GenAI4ELab/papercli-papers huggingface dataset:
Learning the Latent Causal Structure for Modeling Label Noise
Authors: Yexiong Lin; Yu Yao; Tongliang Liu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In label-noise learning, the noise transition matrix reveals how an instance transitions from its clean label to its noisy label. Accurately estimating an instance's noise transition matrix is crucial for estimating its clean label. However, when only a noisy dataset is available, noise transition matrices can be estimated only for some "special" instances. To leverage these estimated transition matrices to help estimate the transition matrices of other instances, it is essential to explore relations between the matrices of these "special" instances and those of others. Existing studies typically build the relation by explicitly defining the similarity between the estimated noise transition matrices of "special" instances and those of other instances. However, these similarity-based assumptions are hard to validate and may not align with real-world data. If these assumptions fail, both noise transition matrices and clean labels cannot be accurately estimated. In this paper, we found that by learning the latent causal structure governing the generating process of noisy data, we can estimate noise transition matrices without the need for similarity-based assumptions. Unlike previous generative label-noise learning methods, we consider causal relations between latent causal variables and model them with a learnable graphical model. Utilizing only noisy data, our method can effectively learn the latent causal structure. Experimental results on various noisy datasets demonstrate that our method achieves state-of-the-art performance in estimating noise transition matrices, which leads to improved classification accuracy. The code is available at: https://github.com/tmllab/2024_NeurIPS_CSGN.
DiffSF: Diffusion Models for Scene Flow Estimation
Authors: Yushan Zhang; Bastian Wandt; Maria Magnusson; Michael Felsberg
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Scene flow estimation is an essential ingredient for a variety of real-world applications, especially for autonomous agents, such as self-driving cars and robots. While recent scene flow estimation approaches achieve reasonable accuracy, their applicability to real-world systems additionally benefits from a reliability measure. Aiming at improving accuracy while additionally providing an estimate for uncertainty, we propose DiffSF that combines transformer-based scene flow estimation with denoising diffusion models. In the diffusion process, the ground truth scene flow vector field is gradually perturbed by adding Gaussian noise. In the reverse process, starting from randomly sampled Gaussian noise, the scene flow vector field prediction is recovered by conditioning on a source and a target point cloud. We show that the diffusion process greatly increases the robustness of predictions compared to prior approaches resulting in state-of-the-art performance on standard scene flow estimation benchmarks. Moreover, by sampling multiple times with different initial states, the denoising process predicts multiple hypotheses, which enables measuring the output uncertainty, allowing our approach to detect a majority of the inaccurate predictions. The code is available at https://github.com/ZhangYushan3/DiffSF.
Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
Authors: Qinbo Bai; Washim Uddin Mondal; Vaneet Aggarwal
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDPs). To the best of our knowledge, this work is the first to delve into the regret and constraint violation analysis of average reward CMDPs with a general policy parametrization. To address this challenge, we propose a primal dual-based policy gradient algorithm that adeptly manages the constraints while ensuring a low regret guarantee toward achieving a global optimal policy. In particular, our proposed algorithm achieves $\tilde{\mathcal{O}}({T}^{4/5})$ objective regret and $\tilde{\mathcal{O}}({T}^{4/5})$ constraint violation bounds.
GRANOLA: Adaptive Normalization for Graph Neural Networks
Authors: Moshe Eliasof; Beatrice Bevilacqua; Carola-Bibiane Schönlieb; Haggai Maron
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Despite the widespread adoption of Graph Neural Networks (GNNs), these models often incorporate off-the-shelf normalization layers like BatchNorm or InstanceNorm, which were not originally designed for GNNs. Consequently, these normalization layers may not effectively capture the unique characteristics of graph-structured data, potentially even weakening the expressive power of the overall architecture. 
While existing graph-specific normalization layers have been proposed, they often struggle to offer substantial and consistent benefits. In this paper, we propose GRANOLA, a novel graph-adaptive normalization layer. Unlike existing normalization layers, GRANOLA normalizes node features by adapting to the specific characteristics of the graph, particularly by generating expressive representations of its nodes, obtained by leveraging the propagation of Random Node Features (RNF) in the graph. We provide theoretical results that support our design choices as well as an extensive empirical evaluation demonstrating the superior performance of GRANOLA over existing normalization techniques. Furthermore, GRANOLA emerges as the top-performing method among all baselines in the same time complexity class of Message Passing Neural Networks (MPNNs).
NeuMA: Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics
Authors: Junyi Cao; Shanyan Guan; Yanhao Ge; Wei Li; Xiaokang Yang; Chao Ma
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: While humans effortlessly discern intrinsic dynamics and adapt to new scenarios, modern AI systems often struggle. Current methods for visual grounding of dynamics either use pure neural-network-based simulators (black box), which may violate physical laws, or traditional physical simulators (white box), which rely on expert-defined equations that may not fully capture actual dynamics. We propose the Neural Material Adaptor (NeuMA), which integrates existing physical laws with learned corrections, facilitating accurate learning of actual dynamics while maintaining the generalizability and interpretability of physical priors. Additionally, we propose Particle-GS, a particle-driven 3D Gaussian Splatting variant that bridges simulation and observed images, allowing back-propagate image gradients to optimize the simulator. Comprehensive experiments on various dynamics in terms of grounded particle accuracy, dynamic rendering quality, and generalization ability demonstrate that NeuMA can accurately capture intrinsic dynamics. Project Page: https://xjay18.github.io/projects/neuma.html.
Improved Regret for Bandit Convex Optimization with Delayed Feedback
Authors: Yuanyu Wan; Chang Yao; Mingli Song; Lijun Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We investigate bandit convex optimization (BCO) with delayed feedback, where only the loss value of the action is revealed under an arbitrary delay. Let $n,T,\bar{d}$ denote the dimensionality, time horizon, and average delay, respectively. Previous studies have achieved an $O(\sqrt{n}T^{3/4}+(n\bar{d})^{1/3}T^{2/3})$ regret bound for this problem, whose delay-independent part matches the regret of the classical non-delayed bandit gradient descent algorithm. However, there is a large gap between its delay-dependent part, i.e., $O((n\bar{d})^{1/3}T^{2/3})$, and an existing $\Omega(\sqrt{\bar{d}T})$ lower bound. In this paper, we illustrate that this gap can be filled in the worst case, where $\bar{d}$ is very close to the maximum delay $d$. Specifically, we first develop a novel algorithm, and prove that it enjoys a regret bound of $O(\sqrt{n}T^{3/4}+\sqrt{dT})$ in general. Compared with the previous result, our regret bound is better for $d=O((n\bar{d})^{2/3}T^{1/3})$, and the delay-dependent part is tight in the worst case. The primary idea is to decouple the joint effect of the delays and the bandit feedback on the regret by carefully incorporating the delayed bandit feedback with a blocking update mechanism. Furthermore, we show that the proposed algorithm can improve the regret bound to $O((nT)^{2/3}\log^{1/3}T+d\log T)$ for strongly convex functions. Finally, if the action sets are unconstrained, we demonstrate that it can be simply extended to achieve an $O(n\sqrt{T\log T}+d\log T)$ regret bound for strongly convex and smooth functions.
Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
Authors: Nazar Buzun; Maksim Bobrin; Dmitry V. Dylov
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularization on dual Kantorovich potentials. The main bottleneck of existing NOT solvers is associated with the procedure of finding a near-exact approximation of the conjugate operator (i.e., the c-transform), which is done either by optimizing over non-convex max-min objectives or by the computationally intensive fine-tuning of the initial approximated prediction. We resolve both issues by proposing a new theoretically justified loss in the form of expectile regularization which enforces binding conditions on the learning process of the dual potentials. Such a regularization provides the upper bound estimation over the distribution of possible conjugate potentials and makes the learning stable, completely eliminating the need for additional extensive fine-tuning. Proposed method, called Expectile-Regularized Neural Optimal Transport (ENOT), outperforms previous state-of-the-art approaches in the established Wasserstein-2 benchmark tasks by a large margin (up to a 3-fold improvement in quality and up to a 10-fold improvement in runtime). Moreover, we showcase performance of ENOT for various cost functions in different tasks, such as image generation, demonstrating generalizability and robustness of the proposed algorithm.
Sample-Efficient Constrained Reinforcement Learning with General Parameterization
Authors: Washim Uddin Mondal; Vaneet Aggarwal
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We consider a constrained Markov Decision Problem (CMDP) where the goal of an agent is to maximize the expected discounted sum of rewards over an infinite horizon while ensuring that the expected discounted sum of costs exceeds a certain threshold. Building on the idea of momentum-based acceleration, we develop the Primal-Dual Accelerated Natural Policy Gradient (PD-ANPG) algorithm that ensures an $\epsilon$ global optimality gap and $\epsilon$ constraint violation with $\tilde{\mathcal{O}}((1-\gamma)^{-7}\epsilon^{-2})$ sample complexity for general parameterized policies where $\gamma$ denotes the discount factor. This improves the state-of-the-art sample complexity in general parameterized CMDPs by a factor of $\mathcal{O}((1-\gamma)^{-1}\epsilon^{-2})$ and achieves the theoretical lower bound in $\epsilon^{-1}$.
CLIP in Mirror: Disentangling text from visual images through reflection
Authors: Tiancheng Wang; Yuguang Yang; Linlin Yang; Shaohui Lin; Juan Zhang; Guodong Guo; Baochang Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The CLIP network excels in various tasks, but struggles with text-visual images i.e., images that contain both text and visual objects; it risks confusing textual and visual representations. To address this issue, we propose MirrorCLIP, a zero-shot framework, which disentangles the image features of CLIP by exploiting the difference in the mirror effect between visual objects and text in the images. Specifically, MirrorCLIP takes both original and flipped images as inputs, comparing their features dimension-wise in the latent space to generate disentangling masks. With disentangling masks, we further design filters to separate textual and visual factors more precisely, and then get disentangled representations. Qualitative experiments using stable diffusion models and class activation mapping (CAM) validate the effectiveness of our disentanglement. Moreover, our proposed MirrorCLIP reduces confusion when encountering text-visual images and achieves a substantial improvement on typographic defense, further demonstrating its superior ability of disentanglement. Our code is available at https://github.com/tcwangbuaa/MirrorCLIP
Spiking Graph Neural Network on Riemannian Manifolds
Authors: Li Sun; Zhenhao Huang; Qiqi Wan; Hao Peng; Philip S. Yu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Graph neural networks (GNNs) have become the dominant solution for learning on graphs, the typical non-Euclidean structures. Conventional GNNs, constructed with the Artificial Neuron Network (ANN), have achieved impressive performance at the cost of high computation and energy consumption. In parallel, spiking GNNs with brain-like spiking neurons are drawing increasing research attention owing to the energy efficiency. So far, existing spiking GNNs consider graphs in Euclidean space, ignoring the structural geometry, and suffer from the high latency issue due to Back-Propagation-Through-Time (BPTT) with the surrogate gradient. In light of the aforementioned issues, we are devoted to exploring spiking GNN on Riemannian manifolds, and present a Manifold-valued Spiking GNN (MSG). In particular, we design a new spiking neuron on geodesically complete manifolds with the diffeomorphism, so that BPTT regarding the spikes is replaced by the proposed differentiation via manifold. Theoretically, we show that MSG  approximates a solver of the manifold ordinary differential equation. Extensive experiments on common graphs show the proposed MSG achieves superior performance to previous spiking GNNs and energy efficiency to conventional GNNs.
Causal Dependence Plots
Authors: Joshua R. Loftus; Lucius E.J. Bynum; Sakina Hansen
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: To use artificial intelligence and machine learning models wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we develop Causal Dependence Plots (CDPs) to visualize how a model's predicted outcome depends on changes in a given predictor *along with consequent causal changes in other predictor variables*. Crucially, this differs from standard methods based on independence or holding other predictors constant, such as regression coefficients or Partial Dependence Plots (PDPs). Our explanatory framework generalizes PDPs, including them as a special case, as well as a variety of other interpretive plots that show, for example, the total, direct, and indirect effects of causal mediation. We demonstrate with simulations and real data experiments how CDPs can be combined in a modular way with methods for causal learning or sensitivity analysis. Since people often think causally about input-output dependence, CDPs can be powerful tools in the xAI or interpretable machine learning toolkit and contribute to applications like scientific machine learning and algorithmic fairness.
Trade-Offs of Diagonal Fisher Information Matrix Estimators
Authors: Alexander Soen; Ke Sun
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The Fisher information matrix can be used to characterize the local geometry of
the parameter space of neural networks. It elucidates insightful theories and
useful tools to understand and optimize neural networks. Given its high
computational cost, practitioners often use random estimators and evaluate only
the diagonal entries. We examine two popular estimators whose accuracy and sample
complexity depend on their associated variances. We derive bounds of the
variances and instantiate them in neural networks for regression and
classification. We navigate trade-offs for both estimators based on analytical
and numerical studies. We find that the variance quantities depend on the
non-linearity w.r.t. different parameter groups and should not be neglected when
estimating the Fisher information.
Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
Authors: Bingcong Li; Liang Zhang; Niao He
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Sharpness-aware minimization (SAM) improves generalization of various deep learning tasks. Motivated by popular architectures such as LoRA, we explore the implicit regularization of SAM for scale-invariant problems involving two groups of variables. Instead of focusing on commonly used sharpness, this work introduces a concept termed *balancedness*, defined as the difference between the squared norm of two variables. This allows us to depict richer global behaviors of SAM. In particular, our theoretical and empirical findings reveal that i) SAM promotes balancedness; and ii) the regularization on balancedness is *data-responsive* -- outliers have stronger impact. 
The latter coincides with empirical observations that SAM outperforms SGD in the presence of outliers. 
Leveraging the implicit regularization, we develop a resource-efficient SAM variant, balancedness-aware regularization (BAR), tailored for scale-invariant problems such as finetuning language models with LoRA. BAR saves 95% computational overhead of SAM, with enhanced test performance across various tasks on RoBERTa, GPT2, and OPT-1.3B.
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
Authors: Haoran Que; Jiaheng Liu; Ge Zhang; Chenchen Zhang; Xingwei Qu; Yinghao Ma; Feiyu Duan; ZhiqiBai; JiakaiWang; Yuanxing Zhang; Xu Tan; Jie Fu; Jiamang Wang; Lin Qu; Wenbo Su; Bo Zheng
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Continual Pre-Training (CPT) on Large Language Models (LLMs) has been widely used to expand the model’s fundamental understanding of specific downstream domains (e.g., math and code). For the CPT on domain-specific LLMs, one important question is how to choose the optimal mixture ratio between the general-corpus (e.g., Dolma, Slim-pajama) and the downstream domain-corpus. Existing methods usually adopt laborious human efforts by grid-searching on a set of mixture ratios, which require high GPU training consumption costs. Besides, we cannot guarantee the selected ratio is optimal for the specific domain. To address the limitations of existing methods, inspired by the Scaling Law for performance prediction, we propose to investigate the Scaling Law of the Domain-specific Continual Pre-Training (D-CPT Law) to decide the optimal mixture ratio with acceptable training costs for LLMs of different sizes. Specifically, by fitting the D-CPT Law, we can easily predict the general and downstream performance of arbitrary mixture ratios, model sizes, and dataset sizes using small-scale training costs on limited experiments. Moreover, we also extend our standard D-CPT Law on cross-domain settings and propose the Cross-Domain D-CPT Law to predict the D-CPT law of target domains, where very small training costs (about 1\% of the normal training costs) are needed for the target domains. Comprehensive experimental results on six downstream domains demonstrate the effectiveness and generalizability of our proposed D-CPT Law and Cross-Domain D-CPT Law.
A Framework for Bilevel Optimization on Riemannian Manifolds
Authors: Andi Han; Bamdev Mishra; Pratik Jawanpuria; Akiko Takeda
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Bilevel optimization has gained prominence in various applications. In this study, we introduce a framework for solving bilevel optimization problems, where the variables in both the lower and upper levels are constrained on Riemannian manifolds. We present several hypergradient estimation strategies on manifolds and analyze their estimation errors. Furthermore, we provide comprehensive convergence and complexity analyses for the proposed hypergradient descent algorithm on manifolds. We also extend our framework to encompass stochastic bilevel optimization and incorporate the use of general retraction. The efficacy of the proposed framework is demonstrated through several applications.
Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
Authors: Huy Nguyen; Nhat Ho; Alessandro Rinaldo
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The softmax gating function is arguably the most popular choice in mixture of experts modeling. Despite its widespread use in practice, the softmax gating may lead to unnecessary competition among experts, potentially causing the undesirable phenomenon of representation collapse due to its inherent structure. In response, the sigmoid gating function has been recently proposed as an alternative and has been demonstrated empirically to achieve superior performance. However, a rigorous examination of the sigmoid gating function is lacking in current literature. In this paper, we verify theoretically that the sigmoid gating, in fact, enjoys a higher sample efficiency than the softmax gating for the statistical task of expert estimation. Towards that goal, we consider a regression framework in which the unknown regression function is modeled as a mixture of experts, and study the rates of convergence of the least squares estimator under the over-specified case in which the number of fitted experts is larger than the true value. We show that two gating regimes naturally arise and, in each of them, we formulate an identifiability condition for the expert functions and derive the corresponding convergence rates. In both cases, we find that experts formulated as feed-forward networks with commonly used activation such as $\mathrm{ReLU}$ and $\mathrm{GELU}$ enjoy faster convergence rates under the sigmoid gating than those under softmax gating. Furthermore, given the same choice of experts, we demonstrate that the sigmoid gating function requires a smaller sample size than its softmax counterpart to attain the same error of expert estimation and, therefore, is more sample efficient.
Learning Social Welfare Functions
Authors: Kanad Shrikar Pardeshi; Itai Shapira; Ariel D. Procaccia; Aarti Singh
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Is it possible to understand or imitate a policy maker's rationale by looking at past decisions they made? We formalize this question as the problem of learning social welfare functions belonging to the well-studied family of power mean functions. We focus on two learning tasks; in the first, the input is vectors of utilities of an action (decision or policy) for individuals in a group and their associated social welfare as judged by a policy maker, whereas in the second, the input is pairwise comparisons between the welfares associated with a given pair of utility vectors. We show that power mean functions are learnable with polynomial sample complexity in both cases, even if the social welfare information is noisy. Finally, we design practical algorithms for these tasks and evaluate their performance.