linjiajie Fang and Wenjia Wang and Bingyi Jing},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lS9e36lkxG}
} | Tabular data is widely utilized in a wide range of real-world applications. The challenge of few-shot learning with tabular data stands as a crucial problem in both industry and academia, due to the high cost or even impossibility of annotating additional samples. However, the inherent heterogeneity of tabular features, combined with the scarcity of labeled data, presents a significant challenge in tabular few-shot classification. In this paper, we propose a novel approach named Diffusion-based Representation with Random Distance matching (D2R2) for tabular few-shot learning. D2R2 leverages the powerful expression ability of diffusion models to extract essential semantic knowledge crucial for denoising process. This semantic knowledge proves beneficial in few-shot downstream tasks. During the training process of our designed diffusion model, we introduce a random distance matching to preserve distance information in the embeddings, thereby improving effectiveness for classification. During the classification stage, we introduce an instance-wise iterative prototype scheme to improve performance by accommodating the multimodality of embeddings and increasing clustering robustness. Our experiments reveal the significant efficacy of D2R2 across various tabular few-shot learning benchmarks, demonstrating its state-of-the-art performance in this field. | D2R2: Diffusion-based Representation with Random Distance Matching for Tabular Few-shot Learning | [
"Ruoxue Liu",
"Linjiajie Fang",
"Wenjia Wang",
"Bingyi Jing"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lQ45aR8L7D | @inproceedings{
mcilroy-young2024orderindependence,
title={Order-Independence Without Fine Tuning},
author={Reid McIlroy-Young and Katrina Brown and Conlan Olson and Linjun Zhang and Cynthia Dwork},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lQ45aR8L7D}
} | The development of generative language models that can create long and coherent textual outputs via autoregression has lead to a proliferation of uses and a corresponding sweep of analyses as researches work to determine the limitations of this new paradigm. Unlike humans, these '*Large Language Models*' (LLMs) are highly sensitive to small changes in their inputs, leading to unwanted inconsistency in their behavior. One problematic inconsistency when LLMs are used to answer multiple-choice questions or analyze multiple inputs is *order dependency*: the output of an LLM can (and often does) change significantly when sub-sequences are swapped, despite both orderings being semantically identical. In this paper we present , a technique that *guarantees* the output of an LLM will not have order dependence on a specified set of sub-sequences. We show that this method *provably* eliminates order dependency, and that it can be applied to *any* transformer-based LLM to enable text generation that is unaffected by re-orderings. Delving into the implications of our method, we show that, despite our inputs being out of distribution, the impact on expected accuracy is small, where the expectation is over the order of uniformly chosen shuffling of the candidate responses, and usually significantly less in practice. Thus, can be used as a '*dropped-in*' method on fully trained models. Finally, we discuss how our method's success suggests that other strong guarantees can be obtained on LLM performance via modifying the input representations.
Code is available at [github.com/reidmcy/set-based-prompting](https://github.com/reidmcy/set-based-prompting.). | Order-Independence Without Fine Tuning | [
"Reid McIlroy-Young",
"Katrina Brown",
"Conlan Olson",
"Linjun Zhang",
"Cynthia Dwork"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lPTWdyIY4O | @inproceedings{
mataigne2024the,
title={The Selective \$G\$-Bispectrum and its Inversion: Applications to \$G\$-Invariant Networks},
author={Simon Mataigne and Johan Mathe and Sophia Sanborn and Christopher Hillar and Nina Miolane},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lPTWdyIY4O}
} | An important problem in signal processing and deep learning is to achieve *invariance* to nuisance factors not relevant for the task. Since many of these factors are describable as the action of a group $G$ (e.g. rotations, translations, scalings), we want methods to be $G$-invariant. The $G$-Bispectrum extracts every characteristic of a given signal up to group action: for example, the shape of an object in an image, but not its orientation. Consequently, the $G$-Bispectrum has been incorporated into deep neural network architectures as a computational primitive for $G$-invariance\textemdash akin to a pooling mechanism, but with greater selectivity and robustness. However, the computational cost of the $G$-Bispectrum ($\mathcal{O}(|G|^2)$, with $|G|$ the size of the group) has limited its widespread adoption. Here, we show that the $G$-Bispectrum computation contains redundancies that can be reduced into a *selective $G$-Bispectrum* with $\mathcal{O}(|G|)$ complexity. We prove desirable mathematical properties of the selective $G$-Bispectrum and demonstrate how its integration in neural networks enhances accuracy and robustness compared to traditional approaches, while enjoying considerable speeds-up compared to the full $G$-Bispectrum. | The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks | [
"Simon Mataigne",
"Johan Mathe",
"Sophia Sanborn",
"Christopher Hillar",
"Nina Miolane"
] | NeurIPS.cc/2024/Conference | 2407.07655 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lPDxPVS6ix | @inproceedings{
dimitrov2024spear,
title={{SPEAR}: Exact Gradient Inversion of Batches in Federated Learning},
author={Dimitar Iliev Dimitrov and Maximilian Baader and Mark Niklas Mueller and Martin Vechev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lPDxPVS6ix}
} | Federated learning is a framework for collaborative machine learning where clients only share gradient updates and not their private data with a server. However, it was recently shown that gradient inversion attacks can reconstruct this data from the shared gradients. In the important honest-but-curious setting, existing attacks enable exact reconstruction only for batch size of $b=1$, with larger batches permitting only approximate reconstruction. In this work, we propose SPEAR, *the first algorithm reconstructing whole batches with $b >1$ exactly*. SPEAR combines insights into the explicit low-rank structure of gradients with a sampling-based algorithm. Crucially, we leverage ReLU-induced gradient sparsity to precisely filter out large numbers of incorrect samples, making a final reconstruction step tractable. We provide an efficient GPU implementation for fully connected networks and show that it recovers high-dimensional ImageNet inputs in batches of up to $b \lesssim 25$ exactly while scaling to large networks. Finally, we show theoretically that much larger batches can be reconstructed with high probability given exponential time. | SPEAR: Exact Gradient Inversion of Batches in Federated Learning | [
"Dimitar Iliev Dimitrov",
"Maximilian Baader",
"Mark Niklas Mueller",
"Martin Vechev"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lOdBHkqzRH | @inproceedings{
hu2024contextual,
title={Contextual Linear Optimization with Bandit Feedback},
author={Yichun Hu and Nathan Kallus and Xiaojie Mao and Yanchen Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lOdBHkqzRH}
} | Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients and thereby improve average-cost performance. An example is the stochastic shortest path problem with random edge costs (e.g., traffic) and contextual features (e.g., lagged traffic, weather). Existing work on CLO assumes the data has fully observed cost coefficient vectors, but in many applications, we can only see the realized cost of a historical decision, that is, just one projection of the random cost coefficient vector, to which we refer as bandit feedback. We study a class of offline learning algorithms for CLO with bandit feedback, which we term induced empirical risk minimization (IERM), where we fit a predictive model to directly optimize the downstream performance of the policy it induces. We show a fast-rate regret bound for IERM that allows for misspecified model classes and flexible choices of the optimization estimate, and we develop computationally tractable surrogate losses. A byproduct of our theory of independent interest is fast-rate regret bound for IERM with full feedback and misspecified policy class. We compare the performance of different modeling choices numerically using a stochastic shortest path example and provide practical insights from the empirical results. | Contextual Linear Optimization with Bandit Feedback | [
"Yichun Hu",
"Nathan Kallus",
"Xiaojie Mao",
"Yanchen Wu"
] | NeurIPS.cc/2024/Conference | 2405.16564 | [
"https://github.com/CausalML/CLOBandit"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lOV9kSX3Uo | @inproceedings{
ding2024optimizing,
title={Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition},
author={Shihong Ding and Long Yang and Luo Luo and Cong Fang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lOV9kSX3Uo}
} | We study a typical optimization model where the optimization variable is composed of multiple probability distributions. Though the model appears frequently in practice, such as for policy problems, it lacks specific analysis in the general setting. For this optimization problem, we propose a new structural condition/landscape description named generalized quasar-convexity (GQC) beyond the realms of convexity. In contrast to original quasar-convexity \citep{hinder2020near}, GQC allows an individual quasar-convex parameter $\gamma_i$ for each variable block $i$ and the smaller of $\gamma_i$ implies less block-convexity. To minimize the objective function, we consider a generalized oracle termed as the internal function that includes the standard gradient oracle as a special case. We provide optimistic mirror descent (OMD) for multiple distributions and prove that the algorithm can achieve an adaptive $\tilde{\mathcal{O}}((\sum_{i=1}^d1/\gamma_i)\epsilon^{-1})$ iteration complexity to find an $\varepsilon$-suboptimal global solution without pre-known the exact values of $\gamma_i$ when the objective admits ``polynomial-like'' structural. Notably, it achieves iteration complexity that does not explicitly depend on the number of distributions and strictly faster $(\sum_{i=1}^d 1/\gamma_i \text{ v.s. } d\max_{i\in[1:d]} 1/\gamma_i)$ than mirror decent methods. We also extend GQC to the minimax optimization problem proposing the generalized quasar-convexity-concavity (GQCC) condition and a decentralized variant of OMD with regularization. Finally, we show the applications of our algorithmic framework on discounted Markov Decision Processes problem and Markov games, which bring new insights on the landscape analysis of reinforcement learning. | Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition | [
"Shihong Ding",
"Long Yang",
"Luo Luo",
"Cong Fang"
] | NeurIPS.cc/2024/Conference | 2407.14839 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lOMHt16T8R | @inproceedings{
luo2024pace,
title={Pa{CE}: Parsimonious Concept Engineering for Large Language Models},
author={Jinqi Luo and Tianjiao Ding and Kwan Ho Ryan Chan and Darshan Thaker and Aditya Chattopadhyay and Chris Callison-Burch and Rene Vidal},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lOMHt16T8R}
} | Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable output, via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities. | PaCE: Parsimonious Concept Engineering for Large Language Models | [
"Jinqi Luo",
"Tianjiao Ding",
"Kwan Ho Ryan Chan",
"Darshan Thaker",
"Aditya Chattopadhyay",
"Chris Callison-Burch",
"Rene Vidal"
] | NeurIPS.cc/2024/Conference | 2406.04331 | [
"https://github.com/peterljq/parsimonious-concept-engineering"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lNCsyA5uS1 | @inproceedings{
katz2024thought,
title={Thought of Search: Planning with Language Models Through The Lens of Efficiency},
author={Michael Katz and Harsha Kokel and Kavitha Srinivas and Shirin Sohrabi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lNCsyA5uS1}
} | Among the most important properties of algorithms investigated in computer science are soundness, completeness, and complexity. These properties, however, are rarely analyzed for the vast collection of recently proposed methods for planning with large language models. In this work, we alleviate this gap. We analyse these properties of using LLMs for planning and highlight that recent trends abandon both soundness and completeness for the sake of inefficiency. We propose a significantly more efficient approach that can, at the same time, maintain both soundness and completeness. We exemplify on four representative search problems, comparing to the LLM-based solutions from the literature that attempt to solve these problems. We show that by using LLMs to produce the code for the search components we can solve the entire datasets with 100% accuracy with only a few calls to the LLM. In contrast, the compared approaches require hundreds of thousands of calls and achieve significantly lower accuracy. We argue for a responsible use of compute resources; urging research community to investigate sound and complete LLM-based approaches that uphold efficiency. | Thought of Search: Planning with Language Models Through The Lens of Efficiency | [
"Michael Katz",
"Harsha Kokel",
"Kavitha Srinivas",
"Shirin Sohrabi"
] | NeurIPS.cc/2024/Conference | 2404.11833 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lKnl4CLhhS | @inproceedings{
mullins2024efficient,
title={Efficient and Private Marginal Reconstruction with Local Non-Negativity},
author={Brett Mullins and Miguel Fuentes and Yingtai Xiao and Daniel Kifer and Cameron N Musco and Daniel Sheldon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lKnl4CLhhS}
} | Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private algorithms for query release and synthetic data contain steps that reconstruct answers to queries from answers to other queries that have been measured privately. Reconstruction is an important subproblem for such mechanisms to economize the privacy budget, minimize error on reconstructed answers, and allow for scalability to high-dimensional datasets. In this paper, we introduce a principled and efficient postprocessing method ReM (Residuals-to-Marginals) for reconstructing answers to marginal queries. Our method builds on recent work on efficient mechanisms for marginal query release, based on making measurements using a residual query basis that admits efficient pseudoinversion, which is an important primitive used in reconstruction. An extension GReM-LNN (Gaussian Residuals-to-Marginals with Local Non-negativity) reconstructs marginals under Gaussian noise satisfying consistency and non-negativity, which often reduces error on reconstructed answers. We demonstrate the utility of ReM and GReM-LNN by applying them to improve existing private query answering mechanisms. | Efficient and Private Marginal Reconstruction with Local Non-Negativity | [
"Brett Mullins",
"Miguel Fuentes",
"Yingtai Xiao",
"Daniel Kifer",
"Cameron N Musco",
"Daniel Sheldon"
] | NeurIPS.cc/2024/Conference | 2410.01091 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lJuQxkDbDo | @inproceedings{
yang2024disengcd,
title={Disen{GCD}: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis},
author={Shangshang Yang and Mingyang Chen and Ziwen Wang and Xiaoshan Yu and Panpan Zhang and Haiping Ma and Xingyi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lJuQxkDbDo}
} | Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation.
To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively.
Specifically, the latter two graphs are first disentangled from the interaction graph.
Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module; multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations,
which can lead to more effective nad robust student representations learned;
the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules.
Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module.The source code is available at https://github.com/BIMK/Intelligent-Education/tree/main/DisenGCD. | DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis | [
"Shangshang Yang",
"Mingyang Chen",
"Ziwen Wang",
"Xiaoshan Yu",
"Panpan Zhang",
"Haiping Ma",
"Xingyi Zhang"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/bimk/intelligent-education"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lIH6oCdppg | @inproceedings{
wu2024on,
title={On the Role of Attention Masks and LayerNorm in Transformers},
author={Xinyi Wu and Amir Ajorlou and Yifei Wang and Stefanie Jegelka and Ali Jadbabaie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lIH6oCdppg}
} | Self-attention is the key mechanism of transformers, which are the essential building blocks of modern foundation models. Recent studies have shown that pure self-attention suffers from an increasing degree of rank collapse as depth increases, limiting model expressivity and further utilization of model depth. The existing literature on rank collapse, however, has mostly overlooked other critical components in transformers that may alleviate the rank collapse issue. In this paper, we provide a general analysis of rank collapse under self-attention, taking into account the effects of attention masks and layer normalization (LayerNorm). In particular, we find that although pure masked attention still suffers from exponential collapse to a rank one subspace, sparse or local masked attention can provably slow down the collapse rate. In the case of self-attention with LayerNorm, we first show that for certain classes of value matrices, collapse to a rank one subspace still happens exponentially. However, through construction of nontrivial counterexamples, we then establish that with proper choice of value matrices, a general class of sequences may not converge to a rank one subspace, and the self-attention dynamics with LayerNorm can simultaneously possess a rich set of equilibria with any possible rank between one and full. Our result refutes the previous hypothesis that LayerNorm plays no role in the rank collapse of self-attention and suggests that self-attention with LayerNorm constitutes a much more expressive, versatile nonlinear dynamical system than what was originally thought. | On the Role of Attention Masks and LayerNorm in Transformers | [
"Xinyi Wu",
"Amir Ajorlou",
"Yifei Wang",
"Stefanie Jegelka",
"Ali Jadbabaie"
] | NeurIPS.cc/2024/Conference | 2405.18781 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lHcvjsQFQq | @inproceedings{
seo2024mitigating,
title={Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction},
author={Seokin Seo and Byung-Jun Lee and Jongmin Lee and HyeongJoo Hwang and Hongseok Yang and Kee-Eung Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lHcvjsQFQq}
} | We consider offline imitation learning (IL), which aims to train an agent to imitate from the dataset of expert demonstrations without online interaction with the environment. Behavioral Cloning (BC) has been a simple yet effective approach to offline IL, but it is also well-known to be vulnerable to the covariate shift resulting from the mismatch between the state distributions induced by the learned policy and the expert policy. Moreover, as often occurs in practice, when expert datasets are collected from an arbitrary state distribution instead of a stationary one, these shifts become more pronounced, potentially leading to substantial failures in existing IL methods. Specifically, we focus on covariate shift resulting from arbitrary state data distributions, such as biased data collection or incomplete trajectories, rather than shifts induced by changes in dynamics or noisy expert actions. In this paper, to mitigate the effect of the covariate shifts in BC, we propose DrilDICE, which utilizes a distributionally robust BC objective by employing a stationary distribution correction ratio estimation (DICE) to derive a feasible solution. We evaluate the effectiveness of our method through an extensive set of experiments covering diverse covariate shift scenarios. The results demonstrate the efficacy of the proposed approach in improving the robustness against the shifts, outperforming existing offline IL methods in such scenarios. | Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction | [
"Seokin Seo",
"Byung-Jun Lee",
"Jongmin Lee",
"HyeongJoo Hwang",
"Hongseok Yang",
"Kee-Eung Kim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lG1VEQJvUH | @inproceedings{
kiani2024unitary,
title={Unitary Convolutions for Learning on Graphs and Groups},
author={Bobak Kiani and Lukas Fesser and Melanie Weber},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lG1VEQJvUH}
} | Data with geometric structure is ubiquitous in machine learning often arising from fundamental symmetries in a domain, such as permutation-invariance in graphs and translation-invariance in images. Group-convolutional architectures, which encode symmetries as inductive bias, have shown great success in applications, but can suffer from instabilities as their depth increases and often struggle to learn long range dependencies in data. For instance, graph neural networks experience instability due to the convergence of node representations (over-smoothing), which can occur after only a few iterations of message-passing, reducing their effectiveness in downstream tasks. Here, we propose and study unitary group convolutions, which allow for deeper networks that are more stable during training. The main focus of the paper are graph neural networks, where we show that unitary graph convolutions provably avoid over-smoothing. Our experimental results confirm that unitary graph convolutional networks achieve competitive performance on benchmark datasets compared to state-of-the-art graph neural networks. We complement our analysis of the graph domain with the study of general unitary convolutions and analyze their role in enhancing stability in general group convolutional architectures. | Unitary Convolutions for Learning on Graphs and Groups | [
"Bobak Kiani",
"Lukas Fesser",
"Melanie Weber"
] | NeurIPS.cc/2024/Conference | 2410.05499 | [
"https://github.com/Weber-GeoML/Unitary_Convolutions"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=lEUle8S4xQ | @inproceedings{
yang2024sft,
title={S\${\textasciicircum}\{2\}\${FT}: Efficient, Scalable and Generalizable {LLM} Fine-tuning by Structured Sparsity},
author={Xinyu Yang and Jixuan Leng and Geyang Guo and Jiawei Zhao and Ryumei Nakada and Linjun Zhang and Huaxiu Yao and Beidi Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lEUle8S4xQ}
} | Current PEFT methods for LLMs can achieve either high quality, efficient training, or scalable serving, but not all three simultaneously. To address this limitation, we investigate sparse fine-tuning and observe a remarkable improvement in generalization ability.
Utilizing this key insight, we propose a family of Structured Sparse Fine-Tuning (S${^2}$FT) methods for LLMs, which concurrently achieve state-of-the-art fine-tuning performance, training efficiency, and inference scalability. S${^2}$FT accomplishes this by selecting sparsely and computing densely. It selects a few heads and channels in the MHA and FFN modules for each Transformer Block, respectively. Next, it co-permutes weight matrices on both sides of the coupled structures in LLMs to connect the selected components in each layer into a dense submatrix. Finally, \model performs in-place gradient updates on all submatrices. Through theoretical analysis and empirical results, our method prevents overfitting and forgetting, delivers SOTA performance on both commonsense and arithmetic reasoning with 4.6% and 1.3% average improvements compared to LoRA, and outperforms full FT by 11.5% when generalize to various domains after instruction tuning. By integrating our partial back-propagation algorithm, \model saves the fine-tuning memory up to 3$\times$ and improves the latency by 1.5-2.7$\times$ compared to full FT, while delivering an average 10% improvement over LoRA on both metrics. We further demonstrate that S${^2}$FT can be decoupled into adapters, enabling effective fusion, fast switch, and efficient parallelism for serving multiple fine-tuned models. | S^2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity | [
"Xinyu Yang",
"Jixuan Leng",
"Geyang Guo",
"Jiawei Zhao",
"Ryumei Nakada",
"Linjun Zhang",
"Huaxiu Yao",
"Beidi Chen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lDtABI541U | @inproceedings{
su2024quadratic,
title={Quadratic Quantum Variational Monte Carlo},
author={Baiyu Su and qiang liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lDtABI541U}
} | This paper introduces the Quadratic Quantum Variational Monte Carlo (Q$^2$VMC) algorithm, an innovative algorithm in quantum chemistry that significantly enhances the efficiency and accuracy of solving the Schrödinger equation. Inspired by the discretization of imaginary-time Schrödinger evolution, Q$^2$VMC employs a novel quadratic update mechanism that integrates seamlessly with neural network-based ansatzes. Our extensive experiments showcase Q$^2$VMC's superior performance, achieving faster convergence and lower ground state energies in wavefunction optimization across various molecular systems, without additional computational cost. This study not only advances the field of computational quantum chemistry but also highlights the important role of discretized evolution in variational quantum algorithms, offering a scalable and robust framework for future quantum research. | Quadratic Quantum Variational Monte Carlo | [
"Baiyu Su",
"qiang liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lD7ziaMHbf | @inproceedings{
shu2024realtime,
title={Real-time Core-Periphery Guided ViT with Smart Data Layout Selection on Mobile Devices},
author={Zhihao Shu and Xiaowei Yu and Zihao Wu and Wenqi Jia and Yinchen Shi and Miao Yin and Tianming Liu and Dajiang Zhu and Wei Niu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lD7ziaMHbf}
} | Mobile devices have become essential enablers for AI applications, particularly in scenarios that require real-time performance. Vision Transformer (ViT) has become a fundamental cornerstone in this regard due to its high accuracy. Recent efforts have been dedicated to developing various transformer architectures that offer im- proved accuracy while reducing the computational requirements. However, existing research primarily focuses on reducing the theoretical computational complexity through methods such as local attention and model pruning, rather than considering realistic performance on mobile hardware. Although these optimizations reduce computational demands, they either introduce additional overheads related to data transformation (e.g., Reshape and Transpose) or irregular computation/data-access patterns. These result in significant overhead on mobile devices due to their limited bandwidth, which even makes the latency worse than vanilla ViT on mobile. In this paper, we present ECP-ViT, a real-time framework that employs the core-periphery principle inspired by the brain functional networks to guide self-attention in ViTs and enable the deployment of ViT models on smartphones. We identify the main bottleneck in transformer structures caused by data transformation and propose a hardware-friendly core-periphery guided self-attention to decrease computation demands. Additionally, we design the system optimizations for intensive data transformation in pruned models. ECP-ViT, with the proposed algorithm-system co-optimizations, achieves a speedup of 4.6× to 26.9× on mobile GPUs across four datasets: STL-10, CIFAR100, TinyImageNet, and ImageNet. | Real-time Core-Periphery Guided ViT with Smart Data Layout Selection on Mobile Devices | [
"Zhihao Shu",
"Xiaowei Yu",
"Zihao Wu",
"Wenqi Jia",
"Yinchen Shi",
"Miao Yin",
"Tianming Liu",
"Dajiang Zhu",
"Wei Niu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lCj0Rvr4D6 | @inproceedings{
woodruff2024john,
title={John Ellipsoids via Lazy Updates},
author={David Woodruff and Taisuke Yasuda},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lCj0Rvr4D6}
} | We give a faster algorithm for computing an approximate John ellipsoid around $n$ points in $d$ dimensions. The best known prior algorithms are based on repeatedly computing the leverage scores of the points and reweighting them by these scores (Cohen et al., 2019). We show that this algorithm can be substantially sped up by delaying the computation of high accuracy leverage scores by using sampling, and then later computing multiple batches of high accuracy leverage scores via fast rectangular matrix multiplication. We also give low-space streaming algorithms for John ellipsoids using similar ideas. | John Ellipsoids via Lazy Updates | [
"David Woodruff",
"Taisuke Yasuda"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lCiqPxcyC0 | @inproceedings{
liu2024replicable,
title={Replicable Uniformity Testing},
author={Sihan Liu and Christopher Ye},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lCiqPxcyC0}
} | Uniformity testing is arguably one of the most fundamental distribution testing problems. Given sample access to an unknown distribution $\mathbf{p}$ on $[n]$, one must decide if $\mathbf{p}$ is uniform or $\varepsilon$-far from uniform (in total variation distance). A long line of work established that uniformity testing has sample complexity $\Theta(\sqrt{n}\varepsilon^{-2})$. However, when the input distribution is neither uniform nor far from uniform, known algorithms may have highly non-replicable behavior.
Consequently, if these algorithms are applied in scientific studies, they may lead to contradictory results that erode public trust in science.
In this work, we revisit uniformity testing under the framework of algorithmic replicability [STOC '22], requiring the algorithm to be replicable under arbitrary distributions. While replicability typically incurs a $\rho^{-2}$ factor overhead in sample complexity, we obtain a replicable uniformity tester using only $\tilde{O}(\sqrt{n} \varepsilon^{-2} \rho^{-1})$ samples. To our knowledge, this is the first replicable learning algorithm with (nearly) linear dependence on $\rho$.
Lastly, we consider a class of ``symmetric" algorithms [FOCS '00] whose outputs are invariant under relabeling of the domain $[n]$, which includes all existing uniformity testers (including ours). For this natural class of algorithms, we prove a nearly matching sample complexity lower bound for replicable uniformity testing. | Replicable Uniformity Testing | [
"Sihan Liu",
"Christopher Ye"
] | NeurIPS.cc/2024/Conference | 2410.10892 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lBp2cda7sp | @inproceedings{
chen2024rmlr,
title={{RMLR}: Extending Multinomial Logistic Regression into General Geometries},
author={Ziheng Chen and Yue Song and Rui Wang and Xiaojun Wu and Nicu Sebe},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lBp2cda7sp}
} | Riemannian neural networks, which extend deep learning techniques to Riemannian spaces, have gained significant attention in machine learning. To better classify the manifold-valued features, researchers have started extending Euclidean multinomial logistic regression (MLR) into Riemannian manifolds. However, existing approaches suffer from limited applicability due to their strong reliance on specific geometric properties. This paper proposes a framework for designing Riemannian MLR over general geometries, referred to as RMLR. Our framework only requires minimal geometric properties, thus exhibiting broad applicability and enabling its use with a wide range of geometries. Specifically, we showcase our framework on the Symmetric Positive Definite (SPD) manifold and special orthogonal group, i.e., the set of rotation matrices. On the SPD manifold, we develop five families of SPD MLRs under five types of power-deformed metrics. On rotation matrices we propose Lie MLR based on the popular bi-invariant metric. Extensive experiments on different Riemannian backbone networks validate the effectiveness of our framework. | RMLR: Extending Multinomial Logistic Regression into General Geometries | [
"Ziheng Chen",
"Yue Song",
"Rui Wang",
"Xiaojun Wu",
"Nicu Sebe"
] | NeurIPS.cc/2024/Conference | 2409.19433 | [
"https://github.com/gitzh-chen/rmlr"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lBh5kuuY1L | @inproceedings{
yoo2024turbohopp,
title={TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models},
author={Kiwoong Yoo and Owen Oertell and Junhyun Lee and Sanghoon Lee and Jaewoo Kang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lBh5kuuY1L}
} | Navigating the vast chemical space of druggable compounds is a formidable challenge in drug discovery, where generative models are increasingly employed to identify viable candidates. Conditional 3D structure-based drug design (3D-SBDD) models, which take into account complex three-dimensional interactions and molecular geometries, are particularly promising. Scaffold hopping is an efficient strategy that facilitates the identification of similar active compounds by strategically modifying the core structure of molecules, effectively narrowing the wide chemical space and enhancing the discovery of drug-like products. However, the practical application of 3D-SBDD generative models is hampered by their slow processing speeds. To address this bottleneck, we introduce TurboHopp, an accelerated pocket-conditioned 3D scaffold hopping model that merges the strategic effectiveness of traditional scaffold hopping with rapid generation capabilities of consistency models. This synergy not only enhances efficiency but also significantly boosts generation speeds, achieving up to 30 times faster inference speed as well as superior generation quality compared to existing diffusion-based models, establishing TurboHopp as a powerful tool in drug discovery. Supported by faster inference speed, we further optimize our model, using Reinforcement Learning for Consistency Models (RLCM), to output desirable molecules. We demonstrate the broad applicability of TurboHopp across multiple drug discovery scenarios, underscoring its potential in diverse molecular settings. | TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models | [
"Kiwoong Yoo",
"Owen Oertell",
"Junhyun Lee",
"Sanghoon Lee",
"Jaewoo Kang"
] | NeurIPS.cc/2024/Conference | 2410.20660 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lA48H7pW3q | @inproceedings{
song2024quest,
title={{QUEST}: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization},
author={Qi Song and Tianxiang Gong and Shiqi Gao and Haoyi Zhou and Jianxin Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lA48H7pW3q}
} | Multimodal contrastive learning (MCL) has recently demonstrated significant success across various tasks. However, the existing MCL treats all negative samples equally and ignores the potential semantic association with positive samples, which limits the model's ability to achieve fine-grained alignment. In multi-view scenarios, MCL tends to prioritize shared information while neglecting modality-specific unique information across different views, leading to feature suppression and suboptimal performance in downstream tasks. To address these limitations, we propose a novel contrastive framework name *QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization*. In the QUEST framework, we propose quaternion contrastive objectives and orthogonal constraints to extract sufficient unique information. Meanwhile, a shared information-guided penalization is introduced to ensure that shared information does not excessively influence the optimization of unique information. Our method leverages quaternion vector spaces to simultaneously optimize shared and unique information. Experiments on multiple datasets show that our method achieves superior performance in multimodal contrastive learning benchmarks. On public benchmark, our approach achieves state-of-the-art performance, and on synthetic shortcut datasets, we outperform existing baseline methods by an average of 97.95\% on the CLIP model. | QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization | [
"Qi Song",
"Tianxiang Gong",
"Shiqi Gao",
"Haoyi Zhou",
"Jianxin Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=l8XnqbQYBK | @inproceedings{
qi2024toward,
title={Toward Conditional Distribution Calibration in Survival Prediction},
author={Shi-ang Qi and Yakun Yu and Russell Greiner},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l8XnqbQYBK}
} | Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of *conditional calibration* for real-world applications – especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model’s predicted individual survival probability at that instance’s observed time. This method effectively improves the model’s marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method’s practical effectiveness and
versatility in various settings. | Toward Conditional Distribution Calibration in Survival Prediction | [
"Shi-ang Qi",
"Yakun Yu",
"Russell Greiner"
] | NeurIPS.cc/2024/Conference | 2410.20579 | [
"https://github.com/shi-ang/makesurvivalcalibratedagain"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=l6xVqzm72i | @inproceedings{
weng2024mamballie,
title={Mamba{LLIE}: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space},
author={Jiangwei Weng and Zhiqiang Yan and Ying Tai and Jianjun Qian and Jian Yang and Jun Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l6xVqzm72i}
} | Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, the vanilla Retinex theory primarily addresses global illumination degradation and neglects local issues such as noise and blur in dark conditions. Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields. While state space models (SSMs) have shown promise in the long-sequence modeling, they face challenges in combining local invariants and global context in visual data. In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design. We first propose a Local-Enhanced State Space Module (LESSM) that incorporates an augmented local bias within a 2D selective scan mechanism, enhancing the original SSMs by preserving local 2D dependency. Additionally, an Implicit Retinex-aware Selective Kernel module (IRSK) dynamically selects features using spatially-varying operations, adapting to varying inputs through an adaptive kernel selection process. Our Global-then-Local State Space Block (GLSSB) integrates LESSM and IRSK with layer normalization (LN) as its core. This design enables MambaLLIE to achieve comprehensive global long-range modeling and flexible local feature aggregation. Extensive experiments demonstrate that MambaLLIE significantly outperforms state-of-the-art CNN and Transformer-based methods. Our code is available at https://github.com/wengjiangwei/MambaLLIE. | MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space | [
"Jiangwei Weng",
"Zhiqiang Yan",
"Ying Tai",
"Jianjun Qian",
"Jian Yang",
"Jun Li"
] | NeurIPS.cc/2024/Conference | 2405.16105 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=l6iICoILGB | @inproceedings{
tukan2024practical,
title={Practical \$0.385\$-Approximation for Submodular Maximization Subject to a Cardinality Constraint},
author={Murad Tukan and Loay Mualem and Moran Feldman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l6iICoILGB}
} | Non-monotone constrained submodular maximization plays a crucial role in various machine learning applications. However, existing algorithms often struggle with a trade-off between approximation guarantees and practical efficiency. The current state-of-the-art is a recent $0.401$-approximation algorithm, but its computational complexity makes it highly impractical. The best practical algorithms for the problem only guarantee $1/e$-approximation. In this work, we present a novel algorithm for submodular maximization subject to a cardinality constraint that combines a guarantee of $0.385$-approximation with a low and practical query complexity of $O(n+k^2)$. Furthermore, we evaluate our algorithm's performance through extensive machine learning applications, including Movie Recommendation, Image Summarization, and more. These evaluations demonstrate the efficacy of our approach. | Practical 0.385-Approximation for Submodular Maximization Subject to a Cardinality Constraint | [
"Murad Tukan",
"Loay Mualem",
"Moran Feldman"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=l5wEQPcDab | @inproceedings{
schlaginhaufen2024towards,
title={Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning},
author={Andreas Schlaginhaufen and Maryam Kamgarpour},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l5wEQPcDab}
} | Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations, motivated by the idea that the reward, rather than the policy, is the most succinct and transferable description of a task [Ng et al., 2000]. However, the reward corresponding to an optimal policy is not unique, making it unclear if an IRL-learned reward is transferable to new transition laws in the sense that its optimal policy aligns with the optimal policy corresponding to the expert's true reward. Past work has addressed this problem only under the assumption of full access to the expert's policy, guaranteeing transferability when learning from two experts with the same reward but different transition laws that satisfy a specific rank condition [Rolland et al., 2022]. In this work, we show that the conditions developed under full access to the expert's policy cannot guarantee transferability in the more practical scenario where we have access only to demonstrations of the expert. Instead of a binary rank condition, we propose principal angles as a more refined measure of similarity and dissimilarity between transition laws. Based on this, we then establish two key results: 1) a sufficient condition for transferability to any transition laws when learning from at least two experts with sufficiently different transition laws, and 2) a sufficient condition for transferability to local changes in the transition law when learning from a single expert. Furthermore, we also provide a probably approximately correct (PAC) algorithm and an end-to-end analysis for learning transferable rewards from demonstrations of multiple experts. | Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning | [
"Andreas Schlaginhaufen",
"Maryam Kamgarpour"
] | NeurIPS.cc/2024/Conference | 2406.01793 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=l5SbrtvSRS | @inproceedings{
du2024parameter,
title={Parameter Competition Balancing for Model Merging},
author={Guodong DU and Junlin Lee and Jing Li and Runhua Jiang and Yifei Guo and Shuyang Yu and Hanting Liu and Sim Kuan Goh and Ho-Kin Tang and Daojing He and Min Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l5SbrtvSRS}
} | While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct tasks, into a single model. This strategy promotes multitasking capabilities without requiring retraining on the original datasets. However, existing methods fall short in addressing potential conflicts and complex correlations between tasks, especially in parameter-level adjustments, posing a challenge in effectively balancing parameter competition across various tasks. This paper introduces an innovative technique named **PCB-Merging** (Parameter Competition Balancing), a *lightweight* and *training-free* technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging employs intra-balancing to gauge parameter significance within individual tasks and inter-balancing to assess parameter similarities across different tasks. Parameters with low importance scores are dropped, and the remaining ones are rescaled to form the final merged model. We assessed our approach in diverse merging scenarios, including cross-task, cross-domain, and cross-training configurations, as well as out-of-domain generalization. The experimental results reveal that our approach achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and large language models, outperforming existing model merging methods. | Parameter Competition Balancing for Model Merging | [
"Guodong DU",
"Junlin Lee",
"Jing Li",
"Runhua Jiang",
"Yifei Guo",
"Shuyang Yu",
"Hanting Liu",
"Sim Kuan Goh",
"Ho-Kin Tang",
"Daojing He",
"Min Zhang"
] | NeurIPS.cc/2024/Conference | 2410.02396 | [
"https://github.com/duguodong7/pcb-merging"
] | https://huggingface.co/papers/2410.02396 | 2 | 0 | 0 | 11 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=l2yvtrz3On | @inproceedings{
hanneke2024improved,
title={Improved Sample Complexity for Multiclass {PAC} Learning},
author={Steve Hanneke and Shay Moran and Qian Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l2yvtrz3On}
} | We aim to understand the optimal PAC sample complexity in multiclass learning. While finiteness of the Daniely-Shalev-Shwartz (DS) dimension has been shown to characterize the PAC learnability of a concept class [Brukhim, Carmon, Dinur, Moran, and Yehudayoff, 2022], there exist polylog factor gaps in the leading term of the sample complexity. In this paper, we reduce the gap in terms of the dependence on the error parameter to a single log factor and also propose two possible routes towards completely resolving the optimal sample complexity, each based on a key open question we formulate: one concerning list learning with bounded list size, the other concerning a new type of shifting for multiclass concept classes. We prove that a positive answer to either of the two questions would completely resolve the optimal sample complexity up to log factors of the DS dimension. | Improved Sample Complexity for Multiclass PAC Learning | [
"Steve Hanneke",
"Shay Moran",
"Qian Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=l0c1j4QvTq | @inproceedings{
wang2024diffusion,
title={Diffusion Actor-Critic with Entropy Regulator},
author={Yinuo Wang and Likun Wang and Yuxuan Jiang and Wenjun Zou and Tong Liu and Xujie Song and Wenxuan Wang and Liming Xiao and Jiang WU and Jingliang Duan and Shengbo Eben Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l0c1j4QvTq}
} | Reinforcement learning (RL) has proven highly effective in addressing complex decision-making and control tasks. However, in most traditional RL algorithms, the policy is typically parameterized as a diagonal Gaussian distribution with learned mean and variance, which constrains their capability to acquire complex policies. In response to this problem, we propose an online RL algorithm termed diffusion actor-critic with entropy regulator (DACER). This algorithm conceptualizes the reverse process of the diffusion model as a novel policy function and leverages the capability of the diffusion model to fit multimodal distributions, thereby enhancing the representational capacity of the policy. Since the distribution of the diffusion policy lacks an analytical expression, its entropy cannot be determined analytically. To mitigate this, we propose a method to estimate the entropy of the diffusion policy utilizing Gaussian mixture model. Building on the estimated entropy, we can learn a parameter $\alpha$ that modulates the degree of exploration and exploitation. Parameter $\alpha$ will be employed to adaptively regulate the variance of the added noise, which is applied to the action output by the diffusion model. Experimental trials on MuJoCo benchmarks and a multimodal task demonstrate that the DACER algorithm achieves state-of-the-art (SOTA) performance in most MuJoCo control tasks while exhibiting a stronger representational capacity of the diffusion policy. | Diffusion Actor-Critic with Entropy Regulator | [
"Yinuo Wang",
"Likun Wang",
"Yuxuan Jiang",
"Wenjun Zou",
"Tong Liu",
"Xujie Song",
"Wenxuan Wang",
"Liming Xiao",
"Jiang WU",
"Jingliang Duan",
"Shengbo Eben Li"
] | NeurIPS.cc/2024/Conference | 2405.15177 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=l04i6dPMxK | @inproceedings{
pasteris2024bandits,
title={Bandits with Abstention under Expert Advice},
author={Stephen Pasteris and Alberto Rumi and Maximilian Thiessen and Shota Saito and Atsushi Miyauchi and Fabio Vitale and Mark Herbster},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=l04i6dPMxK}
} | We study the classic problem of prediction with expert advice under bandit feedback. Our model assumes that one action, corresponding to the learner's abstention from play, has no reward or loss on every trial. We propose the CBA (Confidence-rated Bandits with Abstentions) algorithm, which exploits this assumption to obtain reward bounds that can significantly improve those of the classical Exp4 algorithm. Our problem can be construed as the aggregation of confidence-rated predictors, with the learner having the option to abstain from play. We are the first to achieve bounds on the expected cumulative reward for general confidence-rated predictors. In the special case of specialists, we achieve a novel reward bound, significantly improving previous bounds of SpecialistExp (treating abstention as another action). We discuss how CBA can be applied to the problem of adversarial contextual bandits with the option of abstaining from selecting any action. We are able to leverage a wide range of inductive biases, outperforming previous approaches both theoretically and in preliminary experimental analysis. Additionally, we achieve a reduction in runtime from quadratic to almost linear in the number of contexts for the specific case of metric space contexts. | Bandits with Abstention under Expert Advice | [
"Stephen Pasteris",
"Alberto Rumi",
"Maximilian Thiessen",
"Shota Saito",
"Atsushi Miyauchi",
"Fabio Vitale",
"Mark Herbster"
] | NeurIPS.cc/2024/Conference | 2402.14585 | [
"https://github.com/albertorumi/contextualbanditswithabstention"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kzJ9P7VPnS | @inproceedings{
zhang2024lpdgs,
title={{LP}-3{DGS}: Learning to Prune 3D Gaussian Splatting},
author={Zhaoliang Zhang and Tianchen Song and Yongjae Lee and Li Yang and Cheng Peng and Rama Chellappa and Deliang Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kzJ9P7VPnS}
} | Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream methodologies for novel view synthesis (NVS) due to its high quality and fast rendering speed. However, as a point-based scene representation, 3DGS potentially generates a large number of Gaussians to fit the scene, leading to high memory usage. Improvements that have been proposed require either an empirical pre-set pruning ratio or importance score threshold to prune the point cloud. Such hyperparameters require multiple rounds of training to optimize and achieve the maximum pruning ratio while maintaining the rendering quality for each scene. In this work, we propose learning-to-prune 3DGS (LP-3DGS), where a trainable binary mask is applied to the importance score to automatically find a favorable pruning ratio. Instead of using the traditional straight-through estimator (STE) method to approximate the binary mask gradient, we redesign the masking function to leverage the Gumbel-Sigmoid method, making it differentiable and compatible with the existing training process of 3DGS. Extensive experiments have shown that LP-3DGS consistently achieves a good balance between efficiency and high quality. | LP-3DGS: Learning to Prune 3D Gaussian Splatting | [
"Zhaoliang Zhang",
"Tianchen Song",
"Yongjae Lee",
"Li Yang",
"Cheng Peng",
"Rama Chellappa",
"Deliang Fan"
] | NeurIPS.cc/2024/Conference | 2405.18784 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kxBsNEWB42 | @inproceedings{
lobanov2024acceleration,
title={Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values},
author={Aleksandr Lobanov and Alexander Gasnikov and Andrey Krasnov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kxBsNEWB42}
} | Frequently, the burgeoning field of black-box optimization encounters challenges due to a limited understanding of the mechanisms of the objective function. To address such problems, in this work we focus on the deterministic concept of Order Oracle, which only utilizes order access between function values (possibly with some bounded noise), but without assuming access to their values. As theoretical results, we propose a new approach to create non-accelerated optimization algorithms (obtained by integrating Order Oracle into existing optimization “tools”) in non-convex, convex, and strongly convex settings that are as good as both SOTA coordinate algorithms with first-order oracle and SOTA algorithms with Order Oracle up to logarithm factor. Moreover, using the proposed approach, _we provide the first accelerated optimization algorithm using the Order Oracle_. And also, using an already different approach we provide the asymptotic convergence of _the first algorithm with the stochastic Order Oracle concept_. Finally, our theoretical results demonstrate effectiveness of proposed algorithms through numerical experiments. | Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values | [
"Aleksandr Lobanov",
"Alexander Gasnikov",
"Andrey Krasnov"
] | NeurIPS.cc/2024/Conference | 2402.09014 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kuCY0mW4Q3 | @inproceedings{
li2024vblora,
title={{VB}-Lo{RA}: Extreme Parameter Efficient Fine-Tuning with Vector Banks},
author={Yang Li and Shaobo Han and Shihao Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kuCY0mW4Q3}
} | As the adoption of large language models increases and the need for per-user or per-task model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and its variants, incur substantial storage and transmission costs. To further reduce stored parameters, we introduce a "divide-and-share" paradigm that breaks the barriers of low-rank decomposition across matrix dimensions, modules, and layers by sharing parameters globally via a vector bank. As an instantiation of the paradigm to LoRA, our proposed VB-LoRA composites all the low-rank matrices of LoRA from a shared vector bank with a differentiable top-$k$ admixture module. VB-LoRA achieves extreme parameter efficiency while maintaining comparable or better performance compared to state-of-the-art PEFT methods. Extensive experiments demonstrate the effectiveness of VB-LoRA on natural language understanding, natural language generation, instruction tuning, and mathematical reasoning tasks. When fine-tuning the Llama2-13B model, VB-LoRA only uses 0.4% of LoRA's stored parameters, yet achieves superior results. Our source code is available at https://github.com/leo-yangli/VB-LoRA. This method has been merged into the Hugging Face PEFT package. | VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks | [
"Yang Li",
"Shaobo Han",
"Shihao Ji"
] | NeurIPS.cc/2024/Conference | 2405.15179 | [
"https://github.com/leo-yangli/vb-lora"
] | https://huggingface.co/papers/2405.15179 | 0 | 1 | 1 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=ktpG37Dzh5 | @inproceedings{
wright2024bmrs,
title={{BMRS}: Bayesian Model Reduction for Structured Pruning},
author={Dustin Wright and Christian Igel and Raghavendra Selvan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ktpG37Dzh5}
} | Modern neural networks are often massively overparameterized leading to high compute costs during training and at inference. One effective method to improve both the compute and energy efficiency of neural networks while maintaining good performance is structured pruning, where full network structures (e.g. neurons or convolutional filters) that have limited impact on the model output are removed. In this work, we propose Bayesian Model Reduction for Structured pruning (BMRS), a fully end-to-end Bayesian method of structured pruning. BMRS is based on two recent methods: Bayesian structured pruning with multiplicative noise, and Bayesian model reduction (BMR), a method which allows efficient comparison of Bayesian models under a change in prior. We present two realizations of BMRS derived from different priors which yield different structured pruning characteristics: 1) BMRS_N with the truncated log-normal prior, which offers reliable compression rates and accuracy without the need for tuning any thresholds and 2) BMRS_U with the truncated log-uniform prior that can achieve more aggressive compression based on the boundaries of truncation. Overall, we find that BMRS offers a theoretically grounded approach to structured pruning of neural networks yielding both high compression rates and accuracy. Experiments on multiple datasets and neural networks of varying complexity showed that the two BMRS methods offer a competitive performance-efficiency trade-off compared to other pruning methods. | BMRS: Bayesian Model Reduction for Structured Pruning | [
"Dustin Wright",
"Christian Igel",
"Raghavendra Selvan"
] | NeurIPS.cc/2024/Conference | 2406.01345 | [
"https://github.com/saintslab/bmrs-structured-pruning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=kr7eN85mIT | @inproceedings{
liu2024tell,
title={Tell What You Hear From What You See - Video to Audio Generation Through Text},
author={Xiulong Liu and Kun Su and Eli Shlizerman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kr7eN85mIT}
} | The content of visual and audio scenes is multi-faceted such that a video stream can
be paired with various audio streams and vice-versa. Thereby, in video-to-audio
generation task, it is imperative to introduce steering approaches for controlling the
generated audio. While Video-to-Audio generation is a well-established generative
task, existing methods lack such controllability. In this work, we propose VATT, a
multi-modal generative framework that takes a video and an optional text prompt
as input, and generates audio and optional textual description (caption) of the
audio. Such a framework has two unique advantages: i) Video-to-Audio generation
process can be refined and controlled via text which complements the context
of the visual information, and ii) The model can suggest what audio to generate
for the video by generating audio captions. VATT consists of two key modules:
VATT Converter, which is an LLM that has been fine-tuned for instructions and
includes a projection layer that maps video features to the LLM vector space, and
VATT Audio, a bi-directional transformer that generates audio tokens from visual
frames and from optional text prompt using iterative parallel decoding. The audio
tokens and the text prompt are used by a pretrained neural codec to convert them
into a waveform. Our experiments show that when VATT is compared to existing
video-to-audio generation methods in objective metrics, such as VGGSound audiovisual dataset, it achieves competitive performance when the audio caption is
not provided. When the audio caption is provided as a prompt, VATT achieves
even more refined performance (with lowest KLD score of 1.41). Furthermore,
subjective studies asking participants to choose the most compatible generated
audio for a given silent video, show that VATT Audio has been chosen on average
as a preferred generated audio than the audio generated by existing methods. VATT
enables controllable video-to-audio generation through text as well as suggesting
text prompts for videos through audio captions, unlocking novel applications such
as text-guided video-to-audio generation and video-to-audio captioning. | Tell What You Hear From What You See - Video to Audio Generation Through Text | [
"Xiulong Liu",
"Kun Su",
"Eli Shlizerman"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kqmucDKVcU | @inproceedings{
kornilov2024optimal,
title={Optimal Flow Matching: Learning Straight Trajectories in Just One Step},
author={Nikita Maksimovich Kornilov and Petr Mokrov and Alexander Gasnikov and Alexander Korotin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kqmucDKVcU}
} | Over the several recent years, there has been a boom in development of Flow Matching (FM) methods for generative modeling. One intriguing property pursued by the community is the ability to learn flows with straight trajectories which realize the Optimal Transport (OT) displacements. Straightness is crucial for the fast integration (inference) of the learned flow's paths. Unfortunately, most existing flow straightening methods are based on non-trivial iterative FM procedures which accumulate the error during training or exploit heuristics based on minibatch OT. To address these issues, we develop and theoretically justify the novel Optimal Flow Matching approach which allows recovering the straight OT displacement for the quadratic transport in just one FM step. The main idea of our approach is the employment of vector field for FM which are parameterized by convex functions. The code of our OFM implementation and the conducted experiments is available at https://github.com/Jhomanik/Optimal-Flow-Matching | Optimal Flow Matching: Learning Straight Trajectories in Just One Step | [
"Nikita Maksimovich Kornilov",
"Petr Mokrov",
"Alexander Gasnikov",
"Alexander Korotin"
] | NeurIPS.cc/2024/Conference | 2403.13117 | [
"https://github.com/jhomanik/optimal-flow-matching"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kq166jACVP | @inproceedings{
ji2024aligner,
title={Aligner: Efficient Alignment by Learning to Correct},
author={Jiaming Ji and Boyuan Chen and Hantao Lou and Donghai Hong and Borong Zhang and Xuehai Pan and Tianyi Qiu and Juntao Dai and Yaodong Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kq166jACVP}
} | With the rapid development of large language models (LLMs) and ever-evolving practical requirements, finding an efficient and effective alignment method has never been more critical. However, the tension between the complexity of current alignment methods and the need for rapid iteration in deployment scenarios necessitates the development of a model-agnostic alignment approach that can operate under these constraints. In this paper, we introduce Aligner, a novel and simple alignment paradigm that learns the correctional residuals between preferred and dispreferred answers using a small model. Designed as a model-agnostic, plug-and-play module, Aligner can be directly applied to various open-source and API-based models with only one-off training, making it suitable for rapid iteration. Notably, Aligner can be applied to any powerful, large-scale upstream models. Moreover, it can even iteratively bootstrap the upstream models using corrected responses as synthetic human preference data, breaking through the model's performance ceiling. Our experiments demonstrate performance improvements by deploying the same Aligner model across 11 different LLMs, evaluated on the 3H dimensions (helpfulness, harmlessness, and honesty). Specifically, Aligner-7B has achieved an average improvement of 68.9\% in helpfulness and 23.8\% in harmlessness across the tested LLMs while also effectively reducing hallucination. In the Alpaca-Eval leaderboard, stacking Aligner-2B on GPT-4 Turbo improved its LC Win Rate from 55.0\% to 58.3\%, surpassing GPT-4 Omni's 57.5\% Win Rate (community report). | Aligner: Efficient Alignment by Learning to Correct | [
"Jiaming Ji",
"Boyuan Chen",
"Hantao Lou",
"Donghai Hong",
"Borong Zhang",
"Xuehai Pan",
"Tianyi Qiu",
"Juntao Dai",
"Yaodong Yang"
] | NeurIPS.cc/2024/Conference | 2402.02416 | [
""
] | https://huggingface.co/papers/2402.02416 | 0 | 4 | 0 | 8 | [
"clinic-research/Aligner-Med"
] | [] | [] | [
"clinic-research/Aligner-Med"
] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=kpo6ZCgVZH | @inproceedings{
zhang2024functional,
title={Functional Gradient Flows for Constrained Sampling},
author={Shiyue Zhang and Longlin Yu and Ziheng Cheng and Cheng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kpo6ZCgVZH}
} | Recently, through a unified gradient flow perspective of Markov chain Monte Carlo (MCMC) and variational inference (VI), particle-based variational inference methods (ParVIs) have been proposed that tend to combine the best of both worlds. While typical ParVIs such as Stein Variational Gradient Descent (SVGD) approximate the gradient flow within a reproducing kernel Hilbert space (RKHS), many attempts have been made recently to replace RKHS with more expressive function spaces, such as neural networks. While successful, these methods are mainly designed for sampling from unconstrained domains. In this paper, we offer a general solution to constrained sampling by introducing a boundary condition for the gradient flow which would confine the particles within the specific domain. This allows us to propose a new functional gradient ParVI method for constrained sampling, called *constrained functional gradient flow* (CFG), with provable continuous-time convergence in total variation (TV). We also present novel numerical strategies to handle the boundary integral term arising from the domain constraints. Our theory and experiments demonstrate the effectiveness of the proposed framework. | Functional Gradient Flows for Constrained Sampling | [
"Shiyue Zhang",
"Longlin Yu",
"Ziheng Cheng",
"Cheng Zhang"
] | NeurIPS.cc/2024/Conference | 2410.23170 | [
"https://github.com/ShiyueZhang66/Constrained-Functional-Gradient-Flow"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kngLs5H6l1 | @inproceedings{
wei2024normalgs,
title={Normal-{GS}: 3D Gaussian Splatting with Normal-Involved Rendering},
author={Meng Wei and Qianyi Wu and Jianmin Zheng and Hamid Rezatofighi and Jianfei Cai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kngLs5H6l1}
} | Rendering and reconstruction are long-standing topics in computer vision and graphics. Achieving both high rendering quality and accurate geometry is a challenge. Recent advancements in 3D Gaussian Splatting (3DGS) have enabled high-fidelity novel view synthesis at real-time speeds. However, the noisy and discrete nature of 3D Gaussian primitives hinders accurate surface estimation. Previous attempts to regularize 3D Gaussian normals often degrade rendering quality due to the fundamental disconnect between normal vectors and the rendering pipeline in 3DGS-based methods. Therefore, we introduce Normal-GS, a novel approach that integrates normal vectors into the 3DGS rendering pipeline. The core idea is to model the interaction between normals and incident lighting using the physically-based rendering equation. Our approach re-parameterizes surface colors as the product of normals and a designed Integrated Directional Illumination Vector (IDIV). To optimize memory usage and simplify optimization, we employ an anchor-based 3DGS to implicitly encode locally-shared IDIVs. Additionally, Normal-GS leverages optimized normals and Integrated Directional Encoding (IDE) to accurately model specular effects, enhancing both rendering quality and surface normal precision. Extensive experiments demonstrate that Normal-GS achieves near state-of-the-art visual quality while obtaining accurate surface normals and preserving real-time rendering performance. | Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering | [
"Meng Wei",
"Qianyi Wu",
"Jianmin Zheng",
"Hamid Rezatofighi",
"Jianfei Cai"
] | NeurIPS.cc/2024/Conference | 2410.20593 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=klsyhjLlX5 | @inproceedings{
deng2024groupwise,
title={Group-wise oracle-efficient algorithms for online multi-group learning},
author={Samuel Deng and Jingwen Liu and Daniel Hsu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=klsyhjLlX5}
} | We study the problem of online multi-group learning, a learning model in which an online learner must simultaneously achieve small prediction regret on a large collection of (possibly overlapping) subsequences corresponding to a family of groups. Groups are subsets of the context space, and in fairness applications, they may correspond to subpopulations defined by expressive functions of demographic attributes. In this paper, we design such oracle-efficient algorithms with sublinear regret under a variety of settings, including: (i) the i.i.d. setting, (ii) the adversarial setting with smoothed context distributions, and (iii) the adversarial transductive setting. | Group-wise oracle-efficient algorithms for online multi-group learning | [
"Samuel Deng",
"Jingwen Liu",
"Daniel Hsu"
] | NeurIPS.cc/2024/Conference | 2406.05287 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=klqhrq7fvB | @inproceedings{
sypetkowski2024on,
title={On the Scalability of {GNN}s for Molecular Graphs},
author={Maciej Sypetkowski and Frederik Wenkel and Farimah Poursafaei and Nia Dickson and Karush Suri and Philip Fradkin and Dominique Beaini},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=klqhrq7fvB}
} | Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures. We address this drawback of GNNs by studying their scaling behavior. Specifically, we analyze message-passing networks, graph Transformers, and hybrid architectures on the largest public collection of 2D molecular graphs for supervised pretraining. For the first time, we observe that GNNs benefit tremendously from the increasing scale of depth, width, number of molecules and associated labels. A major factor is the diversity of the pretraining data that comprises thousands of labels per molecule derived from bio-assays, quantum simulations, transcriptomics and phenomic imaging. We further demonstrate strong finetuning scaling behavior on 38 highly competitive downstream tasks, outclassing previous large models. This gives rise to MolGPS, a new graph foundation model that allows to navigate the chemical space, outperforming the previous state-of-the-arts on 26 out the 38 downstream tasks. We hope that our work paves the way for an era where foundational GNNs drive pharmaceutical drug discovery. | On the Scalability of GNNs for Molecular Graphs | [
"Maciej Sypetkowski",
"Frederik Wenkel",
"Farimah Poursafaei",
"Nia Dickson",
"Karush Suri",
"Philip Fradkin",
"Dominique Beaini"
] | NeurIPS.cc/2024/Conference | 2404.11568 | [
""
] | https://huggingface.co/papers/2404.11568 | 0 | 1 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kkmPe0rzY1 | @inproceedings{
feldman2024robust,
title={Robust Conformal Prediction Using Privileged Information},
author={Shai Feldman and Yaniv Romano},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kkmPe0rzY1}
} | We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables.
Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption. Importantly, naively applying conformal prediction does not provide reliable predictions in this setting, due to the distribution shift induced by the corruptions.
To account for the distribution shift, we assume access to privileged information (PI). The PI is formulated as additional features that explain the distribution shift, however, they are only available during training and absent at test time.
We approach this problem by introducing a novel generalization of weighted conformal prediction and support our method with theoretical coverage guarantees.
Empirical experiments on both real and synthetic datasets indicate that our approach achieves a valid coverage rate and constructs more informative predictions compared to existing methods, which are not supported by theoretical guarantees. | Robust Conformal Prediction Using Privileged Information | [
"Shai Feldman",
"Yaniv Romano"
] | NeurIPS.cc/2024/Conference | 2406.05405 | [
"https://github.com/Shai128/pcp"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kk0Eaunc58 | @inproceedings{
haberer2024hydravit,
title={HydraViT: Stacking Heads for a Scalable ViT},
author={Janek Haberer and Ali Hojjat and Olaf Landsiedel},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kk0Eaunc58}
} | The architecture of Vision Transformers (ViTs), particularly the Multi-head Attention (MHA) mechanism, imposes substantial hardware demands. Deploying ViTs on devices with varying constraints, such as mobile phones, requires multiple models of different sizes. However, this approach has limitations, such as training and storing each required model separately. This paper introduces HydraViT, a novel approach that addresses these limitations by stacking attention heads to achieve a scalable ViT. By repeatedly changing the size of the embedded dimensions throughout each layer and their corresponding number of attention heads in MHA during training, HydraViT induces multiple subnetworks. Thereby, HydraViT achieves adaptability across a wide spectrum of hardware environments while maintaining performance. Our experimental results demonstrate the efficacy of HydraViT in achieving a scalable ViT with up to 10 subnetworks, covering a wide range of resource constraints. HydraViT achieves up to 5 p.p. more accuracy with the same GMACs and up to 7 p.p. more accuracy with the same throughput on ImageNet-1K compared to the baselines, making it an effective solution for scenarios where hardware availability is diverse or varies over time. The source code is available at https://github.com/ds-kiel/HydraViT. | HydraViT: Stacking Heads for a Scalable ViT | [
"Janek Haberer",
"Ali Hojjat",
"Olaf Landsiedel"
] | NeurIPS.cc/2024/Conference | 2409.17978 | [
"https://github.com/ds-kiel/hydravit"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kfdEXQu6MC | @inproceedings{
eilers2024a,
title={A generalized neural tangent kernel for surrogate gradient learning},
author={Luke Eilers and Raoul-Martin Memmesheimer and Sven Goedeke},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kfdEXQu6MC}
} | State-of-the-art neural network training methods depend on the gradient of the network function. Therefore, they cannot be applied to networks whose activation functions do not have useful derivatives, such as binary and discrete-time spiking neural networks. To overcome this problem, the activation function's derivative is commonly substituted with a surrogate derivative, giving rise to surrogate gradient learning (SGL). This method works well in practice but lacks theoretical foundation.
The neural tangent kernel (NTK) has proven successful in the analysis of gradient descent. Here, we provide a generalization of the NTK, which we call the surrogate gradient NTK, that enables the analysis of SGL. First, we study a naive extension of the NTK to activation functions with jumps, demonstrating that gradient descent for such activation functions is also ill-posed in the infinite-width limit. To address this problem, we generalize the NTK to gradient descent with surrogate derivatives, i.e., SGL. We carefully define this generalization and expand the existing key theorems on the NTK with mathematical rigor. Further, we illustrate our findings with numerical experiments. Finally, we numerically compare SGL in networks with sign activation function and finite width to kernel regression with the surrogate gradient NTK; the results confirm that the surrogate gradient NTK provides a good characterization of SGL. | A generalized neural tangent kernel for surrogate gradient learning | [
"Luke Eilers",
"Raoul-Martin Memmesheimer",
"Sven Goedeke"
] | NeurIPS.cc/2024/Conference | 2405.15539 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=kf80ZS3fVy | @inproceedings{
pan2024towards,
title={Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration},
author={Kaihang Pan and Zhaoyu Fan and Juncheng Li and Qifan Yu and Hao Fei and Siliang Tang and Richang Hong and Hanwang Zhang and Qianru Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kf80ZS3fVy}
} | The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose \textbf{UniKE}, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic levels. Within such a unified framework, we further promote knowledge collaboration by disentangling the knowledge representations into the semantic and truthfulness spaces. Extensive experiments validate the effectiveness of our method, which ensures that the post-edit MLLM simultaneously maintains excellent reliability, generality, and locality. The code for UniKE is available at https://github.com/beepkh/UniKE. | Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration | [
"Kaihang Pan",
"Zhaoyu Fan",
"Juncheng Li",
"Qifan Yu",
"Hao Fei",
"Siliang Tang",
"Richang Hong",
"Hanwang Zhang",
"Qianru Sun"
] | NeurIPS.cc/2024/Conference | 2409.19872 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=kcQKIzQPZj | @inproceedings{
cui2024localize,
title={Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner},
author={Xing Cui and Pei Pei Li and Zekun Li and Xuannan Liu and Yueying Zou and Zhaofeng He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kcQKIzQPZj}
} | Flexible and accurate drag-based editing is a challenging task that has recently garnered significant attention. Current methods typically model this problem as automatically learning "how to drag" through point dragging and often produce one deterministic estimation, which presents two key limitations: 1) Overlooking the inherently ill-posed nature of drag-based editing, where multiple results may correspond to a given input, as illustrated in Fig.1; 2) Ignoring the constraint of image quality, which may lead to unexpected distortion.
To alleviate this, we propose LucidDrag, which shifts the focus from "how to drag" to "what-then-how" paradigm. LucidDrag comprises an intention reasoner and a collaborative guidance sampling mechanism. The former infers several optimal editing strategies, identifying what content and what semantic direction to be edited. Based on the former, the latter addresses "how to drag" by collaboratively integrating existing editing guidance with the newly proposed semantic guidance and quality guidance.
Specifically, semantic guidance is derived by establishing a semantic editing direction based on reasoned intentions, while quality guidance is achieved through classifier guidance using an image fidelity discriminator.
Both qualitative and quantitative comparisons demonstrate the superiority of LucidDrag over previous methods. | Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner | [
"Xing Cui",
"Pei Pei Li",
"Zekun Li",
"Xuannan Liu",
"Yueying Zou",
"Zhaofeng He"
] | NeurIPS.cc/2024/Conference | 2406.00432 | [
"https://github.com/cuixing100876/luciddrag-neurips2024"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kbBjVMcJ7G | @inproceedings{
novelli2024operator,
title={Operator World Models for Reinforcement Learning},
author={Pietro Novelli and Marco Prattic{\`o} and massimiliano pontil and Carlo Ciliberto},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kbBjVMcJ7G}
} | Policy Mirror Descent (PMD) is a powerful and theoretically sound methodology for sequential decision-making. However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions. We address this challenge by introducing a novel approach based on learning a world model of the environment using conditional mean embeddings. Leveraging tools from operator theory we derive a closed-form expression of the action-value function in terms of the world model via simple matrix operations. Combining these estimators with PMD leads to POWR, a new RL algorithm for which we prove convergence rates to the global optimum. Preliminary experiments in finite and infinite state settings support the effectiveness of our method. | Operator World Models for Reinforcement Learning | [
"Pietro Novelli",
"Marco Pratticò",
"massimiliano pontil",
"Carlo Ciliberto"
] | NeurIPS.cc/2024/Conference | 2406.19861 | [
"https://github.com/csml-iit-ucl/powr"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kamAXSJxGV | @inproceedings{
kazan2024prioritizing,
title={Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy},
author={Zeki Kazan and Jerome Reiter},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kamAXSJxGV}
} | When releasing outputs from confidential data, agencies need to balance the analytical usefulness of the released data with the obligation to protect data subjects' confidentiality. For releases satisfying differential privacy, this balance is reflected by the privacy budget, $\varepsilon$. We provide a framework for setting $\varepsilon$ based on its relationship with Bayesian posterior probabilities of disclosure. The agency responsible for the data release decides how much posterior risk it is willing to accept at various levels of prior risk, which implies a unique $\varepsilon$. Agencies can evaluate different risk profiles to determine one that leads to an acceptable trade-off in risk and utility. | Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy | [
"Zeki Kazan",
"Jerome Reiter"
] | NeurIPS.cc/2024/Conference | 2306.13214 | [
"https://github.com/zekicankazan/choosing_dp_epsilon"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kZpNDbZrzy | @inproceedings{
lee2024gta,
title={{GTA}: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning},
author={Jaewoo Lee and Sujin Yun and Taeyoung Yun and Jinkyoo Park},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kZpNDbZrzy}
} | Offline Reinforcement Learning (Offline RL) presents challenges of learning effective decision-making policies from static datasets without any online interactions. Data augmentation techniques, such as noise injection and data synthesizing, aim to improve Q-function approximation by smoothing the learned state-action region. However, these methods often fall short of directly improving the quality of offline datasets, leading to suboptimal results. In response, we introduce GTA, Generative Trajectory Augmentation, a novel generative data augmentation approach designed to enrich offline data by augmenting trajectories to be both high-rewarding and dynamically plausible. GTA applies a diffusion model within the data augmentation framework. GTA partially noises original trajectories and then denoises them with classifier-free guidance via conditioning on amplified return value. Our results show that GTA, as a general data augmentation strategy, enhances the performance of widely used offline RL algorithms across various tasks with unique challenges. Furthermore, we conduct a quality analysis of data augmented by GTA and demonstrate that GTA improves the quality of the data. Our code is available at https://github.com/Jaewoopudding/GTA | GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning | [
"Jaewoo Lee",
"Sujin Yun",
"Taeyoung Yun",
"Jinkyoo Park"
] | NeurIPS.cc/2024/Conference | 2405.16907 | [
"https://github.com/jaewoopudding/gta"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kZfxICBXd1 | @inproceedings{
chen2024multiwinner,
title={Multi-Winner Reconfiguration},
author={Jiehua Chen and Christian Hatschka and Sofia Simola},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kZfxICBXd1}
} | We introduce a multi-winner reconfiguration model to examine how to transition between subsets of alternatives (aka. committees) through a sequence of minor yet impactful modifications, called reconfiguration path. We analyze this model under four approval-based voting rules: Chamberlin-Courant (CC), Proportional Approval Voting (PAV), Approval Voting (AV), and Satisfaction Approval Voting (SAV). The problem exhibits computational intractability for CC and PAV, and polynomial solvability for AV and SAV. We provide a detailed multivariate complexity analysis for CC and PAV, demonstrating that although the problem remains challenging in many scenarios, there are specific cases that allow for efficient parameterized algorithms. | Multi-Winner Reconfiguration | [
"Jiehua Chen",
"Christian Hatschka",
"Sofia Simola"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kZ4Kc5GhGB | @inproceedings{
feng2024rethinking,
title={Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity},
author={Guhao Feng and Han Zhong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kZ4Kc5GhGB}
} | Reinforcement Learning (RL) encompasses diverse paradigms, including model-based RL, policy-based RL, and value-based RL, each tailored to approximate the model, optimal policy, and optimal value function, respectively. This work investigates the potential hierarchy of representation complexity among these RL paradigms. By utilizing computational complexity measures, including time complexity and circuit complexity, we theoretically unveil a potential representation complexity hierarchy within RL. We find that representing the model emerges as the easiest task, followed by the optimal policy, while representing the optimal value function presents the most intricate challenge. Additionally, we reaffirm this hierarchy from the perspective of the expressiveness of Multi-Layer Perceptrons (MLPs), which align more closely with practical deep RL and contribute to a completely new perspective in theoretical studying representation complexity in RL. Finally, we conduct deep RL experiments to validate our theoretical findings. | Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity | [
"Guhao Feng",
"Han Zhong"
] | NeurIPS.cc/2024/Conference | 2312.17248 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kYio3xH6eb | @inproceedings{
zhang2024mitigating,
title={Mitigating Reward Overoptimization via Lightweight Uncertainty Estimation},
author={Xiaoying Zhang and Jean-Francois Ton and Wei Shen and Hongning Wang and Yang Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kYio3xH6eb}
} | Reinforcement Learning from Human Feedback (RLHF) has been pivotal in aligning Large Language Models with human values but often suffers from overoptimization due to its reliance on a proxy reward model. To mitigate this limitation, we first propose a lightweight uncertainty quantification method that assesses the reliability of the proxy reward using only the last layer embeddings of the reward model. Enabled by this efficient uncertainty quantification method, we formulate AdvPO, a distributionally robust optimization procedure to tackle the reward overoptimization problem in RLHF. Through extensive experiments on the Anthropic HH and TL;DR summarization datasets, we verify the effectiveness of AdvPO in mitigating the overoptimization problem, resulting in enhanced RLHF performance as evaluated through human-assisted evaluation. | Mitigating Reward Overoptimization via Lightweight Uncertainty Estimation | [
"Xiaoying Zhang",
"Jean-Francois Ton",
"Wei Shen",
"Hongning Wang",
"Yang Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kXKrLsR4aJ | @inproceedings{
st{\"o}lzle2024inputtostate,
title={Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space},
author={Maximilian St{\"o}lzle and Cosimo Della Santina},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kXKrLsR4aJ}
} | Even though a variety of methods have been proposed in the literature, efficient and effective latent-space control (i.e., control in a learned low-dimensional space) of physical systems remains an open challenge.
We argue that a promising avenue is to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics, such as potential-energy shaping.
We identify three fundamental shortcomings in existing latent-space models that have so far prevented this powerful combination: (i) they lack the mathematical structure of a physical system, (ii) they do not inherently conserve the stability properties of the real systems, (iii) these methods do not have an invertible mapping between input and latent-space forcing.
This work proposes a novel Coupled Oscillator Network (CON) model that simultaneously tackles all these issues.
More specifically, (i) we show analytically that CON is a Lagrangian system - i.e., it possesses well-defined potential and kinetic energy terms. Then, (ii) we provide formal proof of global Input-to-State stability using Lyapunov arguments.
Moving to the experimental side, we demonstrate that CON reaches SoA performance when learning complex nonlinear dynamics of mechanical systems directly from images.
An additional methodological innovation contributing to achieving this third goal is an approximated closed-form solution for efficient integration of network dynamics, which eases efficient training.
We tackle (iii) by approximating the forcing-to-input mapping with a decoder that is trained to reconstruct the input based on the encoded latent space force.
Finally, we leverage these three properties and show that they enable latent-space control. We use an integral-saturated PID with potential force compensation and demonstrate high-quality performance on a soft robot using raw pixels as the only feedback information. | Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space | [
"Maximilian Stölzle",
"Cosimo Della Santina"
] | NeurIPS.cc/2024/Conference | 2409.08439 | [
"https://github.com/tud-phi/uncovering-iss-coupled-oscillator-networks-from-pixels"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=kXErlJSZ84 | @inproceedings{
baena2024general,
title={General Detection-based Text Line Recognition},
author={Raphael Baena and syrine kalleli and Mathieu Aubry},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kXErlJSZ84}
} | We introduces a general detection-based approach to text line recognition, be it printed (OCR) or handwritten text (HTR), with latin, chinese or ciphered characters. Detection based approaches have until now largely been discarded for HTR because reading characters separately is often challenging, and character-level annotation is difficult and expensive. We overcome these challenges thanks to three main insights: (i) synthetic pre-training with diverse enough data enables to learn reasonable characters localization in any script; (ii) modern transformer-based detectors can jointly detect a large number of instances and, if trained with an adequate masking strategy, leverage consistency between the different detections; (iii) once a pre-trained detection model with approximate character localization is available, it is possible to fine-tune it with line-level annotation on real data, even with a different alphabet. Our approach thus builds on a completely different paradigm than most state-of-the-art methods, which rely on autoregressive decoding, predicting character values one by one, while we treat a complete line in parallel. Remarkably, our method demonstrates good performance on range of scripts, usually tackled with specialized approaches: latin script, chinese script, and ciphers, for which we significantly improve state-of-the-art performances. | General Detection-based Text Line Recognition | [
"Raphael Baena",
"syrine kalleli",
"Mathieu Aubry"
] | NeurIPS.cc/2024/Conference | 2409.17095 | [
"https://github.com/raphael-baena/dtlr"
] | https://huggingface.co/papers/2409.17095 | 0 | 0 | 0 | 3 | [
"Naataan/unofficial-DTLR"
] | [] | [] | [
"Naataan/unofficial-DTLR"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kWMVzIdCEn | @inproceedings{
zhang2024multiscale,
title={Multi-Scale Representation Learning for Protein Fitness Prediction},
author={Zuobai Zhang and Pascal Notin and Yining Huang and Aurelie Lozano and Vijil Chenthamarakshan and Debora Susan Marks and Payel Das and Jian Tang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kWMVzIdCEn}
} | Designing novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets. While initial protein representation learning studies solely focused on either sequence or structural features, recent hybrid architectures have sought to merge these modalities to harness their respective strengths. However, these sequence-structure models have so far achieved only incremental improvements when compared to the leading sequence-only approaches, highlighting unresolved challenges effectively leveraging these modalities together. Moreover, the function of certain proteins is highly dependent on the granular aspects of their surface topology, which have been overlooked by prior models.
To address these limitations, we introduce the Sequence-Structure-Surface Fitness (**S3F**) model — a novel multimodal representation learning framework that integrates protein features across several scales. Our approach combines sequence representations from a protein language model with Geometric Vector Perceptron networks encoding protein backbone and detailed surface topology. The proposed method achieves state-of-the-art fitness prediction on the ProteinGym benchmark encompassing 217 substitution deep mutational scanning assays, and provides insights into the determinants of protein function.
Our code is at https://github.com/DeepGraphLearning/S3F. | Multi-Scale Representation Learning for Protein Fitness Prediction | [
"Zuobai Zhang",
"Pascal Notin",
"Yining Huang",
"Aurelie Lozano",
"Vijil Chenthamarakshan",
"Debora Susan Marks",
"Payel Das",
"Jian Tang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kW30LbNwdV | @inproceedings{
zhao2024improving,
title={Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation},
author={Shiji Zhao and Ranjie Duan and xizhewang and Xingxing Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kW30LbNwdV}
} | Adversarial Training (AT) has been widely proved to be an effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs). As a variant of AT, Adversarial Robustness Distillation (ARD) has demonstrated its superior performance in improving the robustness of small student models with the guidance of large teacher models. However, both AT and ARD encounter the robust fairness problem: these models exhibit strong robustness when facing part of classes (easy class), but weak robustness when facing others (hard class). In this paper, we give an in-depth analysis of the potential factors and argue that the smoothness degree of samples' soft labels for different classes (i.e., hard class or easy class) will affect the robust fairness of DNNs from both empirical observation and theoretical analysis. Based on the above finding, we propose an Anti-Bias Soft Label Distillation (ABSLD) method to mitigate the adversarial robust fairness problem within the framework of Knowledge Distillation (KD). Specifically, ABSLD adaptively reduces the student's error risk gap between different classes to achieve fairness by adjusting the class-wise smoothness degree of samples' soft labels during the training process, and the smoothness degree of soft labels is controlled by assigning different temperatures in KD to different classes. Extensive experiments demonstrate that ABSLD outperforms state-of-the-art AT, ARD, and robust fairness methods in the comprehensive metric (Normalized Standard Deviation) of robustness and fairness. | Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation | [
"Shiji Zhao",
"Ranjie Duan",
"xizhewang",
"Xingxing Wei"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/zhaoshiji123/absld"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kVuw8vzsqZ | @inproceedings{
shahout2024skippredict,
title={SkipPredict: When to Invest in Predictions for Scheduling},
author={Rana Shahout and Michael Mitzenmacher},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kVuw8vzsqZ}
} | Expanding on recent work on scheduling with predicted job sizes, we consider the effect of the cost of predictions in queueing systems, removing the assumption in prior research that predictions are external to the system’s resources and/or cost-free. Additionally, we introduce a novel approach to utilizing predictions, SkipPredict, designed to address their inherent cost. Rather than uniformly applying predictions to all jobs, we propose a tailored approach that categorizes jobs to improve the effectiveness of prediction on performance. To achieve this, we employ one-bit “cheap predictions” to classify jobs as either short or long. SkipPredict prioritizes predicted short jobs over long jobs, and for the long jobs, SkipPredict applies a second round of more detailed “expensive predictions” to approximate Shortest Remaining Processing Time for these jobs. Importantly, our analyses take into account the cost of prediction. We derive closed-form formulas that calculate the mean response time of jobs with size predictions accounting for the prediction cost. We examine the effect of this cost for two distinct models in real-world and synthetic datasets. In the external cost model, predictions are generated by external method without impacting job service times but incur a cost. In the server time cost model, predictions themselves require server processing time and are scheduled on the same server as the jobs. | SkipPredict: When to Invest in Predictions for Scheduling | [
"Rana Shahout",
"Michael Mitzenmacher"
] | NeurIPS.cc/2024/Conference | 2402.03564 | [
""
] | https://huggingface.co/papers/2402.03564 | 0 | 0 | 0 | 2 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kVr3L73pNH | @inproceedings{
wang2024data,
title={Data Attribution for Text-to-Image Models by Unlearning Synthesized Images},
author={Sheng-Yu Wang and Aaron Hertzmann and Alexei A Efros and Jun-Yan Zhu and Richard Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kVr3L73pNH}
} | The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image. Influence is defined such that, for a given output, if a model is retrained from scratch without the most influential images, the model would fail to reproduce the same output. Unfortunately, directly searching for these influential images is computationally infeasible, since it would require repeatedly retraining models from scratch. In our work, we propose an efficient data attribution method by simulating unlearning the synthesized image. We achieve this by increasing the training loss on the output image, without catastrophic forgetting of other, unrelated concepts. We then identify training images with significant loss deviations after the unlearning process and label these as influential. We evaluate our method with a computationally intensive but "gold-standard" retraining from scratch and demonstrate our method's advantages over previous methods. | Data Attribution for Text-to-Image Models by Unlearning Synthesized Images | [
"Sheng-Yu Wang",
"Aaron Hertzmann",
"Alexei A Efros",
"Jun-Yan Zhu",
"Richard Zhang"
] | NeurIPS.cc/2024/Conference | 2406.09408 | [
"https://github.com/peterwang512/attributebyunlearning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kVL5rvkqGG | @inproceedings{
jiang2024repurposing,
title={Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe},
author={Albert Q. Jiang and Alicja Ziarko and Bartosz Piotrowski and Wenda Li and Mateja Jamnik and Piotr Mi{\l}o{\'s}},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kVL5rvkqGG}
} | Text embeddings are essential for tasks such as document retrieval, clustering, and semantic similarity assessment. In this paper, we study how to contrastively train text embedding models in a compute-optimal fashion, given a suite of pretrained decoder-only language models. Our innovation is an algorithm that produces optimal configurations of model sizes, data quantities, and fine-tuning methods for text-embedding models at different computational budget levels. The resulting recipe, which we obtain through extensive experiments, can be used by practitioners to make informed design choices for their embedding models. Specifically, our findings suggest that full fine-tuning and Low-Rank Adaptation fine-tuning produce optimal models at lower and higher computational budgets respectively. | Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe | [
"Albert Q. Jiang",
"Alicja Ziarko",
"Bartosz Piotrowski",
"Wenda Li",
"Mateja Jamnik",
"Piotr Miłoś"
] | NeurIPS.cc/2024/Conference | 2406.04165 | [
"https://github.com/SeqDM/Efficient-Embeddings"
] | https://huggingface.co/papers/2406.04165 | 0 | 1 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kV80nC1afE | @inproceedings{
shi2024adaptive,
title={Adaptive Passive-Aggressive Framework for Online Regression with Side Information},
author={Runhao Shi and Jiaxi Ying and Daniel P. Palomar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kV80nC1afE}
} | The Passive-Aggressive (PA) method is widely used in online regression problems for handling large-scale streaming data, typically updating model parameters in a passive-aggressive manner based on whether the error exceeds a predefined threshold. However, this approach struggles with determining optimal thresholds and adapting to complex scenarios with side information, where tracking accuracy is not the sole metric in the regression model. To address these challenges, we introduce a novel adaptive framework that allows finer adjustments to the weight vector in PA using side information. This framework adaptively selects the threshold parameter in PA, theoretically ensuring convergence to the optimal setting. Additionally, we present an efficient implementation of our algorithm that significantly reduces computational complexity. Numerical experiments show that our model achieves outstanding performance associated with the side information while maintaining low tracking error, demonstrating marked improvements over traditional PA methods across various scenarios. | Adaptive Passive-Aggressive Framework for Online Regression with Side Information | [
"Runhao Shi",
"Jiaxi Ying",
"Daniel P. Palomar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kTtK65vKvD | @inproceedings{
zhu2024odgen,
title={{ODGEN}: Domain-specific Object Detection Data Generation with Diffusion Models},
author={JingYuan Zhu and Shiyu Li and Yuxuan Liu and Jian Yuan and Ping Huang and Jiulong Shan and Huimin Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kTtK65vKvD}
} | Modern diffusion-based image generative models have made significant progress and become promising to enrich training data for the object detection task. However, the generation quality and the controllability for complex scenes containing multi-class objects and dense objects with occlusions remain limited. This paper presents ODGEN, a novel method to generate high-quality images conditioned on bounding boxes, thereby facilitating data synthesis for object detection. Given a domain-specific object detection dataset, we first fine-tune a pre-trained diffusion model on both cropped foreground objects and entire images to fit target distributions. Then we propose to control the diffusion model using synthesized visual prompts with spatial constraints and object-wise textual descriptions. ODGEN exhibits robustness in handling complex scenes and specific domains. Further, we design a dataset synthesis pipeline to evaluate ODGEN on 7 domain-specific benchmarks to demonstrate its effectiveness. Adding training data generated by ODGEN improves up to 25.3% [email protected]:.95 with object detectors like YOLOv5 and YOLOv7, outperforming prior controllable generative methods. In addition, we design an evaluation protocol based on COCO-2014 to validate ODGEN in general domains and observe an advantage up to 5.6% in [email protected]:.95 against existing methods. | ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models | [
"JingYuan Zhu",
"Shiyu Li",
"Yuxuan Liu",
"Jian Yuan",
"Ping Huang",
"Jiulong Shan",
"Huimin Ma"
] | NeurIPS.cc/2024/Conference | 2405.15199 | [
""
] | https://huggingface.co/papers/2405.15199 | 0 | 0 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kS9dciADtY | @inproceedings{
lee2024textinfused,
title={Text-Infused Attention and Foreground-Aware Modeling for Zero-Shot Temporal Action Detection},
author={Yearang Lee and Ho-Joong Kim and Seong-Whan Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kS9dciADtY}
} | Zero-Shot Temporal Action Detection (ZSTAD) aims to classify and localize action segments in untrimmed videos for unseen action categories. Most existing ZSTAD methods utilize a foreground-based approach, limiting the integration of text and visual features due to their reliance on pre-extracted proposals. In this paper, we introduce a cross-modal ZSTAD baseline with mutual cross-attention, integrating both text and visual information throughout the detection process. Our simple approach results in superior performance compared to previous methods. Despite this improvement, we further identify a common-action bias issue that the cross-modal baseline over-focus on common sub-actions due to a lack of ability to discriminate text-related visual parts. To address this issue, we propose Text-infused attention and Foreground-aware Action Detection (Ti-FAD), which enhances the ability to focus on text-related sub-actions and distinguish relevant action segments from the background. Our extensive experiments demonstrate that Ti-FAD outperforms the state-of-the-art methods on ZSTAD benchmarks by a large margin: 41.2\% (+ 11.0\%) on THUMOS14 and 32.0\% (+ 5.4\%) on ActivityNet v1.3. Code is available at: https://github.com/YearangLee/Ti-FAD. | Text-Infused Attention and Foreground-Aware Modeling for Zero-Shot Temporal Action Detection | [
"Yearang Lee",
"Ho-Joong Kim",
"Seong-Whan Lee"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kRwQCAIA7z | @inproceedings{
dagan2024dimensionfree,
title={Dimension-free Private Mean Estimation for Anisotropic Distributions},
author={Yuval Dagan and Michael Jordan and Xuelin Yang and Lydia Zakynthinou and Nikita Zhivotovskiy},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kRwQCAIA7z}
} | We present differentially private algorithms for high-dimensional mean estimation. Previous private estimators on distributions over $\mathbb{R}^d$ suffer from a curse of dimensionality, as they require $\Omega(d^{1/2})$ samples to achieve non-trivial error, even in cases where $O(1)$ samples suffice without privacy. This rate is unavoidable when the distribution is isotropic, namely, when the covariance is a multiple of the identity matrix. Yet, real-world data is often highly anisotropic, with signals concentrated on a small number of principal components. We develop estimators that are appropriate for such signals---our estimators are $(\varepsilon,\delta)$-differentially private and have sample complexity that is dimension-independent for anisotropic subgaussian distributions. Given $n$ samples from a distribution with known covariance-proxy $\Sigma$ and unknown mean $\mu$, we present an estimator $\hat{\mu}$ that achieves error, $\|\hat{\mu}-\mu\|_2\leq \alpha$, as long as $n\gtrsim \text{tr}(\Sigma)/\alpha^2+ \text{tr}(\Sigma^{1/2})/(\alpha\varepsilon)$. We show that this is the optimal sample complexity for this task up to logarithmic factors. Moreover, for the case of unknown covariance, we present an algorithm whose sample complexity has improved dependence on the dimension, from $d^{1/2}$ to $d^{1/4}$. | Dimension-free Private Mean Estimation for Anisotropic Distributions | [
"Yuval Dagan",
"Michael Jordan",
"Xuelin Yang",
"Lydia Zakynthinou",
"Nikita Zhivotovskiy"
] | NeurIPS.cc/2024/Conference | 2411.00775 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kREpCQtHdN | @inproceedings{
sun2024identifying,
title={Identifying Latent State-Transition Processes for Individualized Reinforcement Learning},
author={Yuewen Sun and Biwei Huang and Yu Yao and Donghuo Zeng and Xinshuai Dong and Songyao Jin and Boyang Sun and Roberto Legaspi and Kazushi Ikeda and Peter Spirtes and Kun Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kREpCQtHdN}
} | In recent years, the application of reinforcement learning (RL) involving interactions with individuals has seen significant growth. These interactions, influenced by individual-specific factors ranging from personal preferences to physiological differences, can causally affect state transitions, such as the health conditions in healthcare or learning progress in education. Consequently, different individuals may exhibit different state-transition processes. Understanding these individualized state-transition processes is crucial for optimizing individualized policies. In practice, however, identifying these state-transition processes is challenging, especially since individual-specific factors often remain latent. In this paper, we establish the identifiability of these latent factors and present a practical method that effectively learns these processes from observed state-action trajectories. Our experiments on various datasets show that our method can effectively identify the latent state-transition processes and help learn individualized RL policies. | Identifying Latent State-Transition Processes for Individualized Reinforcement Learning | [
"Yuewen Sun",
"Biwei Huang",
"Yu Yao",
"Donghuo Zeng",
"Xinshuai Dong",
"Songyao Jin",
"Boyang Sun",
"Roberto Legaspi",
"Kazushi Ikeda",
"Peter Spirtes",
"Kun Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kQPzFiwVIu | @inproceedings{
mora2024synthetic,
title={Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages},
author={Federico Mora and Justin Wong and Haley Lepe and Sahil Bhatia and Karim Elmaaroufi and George Varghese and Joseph E. Gonzalez and Elizabeth Polgreen and Sanjit A. Seshia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kQPzFiwVIu}
} | Recent advances in large language models (LLMs) for code applications have demonstrated remarkable zero-shot fluency and instruction following on challenging code related tasks ranging from test case generation to self-repair. Unsurprisingly, however, models struggle to compose syntactically valid programs in programming languages unrepresented in pre-training, referred to as very low-resource Programming Languages (VLPLs). VLPLs appear in crucial settings, including domain-specific languages for internal tools, tool-chains for legacy languages, and formal verification frameworks. Inspired by a technique called natural programming elicitation, we propose designing an intermediate language that LLMs ``naturally'' know how to use and which can be automatically compiled to a target VLPL. When LLMs generate code that lies outside of this intermediate language, we use compiler techniques to repair the code into programs in the intermediate language. Overall, we introduce _synthetic programming elicitation and compilation_ (SPEAC), an approach that enables LLMs to generate syntactically valid code even for VLPLs. We empirically evaluate the performance of SPEAC in a case study for the UCLID5 formal verification language and find that, compared to existing retrieval and fine-tuning baselines, SPEAC produces syntactically correct programs more frequently and without sacrificing semantic correctness. | Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages | [
"Federico Mora",
"Justin Wong",
"Haley Lepe",
"Sahil Bhatia",
"Karim Elmaaroufi",
"George Varghese",
"Joseph E. Gonzalez",
"Elizabeth Polgreen",
"Sanjit A. Seshia"
] | NeurIPS.cc/2024/Conference | 2406.03636 | [
"https://github.com/FedericoAureliano/eudoxus"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kQMyiDWbOG | @inproceedings{
lv2024advancing,
title={Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators},
author={Changze Lv and Dongqi Han and Yansen Wang and Xiaoqing Zheng and Xuanjing Huang and Dongsheng Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kQMyiDWbOG}
} | Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible.
However, applying SNNs to sequential tasks, such as text classification and time-series forecasting, has been hindered by the challenge of creating an effective and hardware-friendly spike-form positional encoding (PE) strategy.
Drawing inspiration from the central pattern generators (CPGs) in the human brain, which produce rhythmic patterned outputs without requiring rhythmic inputs, we propose a novel PE technique for SNNs, termed CPG-PE.
We demonstrate that the commonly used sinusoidal PE is mathematically a specific solution to the membrane potential dynamics of a particular CPG.
Moreover, extensive experiments across various domains, including time-series forecasting, natural language processing, and image classification, show that SNNs with CPG-PE outperform their conventional counterparts.
Additionally, we perform analysis experiments to elucidate the mechanism through which SNNs encode positional information and to explore the function of CPGs in the human brain.
This investigation may offer valuable insights into the fundamental principles of neural computation. | Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators | [
"Changze Lv",
"Dongqi Han",
"Yansen Wang",
"Xiaoqing Zheng",
"Xuanjing Huang",
"Dongsheng Li"
] | NeurIPS.cc/2024/Conference | 2405.14362 | [
"https://github.com/microsoft/seqsnn"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=kQ9LgM2JQT | @inproceedings{
lau2024qgfn,
title={{QGFN}: Controllable Greediness with Action Values},
author={Elaine Lau and Stephen Zhewen Lu and Ling Pan and Doina Precup and Emmanuel Bengio},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kQ9LgM2JQT}
} | Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples. However, consistently biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate, $Q$, to create greedier sampling policies which can be controlled by a mixing parameter. We show that several variants of the proposed method, QGFN, are able to improve on the number of high-reward samples generated in a variety of tasks without sacrificing diversity. | QGFN: Controllable Greediness with Action Values | [
"Elaine Lau",
"Stephen Zhewen Lu",
"Ling Pan",
"Doina Precup",
"Emmanuel Bengio"
] | NeurIPS.cc/2024/Conference | 2402.05234 | [
"https://github.com/yunglau/QGFN"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kPmSfhCM5s | @inproceedings{
fei2024vitron,
title={Vitron: A Unified Pixel-level Vision {LLM} for Understanding, Generating, Segmenting, Editing},
author={Hao Fei and Shengqiong Wu and Hanwang Zhang and Tat-Seng Chua and Shuicheng YAN},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kPmSfhCM5s}
} | Recent developments of vision large language models (LLMs) have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained instance-level understanding, lack of unified support for both images and videos, and insufficient coverage across various vision tasks. In this paper we present Vitron, a universal pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing of both static images and dynamic videos. Building on top of an LLM backbone, Vitron incorporates encoders for images, videos, and pixel-level regional visuals within its frontend modules, while employing state-of-the-art visual specialists as its backend, via which Vitron supports a spectrum of vision end tasks, spanning visual comprehension to visual generation, from low level to high level. To ensure an effective and precise message passing from LLM to backend modules for function invocation, we propose a novel hybrid method by simultaneously integrating discrete textual instructions and continuous signal embeddings. Further, we design various pixel-level spatiotemporal vision-language alignment learning for Vitron to reach the best fine-grained visual capability. Finally, a cross-task synergy module is advised to learn to maximize the task-invariant fine-grained visual features, enhancing the synergy between different visual tasks. Demonstrated over 12 visual tasks and evaluated across 22 datasets, Vitron showcases its extensive capabilities in the four main vision task clusters. Overall, this work illuminates the great potential of developing a more unified multimodal generalist. | Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing | [
"Hao Fei",
"Shengqiong Wu",
"Hanwang Zhang",
"Tat-Seng Chua",
"Shuicheng YAN"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kPGNE4CrTq | @inproceedings{
li2024solving,
title={Solving Sparse {\textbackslash}\& High-Dimensional-Output Regression via Compression},
author={Renyuan Li and Zhehui Chen and Guanyi Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kPGNE4CrTq}
} | Multi-Output Regression (MOR) has been widely used in scientific data analysis for decision-making. Unlike traditional regression models, MOR aims to simultaneously predict multiple real-valued outputs given an input. However, the increasing dimensionality of the outputs poses significant challenges regarding interpretability and computational scalability for modern MOR applications. As a first step to address these challenges, this paper proposes a Sparse \& High-dimensional-Output REgression (SHORE) model by incorporating additional sparsity requirements to resolve the output interpretability, and then designs a computationally efficient two-stage optimization framework capable of solving SHORE with provable accuracy via compression on outputs. Theoretically, we show that the proposed framework is computationally scalable while maintaining the same order of training loss and prediction loss before-and-after compression under arbitrary or relatively weak sample set conditions. Empirically, numerical results further validate the theoretical findings, showcasing the efficiency and accuracy of the proposed framework. | Solving Sparse & High-Dimensional-Output Regression via Compression | [
"Renyuan Li",
"Zhehui Chen",
"Guanyi Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kPBEAZU5Nm | @inproceedings{
stechly2024chain,
title={Chain of Thoughtlessness? An Analysis of CoT in Planning},
author={Kaya Stechly and Karthik Valmeekam and Subbarao Kambhampati},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kPBEAZU5Nm}
} | Large language model (LLM) performance on reasoning problems typically does not generalize out of distribution. Previous work has claimed that this can be mitigated with chain of thought prompting--a method of demonstrating solution procedures--with the intuition that it is possible to in-context teach an LLM an algorithm for solving the problem.
This paper presents a case study of chain of thought on problems from Blocksworld, a classical planning domain, and examines the performance of two state-of-the-art LLMs across two axes: generality of examples given in prompt, and complexity of problems queried with each prompt. While our problems are very simple, we only find meaningful performance improvements from chain of thought prompts when those prompts are exceedingly specific to their problem class, and that those improvements quickly deteriorate as the size n of the query-specified stack grows past the size of stacks shown in the examples.
We also create scalable variants of three domains commonly studied in previous CoT papers and demonstrate the existence of similar failure modes.
Our results hint that, contrary to previous claims in the literature, CoT's performance improvements do not stem from the model learning general algorithmic procedures via demonstrations but depend on carefully engineering highly problem specific prompts. This spotlights drawbacks of chain of thought, especially the sharp tradeoff between possible performance gains and the amount of human labor necessary to generate examples with correct reasoning traces. | Chain of Thoughtlessness? An Analysis of CoT in Planning | [
"Kaya Stechly",
"Karthik Valmeekam",
"Subbarao Kambhampati"
] | NeurIPS.cc/2024/Conference | 2405.04776 | [
""
] | https://huggingface.co/papers/2405.04776 | 1 | 1 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kOMrm4ZJ3m | @inproceedings{
alfarano2024global,
title={Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers},
author={Alberto Alfarano and Francois Charton and Amaury Hayat},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kOMrm4ZJ3m}
} | Despite their spectacular progress, language models still struggle on complex reasoning tasks, such as advanced mathematics.
We consider a long-standing open problem in mathematics: discovering a Lyapunov function that ensures the global stability of a dynamical system. This problem has no known general solution, and algorithmic solvers only exist for some small polynomial systems.
We propose a new method for generating synthetic training samples from random solutions, and show that sequence-to-sequence transformers trained on such datasets perform better than algorithmic solvers and humans on polynomial systems, and can discover new Lyapunov functions for non-polynomial systems. | Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers | [
"Alberto Alfarano",
"Francois Charton",
"Amaury Hayat"
] | NeurIPS.cc/2024/Conference | 2410.08304 | [
""
] | https://huggingface.co/papers/2410.08304 | 0 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kN7GTUss0l | @inproceedings{
bardou2024this,
title={This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization},
author={Anthony Bardou and Patrick Thiran and Giovanni Ranieri},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kN7GTUss0l}
} | Bayesian Optimization (BO) has proven to be very successful at optimizing a static, noisy, costly-to-evaluate black-box function $f : \mathcal{S} \to \mathbb{R}$. However, optimizing a black-box which is also a function of time (*i.e.*, a *dynamic* function) $f : \mathcal{S} \times \mathcal{T} \to \mathbb{R}$ remains a challenge, since a dynamic Bayesian Optimization (DBO) algorithm has to keep track of the optimum over time. This changes the nature of the optimization problem in at least three aspects: (i) querying an arbitrary point in $\mathcal{S} \times \mathcal{T}$ is impossible, (ii) past observations become less and less relevant for keeping track of the optimum as time goes by and (iii) the DBO algorithm must have a high sampling frequency so it can collect enough relevant observations to keep track of the optimum through time. In this paper, we design a Wasserstein distance-based criterion able to quantify the relevancy of an observation with respect to future predictions. Then, we leverage this criterion to build W-DBO, a DBO algorithm able to remove irrelevant observations from its dataset on the fly, thus maintaining simultaneously a good predictive performance and a high sampling frequency, even in continuous-time optimization tasks with unknown horizon. Numerical experiments establish the superiority of W-DBO, which outperforms state-of-the-art methods by a comfortable margin. | This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization | [
"Anthony Bardou",
"Patrick Thiran",
"Giovanni Ranieri"
] | NeurIPS.cc/2024/Conference | 2405.14540 | [
"https://github.com/wdbo-algorithm/wdbo_algo"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kMxdV4Blhn | @inproceedings{
zhang2024rethinking,
title={Rethinking 3D Convolution in \${\textbackslash}ell\_p\$-norm Space},
author={Li Zhang and Yan Zhong and Jianan Wang and Zhe Min and RujingWang and Liu Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kMxdV4Blhn}
} | Convolution is a fundamental operation in the 3D backbone. However, under certain conditions, the feature extraction ability of traditional convolution methods may be weakened. In this paper, we introduce a new convolution method based on $\ell_p$-norm.
For theoretical support, we prove the universal approximation theorem for $\ell_p$-norm based convolution, and analyze the robustness and feasibility of $\ell_p$-norms in 3D point cloud tasks. Concretely, $\ell_{\infty}$-norm based convolution is prone to feature loss. $\ell_2$-norm based convolution is essentially a linear transformation of the traditional convolution. $\ell_1$-norm based convolution is an economical and effective feature extractor. We propose customized optimization strategies to accelerate the training process of $\ell_1$-norm based Nets and enhance the performance. Besides, a theoretical guarantee is given for the convergence by \textit{regret} argument. We apply our methods to classic networks and conduct related experiments. Experimental results indicate that our approach exhibits competitive performance with traditional CNNs, with lower energy consumption and instruction latency. | Rethinking 3D Convolution in ℓ_p-norm Space | [
"Li Zhang",
"Yan Zhong",
"Jianan Wang",
"Zhe Min",
"RujingWang",
"Liu Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=kMnoh7CXrq | @inproceedings{
pagliardini2024denseformer,
title={DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging},
author={Matteo Pagliardini and Amirkeivan Mohtashami and Fran{\c{c}}ois Fleuret and Martin Jaggi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kMnoh7CXrq}
} | The transformer architecture by Vaswani et al. (2017) is now ubiquitous across application domains, from natural language processing to speech processing and image understanding. We propose DenseFormer, a simple modification to the standard architecture that improves the perplexity of the model without increasing its size---adding a few thousand parameters for large-scale models in the 100B parameters range. Our approach relies on an additional averaging step after each transformer block, which computes a weighted average of current and past representations---we refer to this operation as Depth-Weighted-Average (DWA). The learned DWA weights exhibit coherent patterns of information flow, revealing the strong and structured reuse of activations from distant layers. Experiments demonstrate that DenseFormer is more data efficient, reaching the same perplexity of much deeper transformer models, and that for the same perplexity, these new models outperform transformer baselines in terms of memory efficiency and inference time. | DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging | [
"Matteo Pagliardini",
"Amirkeivan Mohtashami",
"François Fleuret",
"Martin Jaggi"
] | NeurIPS.cc/2024/Conference | 2402.02622 | [
""
] | https://huggingface.co/papers/2402.02622 | 1 | 3 | 0 | 4 | [
"winglian/mistral-denseformer-7b",
"winglian/mistral-denseformer-7b-pretrained"
] | [] | [] | [
"winglian/mistral-denseformer-7b",
"winglian/mistral-denseformer-7b-pretrained"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kMAXN7HF6d | @inproceedings{
hajiaghayi2024fairness,
title={Fairness and Efficiency in Online Class Matching},
author={MohammadTaghi Hajiaghayi and Shayan Chashm Jahan and Mohammad Sharifi and Suho Shin and Max Springer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kMAXN7HF6d}
} | The online bipartite matching problem, extensively studied in the literature, deals with the allocation of online arriving vertices (items) to a predetermined set of offline vertices (agents). However, little attention has been given to the concept of class fairness, where agents are categorized into different classes, and the matching algorithm must ensure equitable distribution across these classes.
We here focus on randomized algorithms for the fair matching of indivisible items, subject to various definitions of fairness. Our main contribution is the first (randomized) non-wasteful algorithm that simultaneously achieves a $1/2$ approximation to class envy-freeness (CEF) while simultaneously ensuring an equivalent approximation to the class proportionality (CPROP) and utilitarian social welfare (USW) objectives. We supplement this result by demonstrating that no non-wasteful algorithm can achieve an $\alpha$-CEF guarantee for $\alpha > 0.761$. In a similar vein, we provide a novel input instance for deterministic divisible matching that demonstrates a nearly tight CEF approximation.
Lastly, we define the ``price of fairness," which represents the trade-off between optimal and fair matching. We demonstrate that increasing the level of fairness in the approximation of the solution leads to a decrease in the objective of maximizing USW, following an inverse proportionality relationship. | Fairness and Efficiency in Online Class Matching | [
"MohammadTaghi Hajiaghayi",
"Shayan Chashm Jahan",
"Mohammad Sharifi",
"Suho Shin",
"Max Springer"
] | NeurIPS.cc/2024/Conference | 2410.19163 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kLiWXUdCEw | @inproceedings{
olesker-taylor2024an,
title={An Analysis of Elo Rating Systems via Markov Chains},
author={Sam Olesker-Taylor and Luca Zanetti},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kLiWXUdCEw}
} | We present a theoretical analysis of the Elo rating system, a popular method for ranking skills of players in an online setting. In particular, we study Elo under the Bradley-Terry-Luce model and, using techniques from Markov chain theory, show that Elo learns the model parameters at a rate competitive with the state-of-the-art. We apply our results to the problem of efficient tournament design and discuss a connection with the fastest-mixing Markov chain problem. | An Analysis of Elo Rating Systems via Markov Chains | [
"Sam Olesker-Taylor",
"Luca Zanetti"
] | NeurIPS.cc/2024/Conference | 2406.05869 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kLen1XyW6P | @inproceedings{
bhaskara2024on,
title={On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models},
author={Aditya Bhaskara and Agastya Vibhuti Jha and Michael Kapralov and Naren Sarayu Manoj and Davide Mazzali and Weronika Wrzos-Kaminska},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kLen1XyW6P}
} | In a graph bisection problem, we are given a graph $G$ with two equally-sized unlabeled communities, and the goal is to recover the vertices in these communities. A popular heuristic, known as spectral clustering, is to output an estimated community assignment based on the eigenvector corresponding to the second-smallest eigenvalue of the Laplacian of $G$. Spectral algorithms can be shown to provably recover the cluster structure for graphs generated from probabilistic models, such as the Stochastic Block Model (SBM). However, spectral clustering is known to be non-robust to model mis-specification. Techniques based on semidefinite programming have been shown to be more robust, but they incur significant computational overheads.
In this work, we study the robustness of spectral algorithms against semirandom adversaries. Informally, a semirandom adversary is allowed to ``helpfully'' change the specification of the model in a way that is consistent with the ground-truth solution. Our semirandom adversaries in particular are allowed to add edges inside clusters or increase the probability that an edge appears inside a cluster. Semirandom adversaries are a useful tool to determine the extent to which an algorithm has overfit to statistical assumptions on the input.
On the positive side, we identify a wide range of semirandom adversaries under which spectral bisection using the _unnormalized_ Laplacian is strongly consistent, i.e., it exactly recovers the planted partitioning. On the negative side, we show that in many of these settings, _normalized_ spectral bisection outputs a partitioning that makes a classification mistake on a constant fraction of the vertices. Finally, we demonstrate numerical experiments that complement our theoretical findings. | On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models | [
"Aditya Bhaskara",
"Agastya Vibhuti Jha",
"Michael Kapralov",
"Naren Sarayu Manoj",
"Davide Mazzali",
"Weronika Wrzos-Kaminska"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kK23oMGe9g | @inproceedings{
li2024immiscible,
title={Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment},
author={Yiheng Li and Heyang Jiang and Akio Kodaira and Masayoshi Tomizuka and Kurt Keutzer and Chenfeng Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kK23oMGe9g}
} | In this paper, we point out that suboptimal noise-data mapping leads to slow training of diffusion models. During diffusion training, current methods diffuse each image across the entire noise space, resulting in a mixture of all images at every point in the noise layer. We emphasize that this random mixture of noise-data mapping complicates the optimization of the denoising function in diffusion models. Drawing inspiration from the immiscibility phenomenon in physics, we propose *Immiscible Diffusion*, a simple and effective method to improve the random mixture of noise-data mapping. In physics, miscibility can vary according to various intermolecular forces. Thus, immiscibility means that the mixing of molecular sources is distinguishable. Inspired by this concept, we propose an assignment-then-diffusion training strategy to achieve *Immiscible Diffusion*. As one example, prior to diffusing the image data into noise, we assign diffusion target noise for the image data by minimizing the total image-noise pair distance in a mini-batch. The assignment functions analogously to external forces to expel the diffuse-able areas of images, thus mitigating the inherent difficulties in diffusion training. Our approach is remarkably simple, requiring only *one line of code* to restrict the diffuse-able area for each image while preserving the Gaussian distribution of noise. In this way, each image is preferably projected to nearby noise. To address the high complexity of the assignment algorithm, we employ a quantized assignment strategy, which significantly reduces the computational overhead to a negligible level (e.g. 22.8ms for a large batch size of 1024 on an A6000). Experiments demonstrate that our method can achieve up to 3x faster training for unconditional Consistency Models on the CIFAR dataset, as well as for DDIM and Stable Diffusion on CelebA and ImageNet dataset, and in class-conditional training and fine-tuning. In addition, we conducted a thorough analysis that sheds light on how it improves diffusion training speed while improving fidelity. The code is available at https://yhli123.github.io/immiscible-diffusion | Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment | [
"Yiheng Li",
"Heyang Jiang",
"Akio Kodaira",
"Masayoshi Tomizuka",
"Kurt Keutzer",
"Chenfeng Xu"
] | NeurIPS.cc/2024/Conference | 2406.12303 | [
""
] | https://huggingface.co/papers/2406.12303 | 4 | 4 | 1 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=kJzecLYsRi | @inproceedings{
lu2024on,
title={On the Saturation Effects of Spectral Algorithms in Large Dimensions},
author={Weihao Lu and Haobo Zhang and Yicheng Li and Qian Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kJzecLYsRi}
} | The saturation effects, which originally refer to the fact that kernel ridge regression (KRR) fails to achieve the information-theoretical lower bound when the regression function is over-smooth, have been observed for almost 20 years and were rigorously proved recently for kernel ridge regression and some other spectral algorithms over a fixed dimensional domain. The main focus of this paper is to explore the saturation effects for a large class of spectral algorithms (including the KRR, gradient descent, etc.) in large dimensional settings where $n \asymp d^{\gamma}$. More precisely, we first propose an improved minimax lower bound for the kernel regression problem in large dimensional settings and show that the gradient flow with early stopping strategy will result in an estimator achieving this lower bound (up to a logarithmic factor). Similar to the results in KRR, we can further determine the exact convergence rates (both upper and lower bounds) of a large class of (optimal tuned) spectral algorithms with different qualification $\tau$'s. In particular, we find that these exact rate curves (varying along $\gamma$) exhibit the periodic plateau behavior and the polynomial approximation barrier. Consequently, we can fully depict the saturation effects of the spectral algorithms and reveal a new phenomenon in large dimensional settings (i.e., the saturation effect occurs in large dimensional setting as long as the source condition $s>\tau$ while it occurs in fixed dimensional setting as long as $s>2\tau$). | On the Saturation Effects of Spectral Algorithms in Large Dimensions | [
"Weihao Lu",
"Haobo Zhang",
"Yicheng Li",
"Qian Lin"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kJkp2ECJT7 | @inproceedings{
zhu2024towards,
title={Towards Flexible Visual Relationship Segmentation},
author={Fangrui Zhu and Jianwei Yang and Huaizu Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kJkp2ECJT7}
} | Visual relationship understanding has been studied separately in human-object interaction(HOI) detection, scene graph generation(SGG), and referring relationships(RR) tasks.
Given the complexity and interconnectedness of these tasks, it is crucial to have a flexible framework that can effectively address these tasks in a cohesive manner.
In this work, we propose FleVRS, a single model that seamlessly integrates the above three aspects in standard and promptable visual relationship segmentation, and further possesses the capability for open-vocabulary segmentation to adapt to novel scenarios.
FleVRS leverages the synergy between text and image modalities,
to ground various types of relationships from images and use textual features from vision-language models to visual conceptual understanding.
Empirical validation across various datasets demonstrates that our framework outperforms existing models in standard, promptable, and open-vocabulary tasks, e.g., +1.9 $mAP$ on HICO-DET, +11.4 $Acc$ on VRD, +4.7 $mAP$ on unseen HICO-DET.
Our FleVRS represents a significant step towards a more intuitive, comprehensive, and scalable understanding of visual relationships. | Towards Flexible Visual Relationship Segmentation | [
"Fangrui Zhu",
"Jianwei Yang",
"Huaizu Jiang"
] | NeurIPS.cc/2024/Conference | 2408.08305 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kHXUb494SY | @inproceedings{
gupta2024nesterov,
title={Nesterov acceleration despite very noisy gradients},
author={Kanan Gupta and Jonathan W. Siegel and Stephan Wojtowytsch},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kHXUb494SY}
} | We present a generalization of Nesterov's accelerated gradient descent algorithm. Our algorithm (AGNES) provably achieves acceleration for smooth convex and strongly convex minimization tasks with noisy gradient estimates if the noise intensity is proportional to the magnitude of the gradient at every point. Nesterov's method converges at an accelerated rate if the constant of proportionality is below 1, while AGNES accommodates any signal-to-noise ratio. The noise model is motivated by applications in overparametrized machine learning. AGNES requires only two parameters in convex and three in strongly convex minimization tasks, improving on existing methods. We further provide clear geometric interpretations and heuristics for the choice of parameters. | Nesterov acceleration despite very noisy gradients | [
"Kanan Gupta",
"Jonathan W. Siegel",
"Stephan Wojtowytsch"
] | NeurIPS.cc/2024/Conference | 2302.05515 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kEQFjKqiqM | @inproceedings{
zhao2024distributedorder,
title={Distributed-Order Fractional Graph Operating Network},
author={Kai Zhao and Xuhao Li and Qiyu Kang and Feng Ji and Qinxu Ding and Yanan Zhao and Wenfei Liang and Wee Peng Tay},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kEQFjKqiqM}
} | We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus.
Unlike traditional continuous GNNs that utilize integer-order or single fractional-order differential equations, DRAGON uses a learnable probability distribution over a range of real numbers for the derivative orders.
By allowing a flexible and learnable superposition of multiple derivative orders, our framework captures complex graph feature updating dynamics beyond the reach of conventional models.
We provide a comprehensive interpretation of our framework's capability to capture intricate dynamics through the lens of a non-Markovian graph random walk with node feature updating driven by an anomalous diffusion process over the graph.
Furthermore, to highlight the versatility of the DRAGON framework, we conduct empirical evaluations across a range of graph learning tasks. The results consistently demonstrate superior performance when compared to traditional continuous GNN models. The implementation code is available at \url{https://github.com/zknus/NeurIPS-2024-DRAGON}. | Distributed-Order Fractional Graph Operating Network | [
"Kai Zhao",
"Xuhao Li",
"Qiyu Kang",
"Feng Ji",
"Qinxu Ding",
"Yanan Zhao",
"Wenfei Liang",
"Wee Peng Tay"
] | NeurIPS.cc/2024/Conference | 2411.05274 | [
"https://github.com/zknus/neurips-2024-dragon"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=kEPpD7yETM | @inproceedings{
ma2024large,
title={Large Language Models Play StarCraft {II}:Benchmarks and A Chain of Summarization Approach},
author={Weiyu Ma and Qirui Mi and Yongcheng Zeng and Xue Yan and Runji Lin and Yuqiao Wu and Jun Wang and Haifeng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kEPpD7yETM}
} | With the continued advancement of Large Language Models (LLMs) Agents in reasoning, planning, and decision-making, benchmarks have become crucial in evaluating these skills. However, there is a notable gap in benchmarks for real-time strategic decision-making. StarCraft II (SC2), with its complex and dynamic nature, serves as an ideal setting for such evaluations. To this end, we have developed TextStarCraft II, a specialized environment for assessing LLMs in real-time strategic scenarios within SC2. Addressing the limitations of traditional Chain of Thought (CoT) methods, we introduce the Chain of Summarization (CoS) method, enhancing LLMs' capabilities in rapid and effective decision-making. Our key experiments included:
1. LLM Evaluation: Tested 10 LLMs in TextStarCraft II, most of them defeating LV5 build-in AI, showcasing effective strategy skills.
2. Commercial Model Knowledge: Evaluated four commercial models on SC2 knowledge; GPT-4 ranked highest by Grandmaster-level experts.
3. Human-AI Matches: Experimental results showed that fine-tuned LLMs performed on par with Gold-level players in real-time matches, demonstrating comparable strategic abilities.
All code and data from this
study have been made pulicly available at https://github.com/histmeisah/Large-Language-Models-play-StarCraftII | Large Language Models Play StarCraft II:Benchmarks and A Chain of Summarization Approach | [
"Weiyu Ma",
"Qirui Mi",
"Yongcheng Zeng",
"Xue Yan",
"Runji Lin",
"Yuqiao Wu",
"Jun Wang",
"Haifeng Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=kCabCEhQWv | @inproceedings{
mitchel2024neural,
title={Neural Isometries: Taming Transformations for Equivariant {ML}},
author={Thomas Mitchel and Michael Taylor and Vincent Sitzmann},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kCabCEhQWv}
} | Real-world geometry and 3D vision tasks are replete with challenging symmetries that defy tractable analytical expression. In this paper, we introduce Neural Isometries, an autoencoder framework which learns to map the observation space to a general-purpose latent space wherein encodings are related by isometries whenever their corresponding observations are geometrically related in world space. Specifically, we regularize the latent space such that maps between encodings preserve a learned inner product and commute with a learned functional operator, in the same manner as rigid-body transformations commute with the Laplacian. This approach forms an effective backbone for self-supervised representation learning, and we demonstrate that a simple off-the-shelf equivariant network operating in the pre-trained latent space can achieve results on par with meticulously-engineered, handcrafted networks designed to handle complex, nonlinear symmetries. Furthermore, isometric maps capture information about the respective transformations in world space, and we show that this allows us to regress camera poses directly from the coefficients of the maps between encodings of adjacent views of a scene. | Neural Isometries: Taming Transformations for Equivariant ML | [
"Thomas Mitchel",
"Michael Taylor",
"Vincent Sitzmann"
] | NeurIPS.cc/2024/Conference | 2405.19296 | [
"https://github.com/vsitzmann/neural-isometries"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=k9uZfaeerK | @inproceedings{
liu2024uqguided,
title={{UQ}-Guided Hyperparameter Optimization for Iterative Learners},
author={Jiesong Liu and Feng Zhang and Jiawei Guan and Xipeng Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k9uZfaeerK}
} | Hyperparameter Optimization (HPO) plays a pivotal role in unleashing the potential of iterative machine learning models. This paper addresses a crucial aspect that has largely been overlooked in HPO: the impact of uncertainty in ML model training. The paper introduces the concept of uncertainty-aware HPO and presents a novel approach called the UQ-guided scheme for quantifying uncertainty. This scheme offers a principled and versatile method to empower HPO techniques in handling model uncertainty during their exploration of the candidate space.
By constructing a probabilistic model and implementing probability-driven candidate selection and budget allocation, this approach enhances the quality of the resulting model hyperparameters. It achieves a notable performance improvement of over 50\% in terms of accuracy regret and exploration time. | UQ-Guided Hyperparameter Optimization for Iterative Learners | [
"Jiesong Liu",
"Feng Zhang",
"Jiawei Guan",
"Xipeng Shen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=k9SH68MvJs | @inproceedings{
lai2024diffusionreward,
title={Diffusion-Reward Adversarial Imitation Learning},
author={Chun-Mao Lai and Hsiang-Chun Wang and Ping-Chun Hsieh and Yu-Chiang Frank Wang and Min-Hung Chen and Shao-Hua Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k9SH68MvJs}
} | Imitation learning aims to learn a policy from observing expert demonstrations without access to reward signals from environments. Generative adversarial imitation learning (GAIL) formulates imitation learning as adversarial learning, employing a generator policy learning to imitate expert behaviors and discriminator learning to distinguish the expert demonstrations from agent trajectories. Despite its encouraging results, GAIL training is often brittle and unstable. Inspired by the recent dominance of diffusion models in generative modeling, we propose Diffusion-Reward Adversarial Imitation Learning (DRAIL), which integrates a diffusion model into GAIL, aiming to yield more robust and smoother rewards for policy learning. Specifically, we propose a diffusion discriminative classifier to construct an enhanced discriminator, and design diffusion rewards based on the classifier’s output for policy learning. Extensive experiments are conducted in navigation, manipulation, and locomotion, verifying DRAIL’s effectiveness compared to prior imitation learning methods. Moreover, additional experimental results demonstrate the generalizability and data efficiency of DRAIL. Visualized learned reward functions of GAIL and DRAIL suggest that DRAIL can produce more robust and smoother rewards. Project page: https://nturobotlearninglab.github.io/DRAIL/ | Diffusion-Reward Adversarial Imitation Learning | [
"Chun-Mao Lai",
"Hsiang-Chun Wang",
"Ping-Chun Hsieh",
"Yu-Chiang Frank Wang",
"Min-Hung Chen",
"Shao-Hua Sun"
] | NeurIPS.cc/2024/Conference | 2405.16194 | [
""
] | https://huggingface.co/papers/2405.16194 | 1 | 1 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=k9PXsryuWG | @inproceedings{
chu2024metric,
title={Metric Transforms and Low Rank Representations of Kernels for Fast Attention},
author={Timothy Zer-An Chu and Josh Alman and Gary Miller and Shyam Narayanan and Mark Sellke and Zhao Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k9PXsryuWG}
} | We introduce a new linear-algebraic tool based on group representation theory, and use it to address three key problems in machine learning.
1. Past researchers have proposed fast attention algorithms for LLMs by approximating or replace softmax attention with other functions, such as low-degree polynomials. The key property of these functions is that, when applied entry-wise to the matrix $QK^{\top}$, the result is a low rank matrix when $Q$ and $K$ are $n \times d$ matrices and $n \gg d$. This suggests a natural question: what are all functions $f$ with this property? If other $f$ exist and are quickly computable, they can be used in place of softmax for fast subquadratic attention algorithms. It was previously known that low-degree polynomials have this property. We prove that low-degree polynomials are the only piecewise continuous functions with this property. This suggests that the low-rank fast attention only works for functions approximable by polynomials. Our work gives a converse to the polynomial method in algorithm design.
2. We prove the first full classification of all positive definite kernels that are functions of Manhattan or $\ell_1$ distance. Our work generalizes an existing theorem at the heart of all kernel methods in machine learning: the classification of all positive definite kernels that are functions of Euclidean distance.
3. The key problem in metric transforms, a mathematical theory used in geometry and machine learning, asks what functions transform pairwise distances in semi-metric space $M$ to semi-metric space $N$ for specified $M$ and $N$. We provide the first full classification of functions that transform Manhattan distances to Manhattan distances. Our work generalizes the foundational work of Schoenberg, which fully classifies functions that transform Euclidean to Euclidean distances.
We additionally prove results about stable-rank preserving functions that are potentially useful in algorithmic design, and more. Our core new tool is called the representation theory of the hyperrectangle. | Metric Transforms and Low Rank Representations of Kernels for Fast Attention | [
"Timothy Zer-An Chu",
"Josh Alman",
"Gary Miller",
"Shyam Narayanan",
"Mark Sellke",
"Zhao Song"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=k8AYft5ED1 | @inproceedings{
zhang2024understanding,
title={Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation},
author={Kaike Zhang and Qi Cao and Yunfan Wu and Fei Sun and Huawei Shen and Xueqi Cheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k8AYft5ED1}
} | Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filtering (CF) recommender systems against poisoning attacks. Besides, numerous studies have empirically shown that ACF can also improve recommendation performance compared to traditional CF. Despite these empirical successes, the theoretical understanding of ACF's effectiveness in terms of both performance and robustness remains unclear. To bridge this gap, in this paper, we first theoretically show that ACF can achieve a lower recommendation error compared to traditional CF with the same training epochs in both clean and poisoned data contexts. Furthermore, by establishing bounds for reductions in recommendation error during ACF's optimization process, we find that applying personalized magnitudes of perturbation for different users based on their embedding scales can further improve ACF's effectiveness. Building on these theoretical understandings, we propose Personalized Magnitude Adversarial Collaborative Filtering (PamaCF). Extensive experiments demonstrate that PamaCF effectively defends against various types of poisoning attacks while significantly enhancing recommendation performance. | Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation | [
"Kaike Zhang",
"Qi Cao",
"Yunfan Wu",
"Fei Sun",
"Huawei Shen",
"Xueqi Cheng"
] | NeurIPS.cc/2024/Conference | 2410.22844 | [
"https://github.com/Kaike-Zhang/PamaCF"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=k6m3y6qnSj | @inproceedings{
zhao2024illuminerf,
title={IllumiNe{RF}: 3D Relighting Without Inverse Rendering},
author={Xiaoming Zhao and Pratul P. Srinivasan and Dor Verbin and Keunhong Park and Ricardo Martin Brualla and Philipp Henzler},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k6m3y6qnSj}
} | Existing methods for relightable view synthesis --- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination --- are based on inverse rendering, and attempt to disentangle the object geometry, materials, and lighting that explain the input images. Furthermore, this typically involves optimization through differentiable Monte Carlo rendering, which is brittle and computationally-expensive. In this work, we propose a simpler approach: we first relight each input image using an image diffusion model conditioned on target environment lighting and estimated object geometry. We then reconstruct a Neural Radiance Field (NeRF) with these relit images, from which we render novel views under the target lighting. We demonstrate that this strategy is surprisingly competitive and achieves state-of-the-art results on multiple relighting benchmarks. Please see our project page at [illuminerf.github.io](illuminerf.github.io). | IllumiNeRF: 3D Relighting Without Inverse Rendering | [
"Xiaoming Zhao",
"Pratul P. Srinivasan",
"Dor Verbin",
"Keunhong Park",
"Ricardo Martin Brualla",
"Philipp Henzler"
] | NeurIPS.cc/2024/Conference | 2406.06527 | [
""
] | https://huggingface.co/papers/2406.06527 | 2 | 9 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=k6iyUfwdI9 | @inproceedings{
abbasi-yadkori2024to,
title={To Believe or Not to Believe Your {LLM}: IterativePrompting for Estimating Epistemic Uncertainty},
author={Yasin Abbasi-Yadkori and Ilja Kuzborskij and Andr{\'a}s Gy{\"o}rgy and Csaba Szepesvari},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k6iyUfwdI9}
} | We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers). In particular, we derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large, in which case the output of the model is unreliable. This condition can be computed based solely on the output of the model obtained simply by some special iterative prompting based on the previous responses. Such quantification, for instance, allows to detect hallucinations (cases when epistemic uncertainty is high) in both single- and multi-answer responses. This is in contrast to many standard uncertainty quantification strategies (such as thresholding the log-likelihood of a response) where hallucinations in the multi-answer case cannot be detected. We conduct a series of experiments which demonstrate the advantage of our formulation. Further, our investigations shed some light on how the probabilities assigned to a given output by an LLM can be amplified by iterative prompting, which might be of independent interest. | To Believe or Not to Believe Your LLM: IterativePrompting for Estimating Epistemic Uncertainty | [
"Yasin Abbasi-Yadkori",
"Ilja Kuzborskij",
"András György",
"Csaba Szepesvari"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=k6ZHvF1vkg | @inproceedings{
parisi2024beyond,
title={Beyond Optimism: Exploration With Partially Observable Rewards},
author={Simone Parisi and Alireza Kazemipour and Michael Bowling},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k6ZHvF1vkg}
} | Exploration in reinforcement learning (RL) remains an open challenge.
RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all.
To improve exploration and reward discovery, popular algorithms rely on optimism.
But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process?
In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty.
With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable.
We further propose a collection of tabular environments for benchmarking exploration in RL (with and without unobservable rewards) and show that our method outperforms existing ones. | Beyond Optimism: Exploration With Partially Observable Rewards | [
"Simone Parisi",
"Alireza Kazemipour",
"Michael Bowling"
] | NeurIPS.cc/2024/Conference | 2406.13909 | [
"https://github.com/AmiiThinks/mon_mdp_neurips24"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=k4EP46Q9X2 | @inproceedings{
huang2024unveiling,
title={Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators},
author={Yiyan HUANG and Cheuk Hang LEUNG and WANG Siyi and YIJUN LI and Qi WU},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k4EP46Q9X2}
} | The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal inference. However, selecting the desirable CATE estimator through a conventional model validation procedure remains impractical due to the absence of counterfactual outcomes in observational data. Existing approaches for CATE estimator selection, such as plug-in and pseudo-outcome metrics, face two challenges. First, they must determine the metric form and the underlying machine learning models for fitting nuisance parameters (e.g., outcome function, propensity function, and plug-in learner). Second, they lack a specific focus on selecting a robust CATE estimator. To address these challenges, this paper introduces a Distributionally Robust Metric (DRM) for CATE estimator selection. The proposed DRM is nuisance-free, eliminating the need to fit models for nuisance parameters, and it effectively prioritizes the selection of a distributionally robust CATE estimator. The experimental results validate the effectiveness of the DRM method in selecting CATE estimators that are robust to the distribution shift incurred by covariate shift and hidden confounders. | Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators | [
"Yiyan HUANG",
"Cheuk Hang LEUNG",
"WANG Siyi",
"YIJUN LI",
"Qi WU"
] | NeurIPS.cc/2024/Conference | 2402.18392 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=k2hS5Rt1N0 | @inproceedings{
guo2024offdynamics,
title={Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation},
author={Yihong Guo and Yixuan Wang and Yuanyuan Shi and Pan Xu and Anqi Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k2hS5Rt1N0}
} | Training a policy in a source domain for deployment in the target domain under a dynamics shift can be challenging, often resulting in performance degradation. Previous work tackles this challenge by training on the source domain with modified rewards derived by matching distributions between the source and the target optimal trajectories. However, pure modified rewards only ensure the behavior of the learned policy in the source domain resembles trajectories produced by the target optimal policies, which does not guarantee optimal performance when the learned policy is actually deployed to the target domain. In this work, we propose to utilize imitation learning to transfer the policy learned from the reward modification to the target domain so that the new policy can generate the same trajectories in the target domain. Our approach, Domain Adaptation and Reward Augmented Imitation Learning (DARAIL), utilizes the reward modification for domain adaptation and follows the general framework of generative adversarial imitation learning from observation (GAIfO) by applying a reward augmented estimator for the policy optimization step. Theoretically, we present an error bound for our method under a mild assumption regarding the dynamics shift to justify the motivation of our method. Empirically, our method outperforms the pure modified reward method without imitation learning and also outperforms other baselines in benchmark off-dynamics environments. | Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation | [
"Yihong Guo",
"Yixuan Wang",
"Yuanyuan Shi",
"Pan Xu",
"Anqi Liu"
] | NeurIPS.cc/2024/Conference | 2411.09891 | [
"https://github.com/guoyihonggyh/Off-Dynamics-Reinforcement-Learning-via-Domain-Adaptation-and-Reward-Augmented-Imitation"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=k29Iv0XrBF | @inproceedings{
guo2024physically,
title={Physically Compatible 3D Object Modeling from a Single Image},
author={Minghao Guo and Bohan Wang and Pingchuan Ma and Tianyuan Zhang and Crystal Elaine Owens and Chuang Gan and Joshua B. Tenenbaum and Kaiming He and Wojciech Matusik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k29Iv0XrBF}
} | We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming rigidity or neglecting external forces. Consequently, the reconstructed objects fail to withstand real-world physical forces, resulting in instability or undesirable deformation -- diverging from their intended designs as depicted in the image. Our optimization framework addresses this by embedding physical compatibility into the reconstruction process. We explicitly decompose the three physical attributes and link them through static equilibrium, which serves as a hard constraint, ensuring that the optimized physical shapes exhibit desired physical behaviors. Evaluations on a dataset collected from Objaverse demonstrate that our framework consistently enhances the physical realism of 3D models over existing methods. The utility of our framework extends to practical applications in dynamic simulations and 3D printing, where adherence to physical compatibility is paramount. | Physically Compatible 3D Object Modeling from a Single Image | [
"Minghao Guo",
"Bohan Wang",
"Pingchuan Ma",
"Tianyuan Zhang",
"Crystal Elaine Owens",
"Chuang Gan",
"Joshua B. Tenenbaum",
"Kaiming He",
"Wojciech Matusik"
] | NeurIPS.cc/2024/Conference | 2405.20510 | [
""
] | https://huggingface.co/papers/2405.20510 | 0 | 0 | 0 | 9 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=k1VrxRS6WZ | @inproceedings{
wang2024multilabel,
title={Multi-Label Open Set Recognition},
author={Yibo Wang and Jun-Yi Hang and Min-Ling Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=k1VrxRS6WZ}
} | In multi-label learning, each training instance is associated with multiple labels simultaneously. Traditional multi-label learning studies primarily focus on closed set scenario, i.e. the class label set of test data is identical to those used in training phase. Nevertheless, in numerous real-world scenarios, the environment is open and dynamic where unknown labels may emerge gradually during testing. In this paper, the problem of multi-label open set recognition (MLOSR) is investigated, which poses significant challenges in classifying and recognizing instances with unknown labels in multi-label setting. To enable open set multi-label prediction, a novel approach named SLAN is proposed by leveraging sub-labeling information enriched by structural information in the feature space. Accordingly, unknown labels are recognized by differentiating the sub-labeling information from holistic supervision. Experimental results on various datasets validate the effectiveness of the proposed approach in dealing with the MLOSR problem. | Multi-Label Open Set Recognition | [
"Yibo Wang",
"Jun-Yi Hang",
"Min-Ling Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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