mtybilly/Instruct2DS huggingface dataset:
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
Authors: Bingheng Li, Linxin Yang, Yupeng Chen, Senmiao Wang, Haitao Mao, Qian Chen, Yao Ma, Akang Wang, Tian Ding, Jiliang Tang, Ruoyu Sun
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Solving large-scale linear programming (LP) problems is an important task in various areas such as communication networks, power systems, finance and logistics. Recently, two distinct approaches have emerged to expedite LP solving: (i) First-order methods (FOMs); (ii) Learning to optimize (L2O). In this work, we propose an FOM-unrolled neural network (NN) called PDHG-Net, and propose a two-stage L2O method to solve large-scale LP problems. The new architecture PDHG-Net is designed by unrolling the recently emerged PDHG method into a neural network, combined with channel-expansion techniques borrowed from graph neural networks. We prove that the proposed PDHG-Net can recover PDHG algorithm, thus can approximate optimal solutions of LP instances with a polynomial number of neurons. We propose a two-stage inference approach: first use PDHG-Net to generate an approximate solution, and then apply PDHG algorithm to further improve the solution. Experiments show that our approach can significantly accelerate LP solving, achieving up to a 3$\times$ speedup compared to FOMs for large-scale LP problems.
IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation
Authors: Kai Li, Runxuan Yang, Fuchun Sun, Xiaolin Hu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Recent research has made significant progress in designing fusion modules for audio-visual speech separation. However, they predominantly focus on multi-modal fusion at a single temporal scale of auditory and visual features without employing selective attention mechanisms, which is in sharp contrast with the brain. To address this, We propose a novel model called intra- and inter-attention network (IIANet), which leverages the attention mechanism for efficient audio-visual feature fusion. IIANet consists of two types of attention blocks: intra-attention (IntraA) and inter-attention (InterA) blocks, where the InterA blocks are distributed at the top, middle and bottom of IIANet. Heavily inspired by the way how human brain selectively focuses on relevant content at various temporal scales, these blocks maintain the ability to learn modality-specific features and enable the extraction of different semantics from audio-visual features. Comprehensive experiments on three standard audio-visual separation benchmarks (LRS2, LRS3, and VoxCeleb2) demonstrate the effectiveness of IIANet, outperforming previous state-of-the-art methods while maintaining comparable inference time. In particular, the fast version of IIANet (IIANet-fast) has only 7% of CTCNet’s MACs and is 40% faster than CTCNet on CPUs while achieving better separation quality, showing the great potential of attention mechanism for efficient and effective multimodal fusion.
KernelWarehouse: Rethinking the Design of Dynamic Convolution
Authors: Chao Li, Anbang Yao
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Dynamic convolution learns a linear mixture of $n$ static kernels weighted with their input-dependent attentions, demonstrating superior performance than normal convolution. However, it increases the number of convolutional parameters by $n$ times, and thus is not parameter efficient. This leads to no research progress that can allow researchers to explore the setting $n > 100$ (an order of magnitude larger than the typical setting $n < 10$) for pushing forward the performance boundary of dynamic convolution while enjoying parameter efficiency. To fill this gap, in this paper, we propose KernelWarehouse, a more general form of dynamic convolution, which redefines the basic concepts of “kernels”, “assembling kernels” and “attention function” through the lens of exploiting convolutional parameter dependencies within the same layer and across neighboring layers of a ConvNet. We testify the effectiveness of KernelWarehouse on ImageNet and MS-COCO datasets using various ConvNet architectures. Intriguingly, KernelWarehouse is also applicable to Vision Transformers, and it can even reduce the model size of a backbone while improving the model accuracy. For instance, KernelWarehouse ($n = 4$) achieves 5.61%|3.90%|4.38% absolute top-1 accuracy gain on the ResNet18|MobileNetV2|DeiT-Tiny backbone, and KernelWarehouse ($n = 1/4$) with 65.10% model size reduction still achieves 2.29% gain on the ResNet18 backbone. The code and models are available at https://github.com/OSVAI/KernelWarehouse.
GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model
Authors: Ling Li, Yu Ye, Bingchuan Jiang, Wei Zeng
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: This work tackles the problem of geo-localization with a new paradigm using a large vision-language model (LVLM) augmented with human inference knowledge. A primary challenge here is the scarcity of data for training the LVLM - existing street-view datasets often contain numerous low-quality images lacking visual clues, and lack any reasoning inference. To address the data-quality issue, we devise a CLIP-based network to quantify the degree of street-view images being locatable, leading to the creation of a new dataset comprising highly locatable street views. To enhance reasoning inference, we integrate external knowledge obtained from real geo-localization games, tapping into valuable human inference capabilities. The data are utilized to train GeoReasoner, which undergoes fine-tuning through dedicated reasoning and location-tuning stages. Qualitative and quantitative evaluations illustrate that GeoReasoner outperforms counterpart LVLMs by more than 25% at country-level and 38% at city-level geo-localization tasks, and surpasses StreetCLIP performance while requiring fewer training resources. The data and code are available at https://github.com/lingli1996/GeoReasoner.
Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis
Authors: Yingying Li, Jing Yu, Lauren Conger, Taylan Kargin, Adam Wierman
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: This paper studies uncertainty set estimation for unknown linear systems. Uncertainty sets are crucial for the quality of robust control since they directly influence the conservativeness of the control design. Departing from the confidence region analysis of least squares estimation, this paper focuses on set membership estimation (SME). Though good numerical performances have attracted applications of SME in the control literature, the non-asymptotic convergence rate of SME for linear systems remains an open question. This paper provides the first convergence rate bounds for SME and discusses variations of SME under relaxed assumptions. We also provide numerical results demonstrating SME’s practical promise.
Seesaw: Compensating for Nonlinear Reduction with Linear Computations for Private Inference
Authors: Fabing Li, Yuanhao Zhai, Shuangyu Cai, Mingyu Gao
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: With increasingly serious data privacy concerns and strict regulations, privacy-preserving machine learning (PPML) has emerged to securely execute machine learning tasks without violating privacy. Unfortunately, the computational cost to securely execute nonlinear computations in PPML remains significant, calling for new model architecture designs with fewer nonlinear operations. We propose Seesaw, a novel neural architecture search method tailored for PPML. Seesaw exploits a previously unexplored opportunity to leverage more linear computations and nonlinear result reuse, in order to compensate for the accuracy loss due to nonlinear reduction. It incorporates specifically designed pruning and search strategies, not only to efficiently handle the much larger design space of both linear and nonlinear operators, but also to achieve a better balance between the model accuracy and the online/offline execution latencies. Compared to the state-of-the-art design for image classification on ImageNet, Seesaw achieves 1.68$\times$ lower online latency and 1.55$\times$ lower total online + offline latency at 71% iso-accuracy, or 3.65% higher accuracy at iso-latency of 190 seconds, while using much simpler and faster search and training methods.
Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms
Authors: Yichen Li, Chicheng Zhang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We study interactive imitation learning, where a learner interactively queries a demonstrating expert for action annotations, aiming to learn a policy that has performance competitive with the expert, using as few annotations as possible. We focus on the general agnostic setting where the expert demonstration policy may not be contained in the policy class used by the learner. We propose a new oracle-efficient algorithm MFTPL-P (abbreviation for Mixed Follow the Perturbed Leader with Poisson perturbations) with provable finite-sample guarantees, under the assumption that the learner is given access to samples from some “explorative” distribution over states. Our guarantees hold for any policy class, which is considerably broader than prior state of the art. We further propose Bootstrap-DAgger, a more practical variant that does not require additional sample access.
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Authors: Haoran Li, Zicheng Zhang, Wang Luo, Congying Han, Yudong Hu, Tiande Guo, Shichen Liao
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Establishing robust policies is essential to counter attacks or disturbances affecting deep reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness and suggest the potential lack of an optimal robust policy (ORP), posing challenges in setting strict robustness constraints. This work further investigates ORP: At first, we introduce a consistency assumption of policy (CAP) stating that optimal actions in the Markov decision process remain consistent with minor perturbations, supported by empirical and theoretical evidence. Building upon CAP, we crucially prove the existence of a deterministic and stationary ORP that aligns with the Bellman optimal policy. Furthermore, we illustrate the necessity of $L^ınfty$-norm when minimizing Bellman error to attain ORP. This finding clarifies the vulnerability of prior DRL algorithms that target the Bellman optimal policy with $L^1$-norm and motivates us to train a Consistent Adversarial Robust Deep Q-Network (CAR-DQN) by minimizing a surrogate of Bellman Infinity-error. The top-tier performance of CAR-DQN across various benchmarks validates its practical effectiveness and reinforces the soundness of our theoretical analysis.
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
Authors: Haoyu Li, Shichang Zhang, Longwen Tang, Mathieu Bauchy, Yizhou Sun
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Metallic Glasses (MGs) are widely used materials that are stronger than steel while being shapeable as plastic. While understanding the structure-property relationship of MGs remains a challenge in materials science, studying their energy barriers (EBs) as an intermediary step shows promise. In this work, we utilize Graph Neural Networks (GNNs) to model MGs and study EBs. We contribute a new dataset for EB prediction and a novel Symmetrized GNN (SymGNN) model that is E(3)-invariant in expectation. SymGNN handles invariance by aggregating over orthogonal transformations of the graph structure. When applied to EB prediction, SymGNN are more accurate than molecular dynamics (MD) local-sampling methods and other machine-learning models. Compared to precise MD simulations, SymGNN reduces the inference time on new MGs from roughly <b>41 days</b> to <b>less than one second</b>. We apply explanation algorithms to reveal the relationship between structures and EBs. The structures that we identify through explanations match the medium-range order (MRO) hypothesis and possess unique topological properties. Our work enables effective prediction and interpretation of MG EBs, bolstering material science research.
From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems
Authors: Xin Li, Jingdong Zhang, Qunxi Zhu, Chengli Zhao, Xue Zhang, Xiaojun Duan, Wei Lin
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-free framework, called Fourier NODEs (FNODEs), that effectively trains NODEs by directly matching the target vector field based on Fourier analysis. Specifically, we employ the Fourier analysis to estimate temporal and potential high-order spatial gradients from noisy observational data. We then incorporate the estimated spatial gradients as additional inputs to a neural network. Furthermore, we utilize the estimated temporal gradient as the optimization objective for the output of the neural network. Later, the trained neural network generates more data points through an ODE solver without participating in the computational graph, facilitating more accurate estimations of gradients based on Fourier analysis. These two steps form a positive feedback loop, enabling accurate dynamics modeling in our framework. Consequently, our approach outperforms state-of-the-art methods in terms of training time, dynamics prediction, and robustness. Finally, we demonstrate the superior performance of our framework using a number of representative complex systems.
Feel-Good Thompson Sampling for Contextual Dueling Bandits
Authors: Xuheng Li, Heyang Zhao, Quanquan Gu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Contextual dueling bandits, where a learner compares two options based on context and receives feedback indicating which was preferred, extends classic dueling bandits by incorporating contextual information for decision-making and preference learning. Several algorithms based on the upper confidence bound (UCB) have been proposed for linear contextual dueling bandits. However, no algorithm based on posterior sampling has been developed in this setting, despite the empirical success observed in traditional contextual bandits. In this paper, we propose a Thompson sampling algorithm, named FGTS.CDB, for linear contextual dueling bandits. At the core of our algorithm is a new Feel-Good exploration term specifically tailored for dueling bandits. This term leverages the independence of the two selected arms, thereby avoiding a cross term in the analysis. We show that our algorithm achieves nearly minimax-optimal regret, i.e., $\tilde\mathcalO(d\sqrt T)$, where $d$ is the model dimension and $T$ is the time horizon. Finally, we evaluate our algorithm on synthetic data and observe that FGTS.CDB outperforms existing algorithms by a large margin.
EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search
Authors: Pengyi Li, Yan Zheng, Hongyao Tang, Xian Fu, Jianye Hao
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Both Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) have demonstrated powerful capabilities in policy search with different principles. A promising direction is to combine the respective strengths of both for efficient policy optimization. To this end, many works have proposed various mechanisms to integrate EAs and RL. However, it is still unclear which of these mechanisms are complementary and can be fully combined. In this paper, we revisit different mechanisms from five perspectives: 1) Interaction Mode, 2) Individual Architecture, 3) EAs and operators, 4) Impact of EA on RL, and 5) Fitness Surrogate and Usage. We evaluate the effectiveness of each mechanism and experimentally analyze the reasons for the more effective mechanisms. Using the most effective mechanisms, we develop EvoRainbow and EvoRainbow-Exp, which outperform strong baselines and provide state-of-the-art performance across various tasks with distinct characteristics. To promote community development, we release the code on https://github.com/yeshenpy/EvoRainbow.
Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering
Authors: Haoxuan Li, Chunyuan Zheng, Shuyi Wang, Kunhan Wu, Eric Wang, Peng Wu, Zhi Geng, Xu Chen, Xiao-Hua Zhou
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Recommender system aims to recommend items or information that may interest users based on their behaviors and preferences. However, there may be sampling selection bias in the data collection process, i.e., the collected data is not a representative of the target population. Many debiasing methods are developed based on pseudo-labelings. Nevertheless, the validity of these methods relies heavily on accurate pseudo-labelings (i.e., the imputed labels), which is difficult to satisfy in practice. In this paper, we theoretically propose several novel doubly robust estimators that are unbiased when either (a) the pseudo-labelings deviate from the true labels with an arbitrary user-specific inductive bias, item-specific inductive bias, or a combination of both, or (b) the learned propensities are accurate. We further propose a propensity reconstruction learning approach that adaptively updates the constraint weights using an attention mechanism and effectively controls the variance. Extensive experiments show that our approach outperforms the state-of-the-art on one semi-synthetic and three real-world datasets.
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Authors: Jianxiong Li, Jinliang Zheng, Yinan Zheng, Liyuan Mao, Xiao Hu, Sijie Cheng, Haoyi Niu, Jihao Liu, Yu Liu, Jingjing Liu, Ya-Qin Zhang, Xianyuan Zhan
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Multimodal pretraining is an effective strategy for the trinity of goals of representation learning in autonomous robots: $1)$ extracting both local and global task progressions; $2)$ enforcing temporal consistency of visual representation; $3)$ capturing trajectory-level language grounding. Most existing methods approach these via separate objectives, which often reach sub-optimal solutions. In this paper, we propose a universal unified objective that can simultaneously extract meaningful task progression information from image sequences and seamlessly align them with language instructions. We discover that via implicit preferences, where a visual trajectory inherently aligns better with its corresponding language instruction than mismatched pairs, the popular Bradley-Terry model can transform into representation learning through proper reward reparameterizations. The resulted framework, DecisionNCE, mirrors an InfoNCE-style objective but is distinctively tailored for decision-making tasks, providing an embodied representation learning framework that elegantly extracts both local and global task progression features, with temporal consistency enforced through implicit time contrastive learning, while ensuring trajectory-level instruction grounding via multimodal joint encoding. Evaluation on both simulated and real robots demonstrates that DecisionNCE effectively facilitates diverse downstream policy learning tasks, offering a versatile solution for unified representation and reward learning. Project Page: https://2toinf.github.io/DecisionNCE/
Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses
Authors: Shaojie Li, Bowei Zhu, Yong Liu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: One of the central problems of statistical learning theory is quantifying the generalization ability of learning algorithms within a probabilistic framework. Algorithmic stability is a powerful tool for deriving generalization bounds, however, it typically builds on a critical assumption that losses are bounded. In this paper, we relax this condition to unbounded loss functions with subweibull diameter. This gives new generalization bounds for algorithmic stability and also includes existing results of subgaussian and subexponential diameters as specific cases. Furthermore, we provide a refined stability analysis by developing generalization bounds which can be $\sqrtn$-times faster than the previous results, where $n$ is the sample size. Our main technical contribution is general concentration inequalities for subweibull random variables, which may be of independent interest.
Kepler codebook
Authors: Junrong Lian, Ziyue Dong, Pengxu Wei, Wei Ke, Chang Liu, Qixiang Ye, Xiangyang Ji, Liang Lin
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: A codebook designed for learning discrete distributions in latent space has demonstrated state-of-the-art results on generation tasks. This inspires us to explore what distribution of codebook is better. Following the spirit of Kepler’s Conjecture, we cast the codebook training as solving the sphere packing problem and derive a Kepler codebook with a compact and structured distribution to obtain a codebook for image representations. Furthermore, we implement the Kepler codebook training by simply employing this derived distribution as regularization and using the codebook partition method. We conduct extensive experiments to evaluate our trained codebook for image reconstruction and generation on natural and human face datasets, respectively, achieving significant performance improvement. Besides, our Kepler codebook has demonstrated superior performance when evaluated across datasets and even for reconstructing images with different resolutions. Our trained models and source codes will be publicly released.
Receptive Fields As Experts in Convolutional Neural Architectures
Authors: Dongze Lian, Weihao Yu, Xinchao Wang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: The size of spatial receptive fields, from the early 3$\times$3 convolutions in VGGNet to the recent 7$\times$7 convolutions in ConvNeXt, has always played a critical role in architecture design. In this paper, we propose a Mixture of Receptive Fields (MoRF) instead of using a single receptive field. MoRF contains the combinations of multiple receptive fields with different sizes, e.g., convolutions with different kernel sizes, which can be regarded as experts. Such an approach serves two functions: one is to select the appropriate receptive field according to the input, and the other is to expand the network capacity. Furthermore, we also introduce two types of routing mechanisms, hard routing and soft routing to automatically select the appropriate receptive field experts. In the inference stage, the selected receptive field experts are merged via re-parameterization to maintain a similar inference speed compared to the single receptive field. To demonstrate the effectiveness of MoRF, we integrate the MoRF concept into multiple architectures, e.g., ResNet and ConvNeXt. Extensive experiments show that our approach outperforms the baselines in image classification, object detection, and segmentation tasks without significantly increasing the inference time.
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
Authors: Shijie Lian, Ziyi Zhang, Hua Li, Wenjie Li, Laurence Tianruo Yang, Sam Kwong, Runmin Cong
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance segmentation is a foundational and vital step for various underwater vision tasks, which often suffer from low segmentation accuracy due to the complex underwater circumstances and the adaptive ability of models. Moreover, the lack of large-scale datasets with pixel-level salient instance annotations has impeded the development of machine learning techniques in this field. To address these issues, we construct the first large-scale underwater salient instance segmentation dataset (USIS10K), which contains 10,632 underwater images with pixel-level annotations in 7 categories from various underwater scenes. Then, we propose an Underwater Salient Instance Segmentation architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain. We devise an Underwater Adaptive Visual Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts into the segmentation network. We further design an out-of-the-box underwater Salient Feature Prompter Generator (SFPG) to automatically generate salient prompters instead of explicitly providing foreground points or boxes as prompts in SAM. Comprehensive experimental results show that our USIS-SAM method can achieve superior performance on USIS10K datasets compared to the state-of-the-art methods. Datasets and codes are released on https://github.com/LiamLian0727/USIS10K.
Graph External Attention Enhanced Transformer
Authors: Jianqing Liang, Min Chen, Jiye Liang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works tend to overlook external information of graphs, specifically the correlation between graphs. Intuitively, graphs with similar structures should have similar representations. Therefore, we propose Graph External Attention (GEA) — a novel attention mechanism that leverages multiple external node/edge key-value units to capture inter-graph correlations implicitly. On this basis, we design an effective architecture called Graph External Attention Enhanced Transformer (GEAET), which integrates local structure and global interaction information for more comprehensive graph representations. Extensive experiments on benchmark datasets demonstrate that GEAET achieves state-of-the-art empirical performance. The source code is available for reproducibility at: https://github.com/icm1018/GEAET.
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
Authors: Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel Mcfarland, James Y. Zou
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: <em>ICLR</em> 2024, <em>NeurIPS</em> 2023, <em>CoRL</em> 2023 and <em>EMNLP</em> 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Authors: Langzhang Liang, Sunwoo Kim, Kijung Shin, Zenglin Xu, Shirui Pan, Yuan Qi
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in heterophilic graphs, where adjacent nodes exhibit dissimilar labels or features, Signed Message Passing (SMP) has been widely adopted. However, there is a lack of theoretical and empirical analysis regarding the limitations of SMP. In this work, we unveil the potential pitfalls of SMP and their remedies. We first identify two limitations of SMP: undesirable representation update for multi-hop neighbors and vulnerability against oversmoothing issues. To overcome these challenges, we propose a novel message-passing function called Multiset to Multiset GNN (M2M-GNN). Our theoretical analyses and extensive experiments demonstrate that M2M-GNN effectively alleviates the limitations of SMP, yielding superior performance in comparison.
Single-Trajectory Distributionally Robust Reinforcement Learning
Authors: Zhipeng Liang, Xiaoteng Ma, Jose Blanchet, Jun Yang, Jiheng Zhang, Zhengyuan Zhou
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: To mitigate the limitation that the classical reinforcement learning (RL) framework heavily relies on identical training and test environments, Distributionally Robust RL (DRRL) has been proposed to enhance performance across a range of environments, possibly including unknown test environments. As a price for robustness gain, DRRL involves optimizing over a set of distributions, which is inherently more challenging than optimizing over a fixed distribution in the non-robust case. Existing DRRL algorithms are either model-based or fail to learn from a single sample trajectory. In this paper, we design a first fully model-free DRRL algorithm, called distributionally robust Q-learning with single trajectory (DRQ). We delicately design a multi-timescale framework to fully utilize each incrementally arriving sample and directly learn the optimal distributionally robust policy without modeling the environment, thus the algorithm can be trained along a single trajectory in a model-free fashion. Despite the algorithm’s complexity, we provide asymptotic convergence guarantees by generalizing classical stochastic approximation tools.Comprehensive experimental results demonstrate the superior robustness and sample complexity of our proposed algorithm, compared to non-robust methods and other robust RL algorithms.
Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
Authors: Jian Liang, Lijun Sheng, Zhengbo Wang, Ran He, Tieniu Tan
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: The emergence of vision-language models, such as CLIP, has spurred a significant research effort towards their application for downstream supervised learning tasks. Although some previous studies have explored the unsupervised fine-tuning of CLIP, they often rely on prior knowledge in the form of class names associated with ground truth labels. This paper explores a realistic unsupervised fine-tuning scenario, considering the presence of out-of-distribution samples from unknown classes within the unlabeled data. In particular, we focus on simultaneously enhancing out-of-distribution detection and the recognition of instances associated with known classes. To tackle this problem, we present a simple, efficient, and effective approach called Universal Entropy Optimization (UEO). UEO leverages sample-level confidence to approximately minimize the conditional entropy of confident instances and maximize the marginal entropy of less confident instances. Apart from optimizing the textual prompt, UEO incorporates optimization of channel-wise affine transformations within the visual branch of CLIP. Extensive experiments across 15 domains and 4 different types of prior knowledge validate the effectiveness of UEO compared to baseline methods. The code is at https://github.com/tim-learn/UEO.
Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling
Authors: Yuanbang Liang, Jing Wu, Yu-Kun Lai, Yipeng Qin
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Despite impressive results, deep generative models require massive datasets for training, and as dataset size increases, effective evaluation metrics like precision and recall (P&R) become computationally infeasible on commodity hardware. In this paper, we address this challenge by proposing efficient P&R (eP&R) metrics that give almost identical results as the original P&R but with much lower computational costs. Specifically, we identify two redundancies in the original P&R: i) redundancy in ratio computation and ii) redundancy in manifold inside/outside identification. We find both can be effectively removed via hubness-aware sampling, which extracts representative elements from synthetic/real image samples based on their hubness values, i.e., the number of times a sample becomes a k-nearest neighbor to others in the feature space. Thanks to the insensitivity of hubness-aware sampling to exact k-nearest neighbor (k-NN) results, we further improve the efficiency of our eP&R metrics by using approximate k-NN methods. Extensive experiments show that our eP&R matches the original P&R but is far more efficient in time and space. Our code is available at: https://github.com/Byronliang8/Hubness_Precision_Recall
Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation
Authors: Weixuan Liang, En Zhu, Shengju Yu, Huiying Xu, Xinzhong Zhu, Xinwang Liu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: In this paper, we derive an upper bound of the difference between a kernel matrix and its expectation under a mild assumption. Specifically, we assume that the true distribution of the training data is an unknown isotropic Gaussian distribution. When the kernel function is a Gaussian kernel, and the mean of each cluster is sufficiently separated, we find that the expectation of a kernel matrix can be close to a rank-$k$ matrix, where $k$ is the cluster number. Moreover, we prove that the normalized kernel matrix of the training set deviates (w.r.t. Frobenius norm) from its expectation in the order of $\widetilde\mathcalO(1/\sqrtd)$, where $d$ is the dimension of samples. Based on the above theoretical results, we propose a novel multiple kernel clustering framework which attempts to learn the information of the expectation kernel matrices. First, we aim to minimize the distance between each base kernel and a rank-$k$ matrix, which is a proxy of the expectation kernel. Then, we fuse these rank-$k$ matrices into a consensus rank-$k$ matrix to find the clustering structure. Using an anchor-based method, the proposed framework is flexible with the sizes of input kernel matrices and able to handle large-scale datasets. We also provide the approximation guarantee by deriving two non-asymptotic bounds for the consensus kernel and clustering indicator matrices. Finally, we conduct extensive experiments to verify the clustering performance of the proposed method and the correctness of the proposed theoretical results.
On the Error-Propagation of Inexact Hotelling’s Deflation for Principal Component Analysis
Authors: Fangshuo Liao, Junhyung Lyle Kim, Cruz Barnum, Anastasios Kyrillidis
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Principal Component Analysis (PCA) aims to find subspaces spanned by the so-called <em>principal components</em> that best represent the variance in the dataset. The deflation method is a popular meta-algorithm that sequentially finds individual principal components, starting from the most important ones and working towards the less important ones. However, as deflation proceeds, numerical errors from the imprecise estimation of principal components propagate due to its sequential nature. This paper mathematically characterizes the error propagation of the inexact Hotelling’s deflation method. We consider two scenarios: $i)$ when the sub-routine for finding the leading eigenvector is abstract and can represent various algorithms; and $ii)$ when power iteration is used as the sub-routine. In the latter case, the additional directional information from power iteration allows us to obtain a tighter error bound than the sub-routine agnostic case. For both scenarios, we explicitly characterize how the errors progress and affect subsequent principal component estimations.
Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium
Authors: Luofeng Liao, Christian Kroer
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Linear Fisher market (LFM) is an equilibrium model for fair and efficient resource allocation, and first-price pacing equilibrium (FPPE) is a model for budget-management in first-price auctions. One thing they have in common is that both have a corresponding Eisenberg-Gale convex program characterization. In this paper, we introduce and devise several statistically valid bootstrap inference procedures for LFM and FPPE. The most challenging part is to bootstrap general FPPE, which reduces to bootstrapping constrained M-estimators, a largely unexplored problem. We are able to devise a bootstrap procedure for FPPE with structures by using the powerful tool of epi-convergence theory. Experiments with synthetic and semi-real data verify our theory.
Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning
Authors: Yun-Hsuan Lien, Ping-Chun Hsieh, Tzu-Mao Li, Yu-Shuen Wang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: In offline reinforcement learning (RL), updating the value function with the discrete-time Bellman Equation often encounters challenges due to the limited scope of available data. This limitation stems from the Bellman Equation, which cannot accurately predict the value of unvisited states. To address this issue, we have introduced an innovative solution that bridges the continuous- and discrete-time RL methods, capitalizing on their advantages. Our method uses a discrete-time RL algorithm to derive the value function from a dataset while ensuring that the function’s first derivative aligns with the local characteristics of states and actions, as defined by the Hamilton-Jacobi-Bellman equation in continuous RL. We provide practical algorithms for both deterministic policy gradient methods and stochastic policy gradient methods. Experiments on the D4RL dataset show that incorporating the first-order information significantly improves policy performance for offline RL problems.
Graph Geometry-Preserving Autoencoders
Authors: Jungbin Lim, Jihwan Kim, Yonghyeon Lee, Cheongjae Jang, Frank C. Park
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: When using an autoencoder to learn the low-dimensional manifold of high-dimensional data, it is crucial to find the latent representations that preserve the geometry of the data manifold. However, most existing studies assume a Euclidean nature for the high-dimensional data space, which is arbitrary and often does not precisely reflect the underlying semantic or domain-specific attributes of the data. In this paper, we propose a novel autoencoder regularization framework based on the premise that the geometry of the data manifold can often be better captured with a well-designed similarity graph associated with data points. Given such a graph, we utilize a Riemannian geometric distortion measure as a regularizer to preserve the geometry derived from the graph Laplacian and make it suitable for larger-scale autoencoder training. Through extensive experiments, we show that our method outperforms existing state-of-the-art geometry-preserving and graph-based autoencoders with respect to learning accurate latent structures that preserve the graph geometry, and is particularly effective in learning dynamics in the latent space. Code is available at https://github.com/JungbinLim/GGAE-public.
Momentum Particle Maximum Likelihood
Authors: Jen Ning Lim, Juan Kuntz, Samuel Power, Adam Michael Johansen
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Maximum likelihood estimation (MLE) of latent variable models is often recast as the minimization of a free energy functional over an extended space of parameters and probability distributions. This perspective was recently combined with insights from optimal transport to obtain novel particle-based algorithms for fitting latent variable models to data. Drawing inspiration from prior works which interpret ‘momentum-enriched’ optimization algorithms as discretizations of ordinary differential equations, we propose an analogous dynamical-systems-inspired approach to minimizing the free energy functional. The result is a dynamical system that blends elements of Nesterov’s Accelerated Gradient method, the underdamped Langevin diffusion, and particle methods. Under suitable assumptions, we prove that the continuous-time system minimizes the functional. By discretizing the system, we obtain a practical algorithm for MLE in latent variable models. The algorithm outperforms existing particle methods in numerical experiments and compares favourably with other MLE algorithms.
An Effective Dynamic Gradient Calibration Method for Continual Learning
Authors: Weichen Lin, Jiaxiang Chen, Ruomin Huang, Hu Ding
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Continual learning (CL) is a fundamental topic in machine learning, where the goal is to train a model with continuously incoming data and tasks. Due to the memory limit, we cannot store all the historical data, and therefore confront the “catastrophic forgetting” problem, i.e., the performance on the previous tasks can substantially decrease because of the missing information in the latter period. Though a number of elegant methods have been proposed, the catastrophic forgetting phenomenon still cannot be well avoided in practice. In this paper, we study the problem from the gradient perspective, where our aim is to develop an effective algorithm to calibrate the gradient in each updating step of the model; namely, our goal is to guide the model to be updated in the right direction under the situation that a large amount of historical data are unavailable. Our idea is partly inspired by the seminal stochastic variance reduction methods (e.g., SVRG and SAGA) for reducing the variance of gradient estimation in stochastic gradient descent algorithms. Another benefit is that our approach can be used as a general tool, which is able to be incorporated with several existing popular CL methods to achieve better performance. We also conduct a set of experiments on several benchmark datasets to evaluate the performance in practice.
Equivariant Diffusion for Crystal Structure Prediction
Authors: Peijia Lin, Pin Chen, Rui Jiao, Qing Mo, Cen Jianhuan, Wenbing Huang, Yang Liu, Dan Huang, Yutong Lu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process.
Revisiting the Role of Language Priors in Vision-Language Models
Authors: Zhiqiu Lin, Xinyue Chen, Deepak Pathak, Pengchuan Zhang, Deva Ramanan
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Vision-language models (VLMs) are impactful in part because they can be applied to a variety of visual understanding tasks in a zero-shot fashion, without any fine-tuning. We study $\textitgenerative VLMs$ that are trained for next-word generation given an image. We explore their zero-shot performance on the illustrative task of image-text retrieval across nine popular vision-language benchmarks. Our first observation is that they can be repurposed for discriminative tasks (such as image-text retrieval) by simply computing the match score of generating a particular text string given an image. We call this probabilistic score the Visual Generative Pre-Training Score (VisualGPTScore). While the VisualGPTScore produces near-perfect accuracy on some retrieval benchmarks, it yields poor accuracy on others. We analyze this behavior through a probabilistic lens, pointing out that some benchmarks inadvertently capture unnatural language distributions by creating adversarial but unlikely text captions. In fact, we demonstrate that even a "blind" language model that ignores any image evidence can sometimes outperform all prior art, reminiscent of similar challenges faced by the visual-question answering (VQA) community many years ago. We derive a probabilistic post-processing scheme that controls for the amount of linguistic bias in generative VLMs at test time without having to retrain or fine-tune the model. We show that the VisualGPTScore, when appropriately debiased, is a strong zero-shot baseline for vision-language understanding, oftentimes producing state-of-the-art accuracy.