mtybilly/Instruct2DS huggingface dataset:
Recovering Labels from Local Updates in Federated Learning
Authors: Chen, Huancheng and Vikalo, Haris
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients’ data from communicated model updates. A number of such techniques attempts to accelerate data recovery by first reconstructing labels of the samples used in local training. However, existing label extraction methods make strong assumptions that typically do not hold in realistic FL settings. In this paper we present a novel label recovery scheme, Recovering Labels from Local Updates (RLU), which provides near-perfect accuracy when attacking untrained (most vulnerable) models. More significantly, RLU achieves high performance even in realistic real-world settings where the clients in an FL system run multiple local epochs, train on heterogeneous data, and deploy various optimizers to minimize different objective functions. Specifically, RLU estimates labels by solving a least-square problem that emerges from the analysis of the correlation between labels of the data points used in a training round and the resulting update of the output layer. The experimental results on several datasets, architectures, and data heterogeneity scenarios demonstrate that the proposed method consistently outperforms existing baselines, and helps improve quality of the reconstructed images in GI attacks in terms of both PSNR and LPIPS.
SelfIE: Self-Interpretation of Large Language Model Embeddings
Authors: Chen, Haozhe and Vondrick, Carl and Mao, Chengzhi
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: How do large language models (LLMs) obtain their answers? The ability to explain and control an LLM’s reasoning process is key for reliability, transparency, and future model developments. We propose SelfIE (Self-Interpretation of Embeddings), a framework that enables LLMs to interpret their own embeddings in natural language by leveraging their ability to respond to inquiries about a given passage. Capable of interpreting open-world concepts in the hidden embeddings, SelfIE reveals LLM internal reasoning in cases such as making ethical decisions, internalizing prompt injection, and recalling harmful knowledge. SelfIE’s text descriptions on hidden embeddings open avenues to control LLM reasoning. We propose Supervised Control, which allows editing open-ended concepts while only requiring gradient computation of individual layer. We extend RLHF to hidden embeddings and propose Reinforcement Control that erases harmful knowledge in LLM without supervision targets.
Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
Authors: Chen, Ziqin and Wang, Yongqiang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Decentralized bilevel optimization based machine learning techniques are achieving remarkable success in a wide variety of domains. However, the intensive exchange of information (involving nested-loops of consensus or communication iterations) in existing decentralized bilevel optimization algorithms leads to a great challenge to ensure rigorous differential privacy, which, however, is necessary to bring the benefits of machine learning to domains where involved data are sensitive. By proposing a new decentralized stochastic bilevel-optimization algorithm which avoids nested-loops of information-exchange iterations, we achieve, for the first time, both differential privacy and accurate convergence in decentralized bilevel optimization. This is significant since even for single-level decentralized optimization and learning, existing differential-privacy solutions have to sacrifice convergence accuracy for privacy. Besides characterizing the convergence rate under nonconvex/convex/strongly convex conditions, we also rigorously quantify the price of differential privacy in the convergence rate. Experimental results on machine learning models confirm the efficacy of our algorithm.
Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments
Authors: Chen, Runfa and Wang, Ling and Du, Yu and Xue, Tianrui and Sun, Fuchun and Zhang, Jianwei and Huang, Wenbing
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Learning policies for multi-entity systems in 3D environments is far more complicated against single-entity scenarios, due to the exponential expansion of the global state space as the number of entities increases. One potential solution of alleviating the exponential complexity is dividing the global space into independent local views that are invariant to transformations including translations and rotations. To this end, this paper proposes <em>Subequivariant Hierarchical Neural Networks</em> (SHNN) to facilitate multi-entity policy learning. In particular, SHNN first dynamically decouples the global space into local entity-level graphs via task assignment. Second, it leverages subequivariant message passing over the local entity-level graphs to devise local reference frames, remarkably compressing the representation redundancy, particularly in gravity-affected environments. Furthermore, to overcome the limitations of existing benchmarks in capturing the subtleties of multi-entity systems under the Euclidean symmetry, we propose the <em>Multi-entity Benchmark</em> (MEBEN), a new suite of environments tailored for exploring a wide range of multi-entity reinforcement learning. Extensive experiments demonstrate significant advancements of SHNN on the proposed benchmarks compared to existing methods. Comprehensive ablations are conducted to verify the indispensability of task assignment and subequivariance.
A General Framework for Learning from Weak Supervision
Authors: Chen, Hao and Wang, Jindong and Feng, Lei and Li, Xiang and Wang, Yidong and Xie, Xing and Sugiyama, Masashi and Singh, Rita and Raj, Bhiksha
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment. This paper introduces a general framework for learning from weak supervision (GLWS) with a novel algorithm. Central to GLWS is an Expectation-Maximization (EM) formulation, adeptly accommodating various weak supervision sources, including instance partial labels, aggregate statistics, pairwise observations, and unlabeled data. We further present an advanced algorithm that significantly simplifies the EM computational demands using a Non-deterministic Finite Automaton (NFA) along with a forward-backward algorithm, which effectively reduces time complexity from quadratic or factorial often required in existing solutions to linear scale. The problem of learning from arbitrary weak supervision is therefore converted to the NFA modeling of them. GLWS not only enhances the scalability of machine learning models but also demonstrates superior performance and versatility across 11 weak supervision scenarios. We hope our work paves the way for further advancements and practical deployment in this field.
Diffusion Model-Augmented Behavioral Cloning
Authors: Chen, Shang-Fu and Wang, Hsiang-Chun and Hsu, Ming-Hao and Lai, Chun-Mao and Sun, Shao-Hua
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Imitation learning addresses the challenge of learning by observing an expert’s demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s, a). Despite the simplicity of modeling the conditional probability with BC, it usually struggles with generalization. While modeling the joint probability can improve generalization performance, the inference procedure is often time-consuming, and the model can suffer from manifold overfitting. This work proposes an imitation learning framework that benefits from modeling both the conditional and joint probability of the expert distribution. Our proposed Diffusion Model-Augmented Behavioral Cloning (DBC) employs a diffusion model trained to model expert behaviors and learns a policy to optimize both the BC loss (conditional) and our proposed diffusion model loss (joint). DBC outperforms baselines in various continuous control tasks in navigation, robot arm manipulation, dexterous manipulation, and locomotion. We design additional experiments to verify the limitations of modeling either the conditional probability or the joint probability of the expert distribution, as well as compare different generative models. Ablation studies justify the effectiveness of our design choices.
AutoOS: Make Your OS More Powerful by Exploiting Large Language Models
Authors: Chen, Huilai and Wen, Yuanbo and Cheng, Limin and Kuang, Shouxu and Liu, Yumeng and Li, Weijia and Li, Ling and Zhang, Rui and Song, Xinkai and Li, Wei and Guo, Qi and Chen, Yunji
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: With the rapid development of Artificial Intelligence of Things (AIoT), customizing and optimizing operating system (OS) kernel configurations for various AIoT application scenarios is crucial for maximizing system performance. However, existing approaches falter due to the overwhelming problem complexity (i.e., over 15,000 configuration options in the Linux kernel), together with the huge evaluation costs and error-prone options that may result in OS boot-up failure, which all make it an unresolved problem to optimize the Linux kernel automatically. In this paper, we introduce AutoOS, a novel framework exploiting Large Language Models for customizing and optimizing OS kernel configurations automatically for various AIoT application scenarios.Inspired by the inherently directory-structured kernel configuration process, we first formulate our research problem as optimizing on a dynamic tree. We then propose a novel framework integrating a state machine-based traversal algorithm as the observe-prune-propose-act-correct loop, which can effectively refine the optimization space and ensure a successful OS boot-up.Experimental results show that AutoOS can automatically customize and optimize the OS kernel configurations without human effort. More importantly, AutoOS even achieves better performance by up to 25% than vendor-provided configuration.
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
Authors: Chen, Junfeng and Wu, Kailiang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Operator learning for Partial Differential Equations (PDEs) is rapidly emerging as a promising approach for surrogate modeling of intricate systems. Transformers with the self-attention mechanism—a powerful tool originally designed for natural language processing—have recently been adapted for operator learning. However, they confront challenges, including high computational demands and limited interpretability. This raises a critical question: <em>Is there a more efficient attention mechanism for Transformer-based operator learning?</em> This paper proposes the Position-induced Transformer (PiT), built on an innovative position-attention mechanism, which demonstrates significant advantages over the classical self-attention in operator learning. Position-attention draws inspiration from numerical methods for PDEs. Different from self-attention, position-attention is induced by only the spatial interrelations of sampling positions for input functions of the operators, and does not rely on the input function values themselves, thereby greatly boosting efficiency. PiT exhibits superior performance over current state-of-the-art neural operators in a variety of complex operator learning tasks across diverse PDE benchmarks. Additionally, PiT possesses an enhanced discretization convergence feature, compared to the widely-used Fourier neural operator.
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
Authors: Chen, Shiqi and Xiong, Miao and Liu, Junteng and Wu, Zhengxuan and Xiao, Teng and Gao, Siyang and He, Junxian
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Large language models (LLMs) frequently hallucinate, e.g., making factual errors, yet our understanding of why they make these errors remains limited. In this study, we aim to understand the underlying mechanisms of LLM hallucinations from the perspective of <em>inner representations</em>. We discover a pattern associated with hallucinations: correct generations tend to have <em>sharper</em> context activations in the hidden states of the in-context tokens, compared to that of the incorrect generations. Leveraging this signal, we propose an entropy-based metric to quantify the <em>sharpness</em> among the in-context hidden states and incorporate it into the decoding process, i.e, use the entropy value to adjust the next token prediction distribution to improve the factuality and overall quality of the generated text. Experiments on knowledge-seeking datasets (Natural Questions, HotpotQA, TriviaQA) and hallucination benchmark (TruthfulQA) demonstrate our consistent effectiveness, e.g., up to 8.6 absolute points on TruthfulQA. We believe this study can improve our understanding of hallucinations and serve as a practical solution for hallucination mitigation.
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
Authors: Chen, Anthony and Yang, Huanrui and Gan, Yulu and Gudovskiy, Denis A and Dong, Zhen and Wang, Haofan and Okuno, Tomoyuki and Nakata, Yohei and Keutzer, Kurt and Zhang, Shanghang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Uncertainty estimation is crucial for deep learning models to detect out-of-distribution (OOD) inputs. However, the naive deep learning classifiers produce uncalibrated uncertainty for OOD data. Improving the uncertainty estimation typically requires external data for OOD-aware training or considerable costs to build an ensemble. In this work, we improve on uncertainty estimation without extra OOD data or additional inference costs using an alternative <em>Split-Ensemble</em> method. Specifically, we propose a novel <em>subtask-splitting</em> ensemble training objective where a task is split into several complementary subtasks based on feature similarity. Each subtask considers part of the data as in distribution while all the rest as OOD data. Diverse submodels can therefore be trained on each subtask with OOD-aware objectives, learning generalizable uncertainty estimation. To avoid overheads, we enable low-level feature sharing among submodels, building a tree-like Split-Ensemble architecture via iterative splitting and pruning. Empirical study shows Split-Ensemble, without additional computational cost, improves accuracy over a single model by 0.8%, 1.8%, and 25.5% on CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively. OOD detection for the same backbone and in-distribution datasets surpasses a single model baseline by 2.2%, 8.1%, and 29.6% in mean AUROC, respectively.
Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration
Authors: Chen, Xiong-Hui and Ye, Junyin and Zhao, Hang and Li, Yi-Chen and Liu, Xu-Hui and Shi, Haoran and Xu, Yu-Yan and Ye, Zhihao and Yang, Si-Hang and Yu, Yang and Huang, Anqi and Xu, Kai and Zhang, Zongzhang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: One-shot imitation learning (OSIL) is to learn an imitator agent that can execute multiple tasks with only a single demonstration. In real-world scenario, the environment is dynamic, e.g., unexpected changes can occur after demonstration. Thus, achieving generalization of the imitator agent is crucial as agents would inevitably face situations unseen in the provided demonstrations. While traditional OSIL methods excel in relatively stationary settings, their adaptability to such unforeseen changes, which asking for a higher level of generalization ability for the imitator agents, is limited and rarely discussed. In this work, we present a new algorithm called Deep Demonstration Tracing (DDT). In DDT, we propose a demonstration transformer architecture to encourage agents to adaptively trace suitable states in demonstrations. Besides, it integrates OSIL into a meta-reinforcement-learning training paradigm, providing regularization for policies in unexpected situations. We evaluate DDT on a new navigation task suite and robotics tasks, demonstrating its superior performance over existing OSIL methods across all evaluated tasks in dynamic environments with unforeseen changes. The project page is in https://osil-ddt.github.io.
DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images
Authors: Chen, Baoying and Zeng, Jishen and Yang, Jianquan and Yang, Rui
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Diffusion models have made significant strides in visual content generation but also raised increasing demands on generated image detection. Existing detection methods have achieved considerable progress, but they usually suffer a significant decline in accuracy when detecting images generated by an unseen diffusion model. In this paper, we seek to address the generalizability of generated image detectors from the perspective of hard sample classification. The basic idea is that if a classifier can distinguish generated images that closely resemble real ones, then it can also effectively detect less similar samples, potentially even those produced by a different diffusion model. Based on this idea, we propose Diffusion Reconstruction Contrastive Learning (DRCT), a universal framework to enhance the generalizability of the existing detectors. DRCT generates hard samples by high-quality diffusion reconstruction and adopts contrastive training to guide the learning of diffusion artifacts. In addition, we have built a million-scale dataset, DRCT-2M, including 16 types diffusion models for the evaluation of generalizability of detection methods. Extensive experimental results show that detectors enhanced with DRCT achieve over a 10% accuracy improvement in cross-set tests. The code, models, and dataset will soon be available at https://github.com/beibuwandeluori/DRCT.
$m̊ E(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Authors: Chen, Dingyang and Zhang, Qi
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Identification and analysis of symmetrical patterns in the natural world have led to significant discoveries across various scientific fields, such as the formulation of gravitational laws in physics and advancements in the study of chemical structures. In this paper, we focus on exploiting Euclidean symmetries inherent in certain cooperative multi-agent reinforcement learning (MARL) problems and prevalent in many applications. We begin by formally characterizing a subclass of Markov games with a general notion of symmetries that admits the existence of symmetric optimal values and policies. Motivated by these properties, we design neural network architectures with symmetric constraints embedded as an inductive bias for multi-agent actor-critic methods. This inductive bias results in superior performance in various cooperative MARL benchmarks and impressive generalization capabilities such as zero-shot learning and transfer learning in unseen scenarios with repeated symmetric patterns.
FedMBridge: Bridgeable Multimodal Federated Learning
Authors: Chen, Jiayi and Zhang, Aidong
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Multimodal Federated Learning (MFL) addresses the setup of multiple clients with diversified modality types (e.g. image, text, video, and audio) working together to improve their local personal models in a data-privacy manner. Prior MFL works rely on restrictive compositional neural architecture designs to ensure inter-client information sharing via blockwise model aggregation, limiting their applicability in the real-world <b>Architecture-personalized MFL (AMFL)</b> scenarios, where clients may have distinguished multimodal interaction strategies and there is no restriction on local architecture design. The key challenge in AMFL is how to automatically and efficiently tackle the two heterogeneity patterns–statistical and architecture heterogeneity–while maximizing the beneficial information sharing among clients. To solve this challenge, we propose <b>FedMBridge</b>, which leverages a topology-aware hypernetwork to act as a bridge that can automatically balance and digest the two heterogeneity patterns in a communication-efficient manner. Our experiments on four AMFL simulations demonstrate the efficiency and effectiveness of our proposed approach.
Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness
Authors: Chen, Honghao and Zhang, Yurong and Feng, Xiaokun and Chu, Xiangxiang and Huang, Kaiqi
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Robustness is a vital aspect to consider when deploying deep learning models into the wild. Numerous studies have been dedicated to the study of the robustness of vision transformers (ViTs), which have dominated as the mainstream backbone choice for vision tasks since the dawn of 2020s. Recently, some large kernel convnets make a comeback with impressive performance and efficiency. However, it still remains unclear whether large kernel networks are robust and the attribution of their robustness. In this paper, we first conduct a comprehensive evaluation of large kernel convnets’ robustness and their differences from typical small kernel counterparts and ViTs on six diverse robustness benchmark datasets. Then to analyze the underlying factors behind their strong robustness, we design experiments from both quantitative and qualitative perspectives to reveal large kernel convnets’ intriguing properties that are completely different from typical convnets. Our experiments demonstrate for the first time that pure CNNs can achieve exceptional robustness comparable or even superior to that of ViTs. Our analysis on occlusion invariance, kernel attention patterns and frequency characteristics provide novel insights into the source of robustness. Code available at: https://github.com/Lauch1ng/LKRobust.
One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment
Authors: Chen, Chaochao and Zhang, Jiaming and Li, Yuyuan and Han, Zhongxuan
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: The abundance of free internet data offers unprecedented opportunities for researchers and developers, but it also poses privacy risks. Utilizing data without explicit consent raises critical challenges in protecting personal information.Unlearnable examples have emerged as a feasible protection approach, which renders the data unlearnable, i.e., useless to third parties, by injecting imperceptible perturbations. However, these perturbations only exhibit unlearnable effects on either a particular dataset or label-consistent scenarios, thereby lacking broad applicability. To address both issues concurrently, we propose a universal perturbation generator that harnesses data with concept unlearnability, thereby broadening the scope of unlearnability beyond specific datasets or labels. Specifically, we leverage multi-modal pre-trained models to establish a connection between the data concepts in a shared embedding space. This connection enables the information transformation from image data to text concepts. Consequently, we can align the text embedding using concept-wise discriminant loss, and render the data unlearnable. Extensive experiments conducted on real-world datasets demonstrate the concept unlearnability, i.e., cross-dataset transferability and label-agnostic utility, of our proposed unlearnable examples, as well as their robustness against attacks.
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Authors: Chen, Zhuomin and Zhang, Jiaxing and Ni, Jingchao and Li, Xiaoting and Bian, Yuchen and Islam, Md Mezbahul and Mondal, Ananda and Wei, Hua and Luo, Dongsheng
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Graph Neural Networks (GNNs) have become a building block in graph data processing, with wide applications in critical domains. The growing needs to deploy GNNs in high-stakes applications necessitate explainability for users in the decision-making processes. A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs. This task is challenging due to the substantial distributional shift from the original graphs in the training set to the set of explainable subgraphs, which prevents accurate prediction of labels with the subgraphs. To address it, in this paper, we propose a novel method that generates proxy graphs for explainable subgraphs that are in the distribution of training data. We introduce a parametric method that employs graph generators to produce proxy graphs. A new training objective based on information theory is designed to ensure that proxy graphs not only adhere to the distribution of training data but also preserve explanatory factors. Such generated proxy graphs can be reliably used to approximate the predictions of the labels of explainable subgraphs. Empirical evaluations across various datasets demonstrate our method achieves more accurate explanations for GNNs.
Diffusive Gibbs Sampling
Authors: Chen, Wenlin and Zhang, Mingtian and Paige, Brooks and Hernández-Lobato, José Miguel and Barber, David
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayesian inference and molecular dynamics. Addressing this, we propose Diffusive Gibbs Sampling (DiGS), an innovative family of sampling methods designed for effective sampling from distributions characterized by distant and disconnected modes. DiGS integrates recent developments in diffusion models, leveraging Gaussian convolution to create an auxiliary noisy distribution that bridges isolated modes in the original space and applying Gibbs sampling to alternately draw samples from both spaces. A novel Metropolis-within-Gibbs scheme is proposed to enhance mixing in the denoising sampling step. DiGS exhibits a better mixing property for sampling multi-modal distributions than state-of-the-art methods such as parallel tempering, attaining substantially improved performance across various tasks, including mixtures of Gaussians, Bayesian neural networks and molecular dynamics.
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Authors: Chen, Yu and Zhang, Xiangcheng and Wang, Siwei and Huang, Longbo
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: In the realm of reinforcement learning (RL), accounting for risk is crucial for making decisions under uncertainty, particularly in applications where safety and reliability are paramount. In this paper, we introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation. Our framework covers a broad class of risk-sensitive RL, and facilitates analysis of the impact of estimation functions on the effectiveness of RSRL strategies and evaluation of their sample complexity. We design two innovative meta-algorithms: RS-DisRL-M, a model-based strategy for model-based function approximation, and RS-DisRL-V, a model-free approach for general value function approximation. With our novel estimation techniques via Least Squares Regression (LSR) and Maximum Likelihood Estimation (MLE) in distributional RL with augmented Markov Decision Process (MDP), we derive the first $\widetilde\mathcalO(\sqrtK)$ dependency of the regret upper bound for RSRL with static LRM, marking a pioneering contribution towards statistically efficient algorithms in this domain.
$\textttMoE-RBench$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
Authors: Chen, Guanjie and Zhao, Xinyu and Chen, Tianlong and Cheng, Yu
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, the reliability assessment of MoE lags behind its surging applications. Moreover, when transferred to new domains such as in fine-tuning MoE models sometimes underperform their dense counterparts. Motivated by the research gap and counter-intuitive phenomenon, we propose $\textttMoE-RBench$, the first comprehensive assessment of SMoE reliability from three aspects: $\textit(i)$ safety and hallucination, $\textit(ii)$ resilience to adversarial attacks, and $\textit(iii)$ out-of-distribution robustness. Extensive models and datasets are tested to compare the MoE to dense networks from these reliability dimensions. Our empirical observations suggest that with appropriate hyperparameters, training recipes, and inference techniques, we can build the MoE model more reliably than the dense LLM. In particular, we find that the robustness of SMoE is sensitive to the basic training settings. We hope that this study can provide deeper insights into how to adapt the pre-trained MoE model to other tasks with higher-generation security, quality, and stability. Codes are available at https://github.com/UNITES-Lab/MoE-RBench.
LLaGA: Large Language and Graph Assistant
Authors: Chen, Runjin and Zhao, Tong and Jaiswal, Ajay Kumar and Shah, Neil and Wang, Zhangyang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in deep learning. However, their application to graph data poses distinct challenges due to the inherent difficulty of translating graph structures to language. To this end, we introduce the the <b>L</b>arge <b>L</b>anguage <b>a</b>nd <b>G</b>raph <b>A</b>ssistant (<b>LLaGA</b>), an innovative model that effectively integrates LLM capabilities to handle the complexities of graph-structured data. LLaGA retains the general-purpose nature of LLMs while adapting graph data into a format compatible with LLM input. LLaGA achieves this by reorganizing graph nodes to structure-aware sequences and then mapping these into the token embedding space through a versatile projector. LLaGA excels in versatility, generalizability and interpretability, allowing it to perform consistently well across different datasets and tasks, extend its ability to unseen datasets or tasks, and provide explanations for graphs. Our extensive experiments across popular graph benchmarks show that LLaGA delivers outstanding performance across four datasets and three tasks using one single model, surpassing state-of-the-art graph models in both supervised and zero-shot scenarios.
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
Authors: Chen, Zhaorun and Zhao, Zhuokai and Luo, Hongyin and Yao, Huaxiu and Li, Bo and Zhou, Jiawei
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: While large vision-language models (LVLMs) have demonstrated impressive capabilities in interpreting multi-modal contexts, they invariably suffer from object hallucinations (OH). We introduce HALC, a novel decoding algorithm designed to mitigate OH in LVLMs. HALC leverages distinct fine-grained optimal visual information in vision-language tasks and operates on both local and global contexts simultaneously. Specifically, HALC integrates a robust auto-focal grounding mechanism (locally) to correct hallucinated tokens on the fly, and a specialized beam search algorithm (globally) to significantly reduce OH while preserving text generation quality. Additionally, HALC can be integrated into any LVLMs as a plug-and-play module without extra training. Extensive experimental studies demonstrate HALC’s effectiveness in reducing OH, outperforming state-of-the-arts across four benchmarks. Code is released at https://github.com/BillChan226/HALC.
Compact Optimality Verification for Optimization Proxies
Authors: Chen, Wenbo and Zhao, Haoruo and Tanneau, Mathieu and Van Hentenryck, Pascal
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions. Following recent work by (Nellikkath & Chatzivasileiadis, 2021), this paper reconsiders the optimality verification problem for optimization proxies, i.e., the determination of the worst-case optimality gap over the instance distribution. The paper proposes a compact formulation for optimality verification and a gradient-based primal heuristic that brings significant computational benefits to the original formulation. The compact formulation is also more general and applies to non-convex optimization problems. The benefits of the compact formulation are demonstrated on large-scale DC Optimal Power Flow and knapsack problems.
Enhancing Implicit Shape Generators Using Topological Regularizations
Authors: Chen, Liyan and Zheng, Yan and Li, Yang and Jagarapu, Lohit Anirudh and Li, Haoxiang and Kang, Hao and Hua, Gang and Huang, Qixing
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: A fundamental problem in learning 3D shapes generative models is that when the generative model is simply fitted to the training data, the resulting synthetic 3D models can present various artifacts. Many of these artifacts are topological in nature, e.g., broken legs, unrealistic thin structures, and small holes. In this paper, we introduce a principled approach that utilizes topological regularization losses on an implicit shape generator to rectify topological artifacts. The objectives are two-fold. The first is to align the persistent diagram (PD) distribution of the training shapes with that of synthetic shapes. The second ensures that the PDs are smooth among adjacent synthetic shapes. We show how to achieve these two objectives using two simple but effective formulations. Specifically, distribution alignment is achieved to learn a generative model of PDs and align this generator with PDs of synthetic shapes. We show how to handle discrete and continuous variabilities of PDs by using a shape-regularization term when performing PD alignment. Moreover, we enforce the smoothness of the PDs using a smoothness loss on the PD generator, which further improves the behavior of PD distribution alignment. Experimental results on ShapeNet show that our approach leads to much better generalization behavior than state-of-the-art implicit shape generators.
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Authors: Chen, Yanda and Zhong, Ruiqi and Ri, Narutatsu and Zhao, Chen and He, He and Steinhardt, Jacob and Yu, Zhou and Mckeown, Kathleen
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs process different inputs? To answer these questions, we propose to evaluate $\textbfcounterfactual simulatability$ of natural language explanations: whether an explanation can enable humans to precisely infer the model’s outputs on diverse counterfactuals of the explained input. For example, if a model answers ”$\textityes$” to the input question ”$\textitCan eagles fly?$” with the explanation ”$\textitall birds can fly$”, then humans would infer from the explanation that it would also answer ”$\textityes$” to the counterfactual input ”$\textitCan penguins fly?$”. If the explanation is precise, then the model’s answer should match humans’ expectations. We implemented two metrics based on counterfactual simulatability: precision and generality. We generated diverse counterfactuals automatically using LLMs. We then used these metrics to evaluate state-of-the-art LLMs (e.g., GPT-4) on two tasks: multi-hop factual reasoning and reward modeling. We found that LLM’s explanations have low precision and that precision does not correlate with plausibility. Therefore, naively optimizing human approvals (e.g., RLHF) may be insufficient.
On the Trajectory Regularity of ODE-based Diffusion Sampling
Authors: Chen, Defang and Zhou, Zhenyu and Wang, Can and Shen, Chunhua and Lyu, Siwei
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Diffusion-based generative models use stochastic differential equations (SDEs) and their equivalent ordinary differential equations (ODEs) to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models. We characterize an implicit denoising trajectory and discuss its vital role in forming the coupled sampling trajectory with a strong shape regularity, regardless of the generated content. We also describe a dynamic programming-based scheme to make the time schedule in sampling better fit the underlying trajectory structure. This simple strategy requires minimal modification to any given ODE-based numerical solvers and incurs negligible computational cost, while delivering superior performance in image generation, especially in $5∼ 10$ function evaluations.
ODIN: Disentangled Reward Mitigates Hacking in RLHF
Authors: Chen, Lichang and Zhu, Chen and Chen, Jiuhai and Soselia, Davit and Zhou, Tianyi and Goldstein, Tom and Huang, Heng and Shoeybi, Mohammad and Catanzaro, Bryan
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: In this work, we study the issue of reward hacking on the response length, a challenge emerging in Reinforcement Learning from Human Feedback (RLHF) on LLMs. A well-formatted, verbose but less helpful response from the LLMs can often deceive LLMs or even human evaluators and achieve high scores. The same issue also holds for some reward models in RL. To address the challenges in both training and evaluation, we establish a more reliable evaluation protocol for comparing different training configurations, which inspects the trade-off between LLM evaluation score and response length obtained by varying training hyperparameters. Based on this evaluation, we conduct large-scale studies, where the results shed insights into the efficacy of hyperparameters and tricks used in RL on mitigating length bias. We further propose to improve the reward model by jointly training two linear heads to predict the preference, one trained to correlate with length and the other trained to decorrelate with length and therefore focusing more on the actual content. We then discard the length head in RL to ignore the spurious length reward. Experiments demonstrate that our approach eliminates the reward correlation with length, and improves the obtained policy by a significant margin.
Stacking Deep Set Networks and Pooling by Quantiles
Authors: Chen, Zhuojun and Zhu, Xinghua and Su, Dongzhe and Chuang, Justin C. I.
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We propose Stacked Deep Sets and Quantile Pooling for learning tasks on set data. We introduce Quantile Pooling, a novel permutation-invariant pooling operation that synergizes max and average pooling. Just like max pooling, quantile pooling emphasizes the most salient features of the data. Like average pooling, it captures the overall distribution and subtle features of the data. Like both, it is lightweight and fast. We demonstrate the effectiveness of our approach in a variety of tasks, showing that quantile pooling can outperform both max and average pooling in each of their respective strengths. We also introduce a variant of deep set networks that is more expressive and universal. While Quantile Pooling balances robustness and sensitivity, Stacked Deep Sets enhances learning with depth.
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks
Authors: Chen, Xingwu and Zou, Difan
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: We study the capabilities of the transformer architecture with varying depth. Specifically, we designed a novel set of sequence learning tasks to systematically evaluate and comprehend how the depth of transformer affects its ability to perform memorization, reasoning, generalization, and contextual generalization. We show a transformer with only one attention layer can excel in memorization but falls short in other tasks. Then, we show that exhibiting reasoning and generalization ability requires the transformer to have at least two attention layers, while context generalization ability may necessitate three attention layers. Additionally, we identify a class of simple operations that a single attention layer can execute, and show that the complex tasks can be approached as the combinations of these simple operations and thus can be resolved by stacking multiple attention layers. This sheds light on studying more practical and complex tasks beyond our design. Numerical experiments corroborate our theoretical findings.
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
Authors: Cheng, Xiang and Chen, Yuxin and Sra, Suvrit
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Many neural network architectures are known to be Turing Complete, and can thus, in principle implement arbitrary algorithms. However, Transformers are unique in that they can implement gradient-based learning algorithms <em>under simple parameter configurations</em>. This paper provides theoretical and empirical evidence that (non-linear) Transformers naturally learn to implement gradient descent <em>in function space</em>, which in turn enable them to learn non-linear functions in context. Our results apply to a broad class of combinations of non-linear architectures and non-linear in-context learning tasks. Additionally, we show that the optimal choice of non-linear activation depends in a natural way on the class of functions that need to be learned.
Layerwise Change of Knowledge in Neural Networks
Authors: Cheng, Xu and Cheng, Lei and Peng, Zhaoran and Xu, Yang and Han, Tian and Zhang, Quanshi
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: This paper aims to explain how a deep neural network (DNN) gradually extracts new knowledge and forgets noisy features through layers in forward propagation. Up to now, although how to define knowledge encoded by the DNN has not reached a consensus so far, previous studies have derived a series of mathematical evidences to take interactions as symbolic primitive inference patterns encoded by a DNN. We extend the definition of interactions and, for the first time, extract interactions encoded by intermediate layers. We quantify and track the newly emerged interactions and the forgotten interactions in each layer during the forward propagation, which shed new light on the learning behavior of DNNs. The layer-wise change of interactions also reveals the change of the generalization capacity and instability of feature representations of a DNN.
Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations
Authors: Cheng, Ze and Hao, Zhongkai and Wang, Xiaoqiang and Huang, Jianing and Wu, Youjia and Liu, Xudan and Zhao, Yiru and Liu, Songming and Su, Hang
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: For partial differential equations on domains of arbitrary shapes, existing works of neural operators attempt to learn a mapping from geometries to solutions. It often requires a large dataset of geometry-solution pairs in order to obtain a sufficiently accurate neural operator. However, for many industrial applications, e.g., engineering design optimization, it can be prohibitive to satisfy the requirement since even a single simulation may take hours or days of computation. To address this issue, we propose <em>reference neural operators</em> (RNO), a novel way of implementing neural operators, i.e., to learn the smooth dependence of solutions on geometric deformations. Specifically, given a reference solution, RNO can predict solutions corresponding to arbitrary deformations of the referred geometry. This approach turns out to be much more data efficient. Through extensive experiments, we show that RNO can learn the dependence across various types and different numbers of geometry objects with relatively small datasets. RNO outperforms baseline models in accuracy by a large lead and achieves up to 80% error reduction.
Causal Inference out of Control: Estimating Performativity without Treatment Randomization
Authors: Cheng, Gary and Hardt, Moritz and Mendler-Dünner, Celestine
Published in: Proceedings of the 41st International Conference on Machine Learning (Main Conference Track), 2024

Abstract: Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on user consumption. In pursuit of estimating this effect from observational data, we identify a set of assumptions that permit causal identifiability without assuming randomized platform actions. Our results are applicable to platforms that rely on machine-learning-powered predictions and leverage knowledge from historical data. The key novelty of our approach is to explicitly model the dynamics of consumption over time, exploiting the repeated interaction of digital platforms with their participants to prove our identifiability results. By viewing the platform as a controller acting on a dynamical system, we can show that exogenous variation in consumption and appropriately responsive algorithmic control actions are sufficient for identifying the causal effect of interest. We complement our claims with an analysis of ready-to-use finite sample estimators and empirical investigations. More broadly, our results deriving identifiability conditions tailored to digital platform settings illustrate a fruitful interplay of control theory and causal inference.