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Furong Huang

105 accepted papers

2026

AdvBDGen: A Robust Framework for Generating Adaptive and Stealthy Backdoors in LLM Alignment

AAAI 2026technical

With the increasing adoption of reinforcement learning with human feedback (RLHF) to align large language models (LLMs), the risk of backdoor installation during the alignment process has grown, potentially leading to unintended and harmful behaviors. Existing backdoor attacks mostly focus on simple

Cited by 0SourcePDFScholar
2026

MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systems

ICML 2026poster

Algorithmic fairness is often studied in static or single-agent settings, yet many real-world decision-making systems involve multiple interacting entities whose multi-stage actions jointly influence long-term outcomes. Existing fairness methods applied at isolated decision points frequently fail to…

Cited by 0SourceScholar
2026

MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Models for Embodied Task Planning

ICLR 2026oral

Mobile manipulators in households must both navigate and manipulate. This requires a compact, semantically rich scene representation that captures where objects are, how they function, and which parts are actionable. Scene graphs are a natural choice, yet prior work often separates spatial and funct…

Cited by 0SourcecodeScholar
2026

Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing

ICML 2026poster

Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-trajectory signals and lack principled mechanisms to exploit global dynamics across parallel branches. We introduce 2D probi…

Cited by 0SourceScholar
2026

PropensityBench: Evaluating Latent Safety Risks in Large Language Models via an Agentic Approach

ICLR 2026poster

Recent advances in Large Language Models (LLMs) have sparked concerns over their potential to acquire and misuse dangerous capabilities, posing frontier risks to society. Current safety evaluations primarily test for what a model *can* do---its capabilities---without assessing what it *would* do if…

Cited by 0SourcecodeScholar
2026

ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation

ICLR 2026poster

Unified multimodal models (UMMs) have shown remarkable advances in jointly understanding and generating text and images. However, prevailing evaluations treat these abilities in isolation, such that tasks with multimodal inputs and outputs are scored primarily through unimodal reasoning: textual ben…

Cited by 0SourcecodeScholar
2026

Safety Recovery in Reasoning Models Is Only a Few Early Steering Steps Away

ICML 2026poster

Reinforcement learning (RL) based post-training for explicit chain-of-thought (e.g., GRPO) improves the reasoning ability of multimodal large-scale reasoning models (MLRMs). But recent evidence shows that it can simultaneously degrade safety alignment and increase jailbreak success rates. We propose…

Cited by 0SourceScholar
2026

Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models

ICML 2026poster

Masked diffusion language models decode by iteratively unmasking tokens, where the unmasking order defines an ``order of thought'' that strongly influences generation quality yet is typically chosen heuristically. We derive a tractable upper bound on the sequential decoding mismatch, measured by the…

Cited by 0SourceScholar
2026

SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?

ICML 2026poster

Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While existing benchmarks evaluate LLMs on scientific knowledge and reasoning, their ability to predict experimental outcomes---…

Cited by 0SourceScholar
2026

TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models

ICML 2026poster

Time series data is ubiquitous in real-world scenarios and crucial for critical applications ranging from energy management to traffic control. Consequently, the ability to reason over time series is a fundamental skill for generalist models to solve complex problems. However, current benchmarks for…

Cited by 0SourceScholar
2026

Token-Level LLM Collaboration via FusionRoute

ICML 2026poster

Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a single general-purpose model typically requires scaling to sizes that are prohibitively expensive to train and deploy. On the other hand, while smaller domain-spec…

Cited by 0SourceScholar
2026

TraceGen: World Modeling in 3D Trace Space Enables Learning from Cross-Embodiment Videos

CVPR 2026

Learning new robot tasks on new platforms and in new scenes from only a handful of demonstrations remains challenging. While videos of other embodiments---humans and different robots---are abundant, differences in embodiment, camera, and environment hinder their direct use. We address the small-data

Cited by 0SourcecodeScholar
2026

TrustGen: A Platform of Dynamic Benchmarking on the Trustworthiness of Generative Foundation Models

ICLR 2026poster

Generative foundation models (GenFMs), such as large language models and text-to-image systems, have demonstrated remarkable capabilities in various downstream applications. As they are increasingly deployed in high-stakes applications, assessing their trustworthiness has become both a critical nece…

Cited by 0SourceScholar
2026

Zebra-CoT: A Dataset for Interleaved Vision-Language Reasoning

ICLR 2026poster

Humans often rely on visual aids, such as diagrams or sketches, when tackling complex problems. Teaching multimodal models to adopt similar strategies, a process known as Visual Chain of Thought (visual CoT), is much more difficult. The main challenges are: (1) weak performance of off-the-shelf visu…

Cited by 0SourcecodeScholar
2025

A Technical Report on “Erasing the Invisible”: The 2024 NeurIPS Competition on Stress Testing Image Watermarks

NeurIPS 2025poster

AI-generated images have become pervasive, raising critical concerns around content authenticity, intellectual property, and the spread of misinformation. Invisible watermarks offer a promising solution for identifying AI-generated images, preserving content provenance without degrading visual quali…

Cited by 0SourceScholar
2025

Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset

ICLR 2025poster

Machine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy concerns in Vision Language Models (VLMs) remain underexplored. To address this, we introduce Facial Identity Unlearning…

2025

Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?

AAAI 2025technical

Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as LLMs may inadvertently produce copyrighted material. In this paper, we first investigate the effectiveness of watermark…

Cited by 4SourcePDFScholar
2025

Collab: Controlled Decoding using Mixture of Agents for LLM Alignment

ICLR 2025poster

Alignment of Large Language models (LLMs) is crucial for safe and trustworthy deployment in applications. Reinforcement learning from human feedback (RLHF) has emerged as an effective technique to align LLMs to human preferences, and broader utilities, but it requires updating billions of model para…

Cited by 1SourcePDFScholar
2025

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data

EMNLP 2025

Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). Among RLHF methods, Group Relative Policy Optimization (GRPO) has gained attention for its simplicity and strong performance, notably eliminating the need for a lea

Cited by 0SourcePDFScholar
2025

Does Thinking More Always Help? Mirage of Test-Time Scaling in Reasoning Models

NeurIPS 2025poster

Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek R1) have led to a popular belief that extending thinking traces using prompts like “Wait” or “Let me rethink” can improve performance. This raises a natural question: Does thinking more at test-time truly lead to bet…

Cited by 0SourceScholar
2025

Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement

NAACL 2025findings

Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often de…

2025

FLARE: Robot Learning with Implicit World Modeling

CoRL 2025poster

We introduce **F**uture **LA**tent **R**presentation Alignm**E**nt (**FLARE**), a novel framework that integrates predictive world modeling into robot policy learning. By aligning features from a diffusion transformer with latent embeddings of future observations, **FLARE** enables a diffusion trans…

Cited by 0SourceScholar
2025

GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-Time Alignment

ICLR 2025poster

Large Language Models (LLMs) exhibit impressive capabilities but require careful alignment with human preferences. Traditional training-time methods finetune LLMs using human preference datasets but incur significant training costs and require repeated training to handle diverse user preferences. Te…

2025

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning

ICCV 2025poster

Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depend on the quality of generated data and struggle with fine-grained manipulation due to the lack of environment feedback.…

2025

Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Models

CoRL 2025poster

Exploration is key for general-purpose robotic learning, particularly in open-ended environments where explicit guidance or task-specific feedback is limited. Vision-language models (VLMs), which can reason about object semantics, spatial relations, and potential outcomes, offer a promising foundati…

Cited by 0SourceScholar
2025

Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment

CVPR 2025poster

With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that despite training-time safety alignment, these models remain vulnerable to jailbreak attacks--carefully crafted image-prompt…

Cited by 3SourcePDFScholar
2025

Is Poisoning a Real Threat to DPO? Maybe More So Than You Think

AAAI 2025technical

Recent advancements in Reinforcement Learning with Human Feedback (RLHF) have significantly impacted the alignment of Large Language Models (LLMs). The sensitivity of reinforcement learning algorithms such as Proximal Policy Optimization (PPO) has led to new line work on Direct Preference Optimizati…

Cited by 0SourcePDFScholar
2025

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

NAACL 2025findings

Causal inference has demonstrated significant potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability by capturing causal relationships among variables. The rise of generative Large Language Models (LLMs) has greatl…

Cited by 0SourcePDFScholar
2025

MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs

NAACL 2025long

The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert LLMs into a unified Mixture-of-Experts (MoE) model, with the goal of enhancing performance in each domain while retainin…

Cited by 0SourcePDFScholar
2025

PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models

NAACL 2025long

As the capabilities of large language models (LLMs) continue to expand, their usage has become increasingly prevalent. However, as reflected in numerous ongoing lawsuits regarding LLM-generated content, addressing copyright infringement remains a significant challenge. In this paper, we introduce Po…

Cited by 1SourcePDFScholar
2025

Safety Guaranteed Robust Multi-Agent Reinforcement Learning with Hierarchical Control for Connected and Automated Vehicles

ICRA 2025

We address the problem of coordination and control of Connected and Automated Vehicles (CAVs) in the presence of imperfect observations in mixed traffic environment. A commonly used approach is learning-based decision-making, such as reinforcement learning (RL). However, most existing safe RL method

Cited by 5SourceScholar
2025

Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension

ICCV 2025poster

Despite significant advancements in vision-language models (VLMs), there lacks effective approaches to enhance response quality by scaling inference-time computation. This capability is known to be a core step towards the self-improving models in recent large language model studies. In this paper, w…

2025

SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement

NeurIPS 2025spotlight

We introduce ThinkLite-VL, a family of visual reasoning models that achieve state-of-the-art (SoTA) performance using an order of magnitude fewer training samples, relying purely on reinforcement fine-tuning (RFT) self-improvement without any knowledge distillation. Our central insight is that sampl…

Cited by 0SourcecodeScholar
2025

Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory

AISTATS 2025poster

Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings. In lifelong RL, the "life" of an RL agent is modeled as a stream of tasks drawn from a task distribution. We propose EPIC (Empirical PAC-Bayes that Improves Continu…

Cited by 0SourceScholar
2025

TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies

ICLR 2025poster

Although large vision-language-action (VLA) models pretrained on extensive robot datasets offer promising generalist policies for robotic learning, they still struggle with spatial-temporal dynamics in interactive robotics, making them less effective in handling complex tasks, such as manipulation.…

Cited by 8SourcePDFScholar
2025

Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems

EMNLP 2025

Large Language Models (LLMs) have demonstrated exceptional capabilities, yet selecting the most reliable response from multiple LLMs remains a challenge, particularly in resource-constrained settings. Existing approaches often depend on costly external verifiers, human evaluators, or self-consistenc

2025

ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs

NeurIPS 2025poster

Reinforcement learning (RL) has shown great effectiveness for fine-tuning large language models (LLMs) using tasks that are challenging yet easily verifiable, such as math reasoning or code generation. However, extending this success to visual perception in vision–language models (VLMs) has been imp…

Cited by 0SourcecodeScholar
2025

World Models with Hints of Large Language Models for Goal Achieving

NAACL 2025long

Reinforcement learning struggles in the face of long-horizon tasks and sparse goals due to the difficulty in manual reward specification. While existing methods address this by adding intrinsic rewards, they may fail to provide meaningful guidance in long-horizon decision-making tasks with large sta…

Cited by 2SourcePDFScholar
2025

Zero-Shot Vision Encoder Grafting via LLM Surrogates

ICCV 2025poster

Vision language models (VLMs) typically pair a modestly sized vision encoder with a large language model (LLM), e.g., Llama-70B, making the decoder the primary computational burden during training.To reduce costs, a promising strategy is to first train the vision encoder using a small language model…

2024

A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)

ICML 2024poster

We propose VecKM, a local point cloud geometry encoder that is descriptive and efficient to compute. VecKM leverages a unique approach by vectorizing a kernel mixture to represent the local point cloud. Such representation's descriptiveness is supported by two theorems that validate its ability to r…

2024

ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization

ICML 2024oral

The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore the causal relationship between different action dimensions and rewards to evaluate the significance of various primi…

2024

Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate

ICML 2024poster

Decisions made by machine learning models can have lasting impacts, making long-term fairness a critical consideration. It has been observed that ignoring the long-term effect and directly applying fairness criterion in static settings can actually worsen bias over time. To address biases in sequent…

2024

AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models

EMNLP 2024finding

Large vision-language models (LVLMs) are prone to hallucinations, where certain contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects. While some benchmarks have been developed to investigate LVLM hallucina…

2024

Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies

ICLR 2024spotlight

In light of the burgeoning success of reinforcement learning (RL) in diverse real-world applications, considerable focus has been directed towards ensuring RL policies are robust to adversarial attacks during test time. Current approaches largely revolve around solving a minimax problem to prepare f…

2024

Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance

NeurIPS 2024poster

Multi-Agent Reinforcement Learning (MARL) struggles with sample inefficiency and poor generalization [1]. These challenges are partially due to a lack of structure or inductive bias in the neural networks typically used in learning the policy. One such form of structure that is commonly observed in…

Cited by 3SourcePDFScholar
2024

COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL

ICLR 2024poster

Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for dynamics model learning. However, due to the complex real-world environment, it is inevitable to learn an imperfect dynam…

Cited by 10SourcePDFScholar
2024

Decodable and Sample Invariant Continuous Object Encoder

ICLR 2024poster

We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object, invariant to the sample distribution and density. Sample distribution and density invariance enables HDFE to consistentl…

2024

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

ICLR 2024spotlight

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In t…

2024

Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization

NeurIPS 2024poster

Despite the abundance of datasets available for assessing large language models (LLMs), the scarcity of continuous and reliable difficulty labels for individual data points, in most cases, curtails their capacity to benchmark model generalization performance across different levels of complexity. Ad…

Cited by 0SourcePDFScholar
2024

Explore Spurious Correlations at the Concept Level in Language Models for Text Classification

ACL 2024long

Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate exceptional performance, they face robustness challenges due to spurious correlations arising from imbalanced label distribut…

2024

FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?

NeurIPS 2024poster

Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model. While prior mechanisms attempt to solve the free-rider dilemma, none have addressed the issue of truthfulness. In p…

2024

Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations

ICLR 2024poster

Deploying reinforcement learning (RL) systems requires robustness to uncertainty and model misspecification, yet prior robust RL methods typically only study noise introduced independently across time. However, practical sources of uncertainty are usually coupled across time. We formally introduce t…

Cited by 7SourcePDFScholar
2024

HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

CVPR 2024poster

We introduce "HallusionBench" a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs) such as GPT-4V(ision) Gemini Pro Vision Claude 3 and LLaVA-1.5 by emphasizing nuanced unders…

2024

Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds

ICLR 2024poster

Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group annotations, proposed solutions rely on heuristics that aim to identify and then amplify the minority samples during tra…

Cited by 0SourcePDFScholar
2024

Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion

NeurIPS 2024poster

Can we generate a control policy for an agent using just one demonstration of desired behaviors as a prompt, as effortlessly as creating an image from a textual description? In this paper, we present **Make-An-Agent**, a novel policy parameter generator that leverages the power of conditional diffus…

2024

MaxMin-RLHF: Alignment with Diverse Human Preferences

ICML 2024poster

Reinforcement Learning from Human Feedback (RLHF) aligns language models to human preferences by employing a singular reward model derived from preference data. However, the single reward model overlooks the rich diversity of human preferences inherent in data collected from multiple users. In this…

Cited by 82SourcePDFScholar
2024

Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences

ACL 2024long

Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate fr…

2024

Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation

EMNLP 2024finding

Large language models (LLMs) have significantly advanced various natural language processing tasks, but deploying them remains computationally expensive. Knowledge distillation (KD) is a promising solution, enabling the transfer of capabilities from larger teacher LLMs to more compact student models…

2024

PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback

ICLR 2024poster

We present a novel unified bilevel optimization-based framework, \textsf{PARL}, formulated to address the recently highlighted critical issue of policy alignment in reinforcement learning using utility or preference-based feedback. We identify a major gap within current algorithmic designs for solvi…

Cited by 29SourcePDFScholar
2024

PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control

ICML 2024oral

Temporal action abstractions, along with belief state representations, are a powerful knowledge sharing mechanism for sequential decision making. In this work, we propose a novel view that treats inducing temporal action abstractions as a sequence compression problem. To do so, we bring a subtle but…

2024

PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts

ICLR 2024poster

Vision-language models like CLIP are widely used in zero-shot image classification due to their ability to understand various visual concepts and natural language descriptions. However, how to fully leverage CLIP's unprecedented human-like understanding capabilities to achieve better performance is…

2024

Position: On the Possibilities of AI-Generated Text Detection

ICML 2024poster

Our study addresses the challenge of distinguishing human-written text from Large Language Model (LLM) outputs. We provide evidence that this differentiation is consistently feasible, except when human and machine text distributions are indistinguishable across their entire support. Employing inform…

Cited by 4SourcePDFScholar
2024

Position: TrustLLM: Trustworthiness in Large Language Models

ICML 2024poster

Large language models (LLMs) have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLM…

Cited by 95SourcePDFScholar
2024

Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss

ICML 2024poster

We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-TACO leverages a subset of multitask offline datasets for pretraining a general feature representation, which captures cr…

2024

Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL

ICLR 2024poster

Most existing works focus on direct perturbations to the victim's state/action or the underlying transition dynamics to demonstrate the vulnerability of reinforcement learning agents to adversarial attacks. However, such direct manipulations may not be always realizable. In this paper, we consider…

Cited by 4SourcePDFScholar
2024

SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation

ICLR 2024poster

Data augmentation, a cornerstone technique in deep learning, is crucial in enhancing model performance, especially with scarce labeled data. While traditional techniques are effective, their reliance on hand-crafted methods limits their applicability across diverse data types and tasks. Although mod…

Cited by 1SourcePDFScholar
2024

Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models

NeurIPS 2024poster

Vision-Language Models (VLMs) excel in generating textual responses from visual inputs, but their versatility raises security concerns. This study takes the first step in exposing VLMs’ susceptibility to data poisoning attacks that can manipulate responses to innocuous, everyday prompts. We introduc…

2024

Transfer Q-star : Principled Decoding for LLM Alignment

NeurIPS 2024poster

Aligning foundation models is essential for their safe and trustworthy deployment. However, traditional fine-tuning methods are computationally intensive and require updating billions of model parameters. A promising alternative, alignment via decoding, adjusts the response distribution directly wit…

Cited by 20SourcePDFScholar
2024

WAVES: Benchmarking the Robustness of Image Watermarks

ICML 2024poster

In the burgeoning age of generative AI, watermarks act as identifiers of provenance and artificial content. We present WAVES (Watermark Analysis via Enhanced Stress-testing), a benchmark for assessing image watermark robustness, overcoming the limitations of current evaluation methods. WAVES integra…

2023

$\texttt{TACO}$: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning

NeurIPS 2023poster

Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle. Prior works have attempted to address this challenge by creating self-supervised auxiliary tasks, aiming to enrich the agent's learned representations with co…

2023

C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder

NeurIPS 2023poster

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally disentangled, meaning that distinct factors are encoded into sep…

2023

Certifiably Robust Policy Learning against Adversarial Multi-Agent Communication

ICLR 2023poster

Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based…

Cited by 21SourcePDFScholar
2023

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

NeurIPS 2023poster

Standard diffusion models involve an image transform -- adding Gaussian noise -- and an image restoration operator that inverts this degradation. We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact, an entire family…

2023

Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness

ICLR 2023poster

The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training. To understand this, we propose…

2023

Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function

ICLR 2023poster

Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance and sample efficiency. In this paper, we provide both practical and theoretical insights on the empirical success of th…

Cited by 16SourcePDFScholar
2023

Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator

ICML 2023poster

Current approaches for training robust models are typically tailored to scenarios where data variations are accessible in the training set. While shown effective in achieving robustness to these foreseen variations, these approaches are ineffective in learning *unforeseen* robustness, i.e., robustne…

Cited by 2SourcePDFScholar
2023

Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy

ICML 2023poster

Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning. Previous works learn a dynamics model that fits under the empirical state-action visitation distribution for all histor…

2023

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

AAAI 2023technical

Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity…

Cited by 11SourcePDFScholar
2023

SMART: Self-supervised Multi-task pretrAining with contRol Transformers

ICLR 2023top-25%

Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining representations without explicit labels. When it comes to sequential decision-making tasks, however, it is difficult to prop…

2023

STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning

ICML 2023poster

Directed Exploration is a crucial challenge in reinforcement learning (RL), especially when rewards are sparse. Information-directed sampling (IDS), which optimizes the information ratio, seeks to do so by augmenting regret with information gain. However, estimating information gain is computational…

Cited by 8SourcePDFScholar
2023

SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

ICLR 2023poster

The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchroniza…

2022

Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach

NeurIPS 2022accept

Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on automatic data augmentation learn a policy to form a sequence of augmentation operations, which are still pre-defined and re…

2022

Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning

NeurIPS 2022accept

Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations. Therefore, it is crucial to train RL agents that are robust against any attacks with a bounded budget. Existing robust training methods…

2022

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking

NeurIPS 2022accept

Machine learning systems perform well on pattern matching tasks, but their ability to perform algorithmic or logical reasoning is not well understood. One important reasoning capability is algorithmic extrapolation, in which models trained only on small/simple reasoning problems can synthesize compl…

Cited by 21SourcePDFScholar
2022

Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory

ICLR 2022spotlight

We study reinforcement learning for partially observable multi-agent systems where each agent only has access to its own observation and reward and aims to maximize its cumulative rewards. To handle partial observations, we propose graph-assisted predictive state representations (GAPSR), a scalable…

Cited by 12SourcePDFScholar
2022

Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework

ICML 2022spotlight

Enforcing orthogonality in convolutional neural networks is a remedy for gradient vanishing/exploding problems and sensitivity to perturbation. Many previous approaches for orthogonal convolutions enforce orthogonality on its flattened kernel, which, however, do not lead to the orthogonality of the…

2022

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity

NeurIPS 2022accept

Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification. When scaling up the underlying graphs of GNNs to a larger size, we are forced to either train on the complete graph and keep the full graph adjacency and node embeddings in memory (which is often…

Cited by 23SourcePDFScholar
2022

Transfer RL across Observation Feature Spaces via Model-Based Regularization

ICLR 2022poster

In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramatic changes over time (e.g. increased number of observable features). However, when the observation space changes, the pre…

Cited by 23SourcePDFScholar
2022

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

NeurIPS 2022accept

The increasing reliance on ML models in high-stakes tasks has raised a major concern about fairness violations. Although there has been a surge of work that improves algorithmic fairness, most are under the assumption of an identical training and test distribution. In many real-world applications, h…

2022

Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency

ICLR 2022poster

Transformers are neural network architectures that achieve remarkable performance in many areas. However, the core component of Transformers, multi-head self-attention (MHSA), is mainly derived from heuristics, and the interactions across its components are not well understood. To address the proble…

Cited by 6SourcePDFScholar
2022

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

NeurIPS 2022accept

Conventional saliency maps highlight input features to which neural network predictions are highly sensitive. We take a different approach to saliency, in which we identify and analyze the network parameters, rather than inputs, which are responsible for erroneous decisions. We first verify that ide…

2022

Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL

ICLR 2022poster

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms…

2021

Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks

AAAI 2021technical

Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks. However, traditional defense mechanisms assume a uniform attack over the examples according to the underlying data distribution, which is appa…

Cited by 28SourcePDFScholar
2021

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

NeurIPS 2021poster

Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans possess the ability to extrapolate reasoning strategies learned on simple problems to solve harder examples, often by thinking for…

2021

TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL

AAAI 2021technical

Transferring knowledge among various environments is important for efficiently learning multiple tasks online. Most existing methods directly use the previously learned models or previously learned optimal policies to learn new tasks. However, these methods may be inefficient when the underlying mod…

2021

Understanding the Generalization Benefit of Model Invariance from a Data Perspective

NeurIPS 2021poster

Machine learning models that are developed to be invariant under certain types of data transformations have shown improved generalization in practice. However, a principled understanding of why invariance benefits generalization is limited. Given a dataset, there is often no principled way to select…

2021

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

NeurIPS 2021poster

Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To scale such GNNs to large graphs, various neighbor-, layer-, or subgraph-sampling techniques are proposed to alleviate the "…

2020

An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm

ICML 2020poster

We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consistency of the estimated model parameters. We represent the spectral algorithm as a computational graph. Noise can be inject…

Cited by 12SourcePDFScholar
2020

Convolutional Tensor-Train LSTM for Spatio-Temporal Learning

NeurIPS 2020poster

Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation. However, existing methods still perform poorly on challenging video tasks such as long-term forecasting. This is because these kinds of challengin…

2020

Understanding Generalization in Deep Learning via Tensor Methods

AISTATS 2020poster

Deep neural networks generalize well on unseen data though the number of parameters often far exceeds the number of training examples. Recently proposed complexity measures have provided insights to understanding the generalizability in neural networks from perspectives of PAC-Bayes, robustness, ove…

2019

Guaranteed Scalable Learning of Latent Tree Models

UAI 2019poster

We present an integrated approach to structure and parameter estimation in latent tree graphical models, where some nodes are hidden. Our overall approach follows a “divide-and-conquer” strategy that learns models over small groups of variables and iteratively merges into a global solution. The s…

Cited by 10SourcePDFScholar