Mengdi Wang

ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning ACL 2026 Jailbreaks as Inference-Time Alignment: A Framework for Understanding Safety Failures in LLMs EACL 2026 CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning WACV 2026 Deep Reinforcement Learning for Efficient and Fair Allocation of Healthcare Resources IJCAI 2025 WenyanGPT: A Large Language Model for Classical Chinese Tasks IJCAI 2025 Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data? ACL 2025 Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models ICML 2025 A First-order Generative Bilevel Optimization Framework for Diffusion Models ICML 2025 BaWA: Automatic Optimizing Pruning Metric for Large Language Models with Balanced Weight and Activation ICML 2025 Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow ICLR 2025 Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias ICLR 2025 IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation ICLR 2025 EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety EMNLP 2025 Temporal Consistency for LLM Reasoning Process Error Identification EMNLP 2025 Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data ICLR 2025 Collab: Controlled Decoding using Mixture of Agents for LLM Alignment ICLR 2025 Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment CVPR 2025 MATH-Perturb: Benchmarking LLMs’ Math Reasoning Abilities against Hard Perturbations ICML 2025 A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement ICLR 2025 Preacher: Paper-to-Video Agentic System ICCV 2025 TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling EMNLP 2025 MaxMin-RLHF: Alignment with Diverse Human Preferences ICML 2024 Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis AISTATS 2024 On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control JMLR 2024 Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight ICLR 2024 PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback ICLR 2024 Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective ICML 2024 Theoretical insights for diffusion guidance: A case study for Gaussian mixture models ICML 2024 Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds JMLR 2024 Global Convergence in Training Large-Scale Transformers NIPS 2024 Fast Best-of-N Decoding via Speculative Rejection NIPS 2024 FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling NIPS 2024 Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks NIPS 2024 Offline Multitask Representation Learning for Reinforcement Learning NIPS 2024 One-Layer Transformer Provably Learns One-Nearest Neighbor In Context NIPS 2024 Gradient Guidance for Diffusion Models: An Optimization Perspective NIPS 2024 Transfer Q-star : Principled Decoding for LLM Alignment NIPS 2024 A Theoretical Perspective for Speculative Decoding Algorithm NIPS 2024 Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications ICML 2024 Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization AAAI 2024 TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients AAAI 2024 Visual Adversarial Examples Jailbreak Aligned Large Language Models AAAI 2024 Information-Directed Pessimism for Offline Reinforcement Learning ICML 2024 Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling ICML 2024 Provable Benefits of Representational Transfer in Reinforcement Learning COLT 2023 Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation NIPS 2023 Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective NIPS 2023 Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations NIPS 2023 Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement NIPS 2023 Byzantine-Robust Online and Offline Distributed Reinforcement Learning AISTATS 2023 Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient ICLR 2023 Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment ICLR 2023 Deep Reinforcement Learning for Cost-Effective Medical Diagnosis ICLR 2023 Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks ICLR 2023 Representation Learning for Low-rank General-sum Markov Games ICLR 2023 STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning ICML 2023 Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data ICML 2023 Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP ICML 2023 Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories ICML 2023 Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition JMLR 2023 Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning JMLR 2023 Optimal Estimation of Policy Gradient via Double Fitted Iteration ICML 2022 Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach ICML 2022 Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory ICML 2022 Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks NIPS 2022 Communication Efficient Distributed Learning for Kernelized Contextual Bandits NIPS 2022 Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization NIPS 2022 Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic AAAI 2022 Offline stochastic shortest path: Learning, evaluation and towards optimality UAI 2022 Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism ICLR 2022 Parameter-Efficient Sparsity for Large Language Models Fine-Tuning IJCAI 2022 Online Sparse Reinforcement Learning AISTATS 2021 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient ICML 2021 Bootstrapping Fitted Q-Evaluation for Off-Policy Inference ICML 2021 On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method NIPS 2021 Contrastive Multi-document Question Generation EACL 2021 Towards Compact CNNs via Collaborative Compression CVPR 2021 Generalization Bounds for Stochastic Saddle Point Problems AISTATS 2021 High-Dimensional Sparse Linear Bandits NIPS 2020 Variational Policy Gradient Method for Reinforcement Learning with General Utilities NIPS 2020 A Duality Approach for Regret Minimization in Average-Award Ergodic Markov Decision Processes L4DC 2020 Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation ICML 2020 Model-Based Reinforcement Learning with Value-Targeted Regression ICML 2020 Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound ICML 2020 Model-Based Reinforcement Learning with Value-Targeted Regression L4DC 2020 Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity AISTATS 2020 Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations NIPS 2020 Generalized Leverage Score Sampling for Neural Networks NIPS 2020 Sketching Transformed Matrices with Applications to Natural Language Processing AISTATS 2020 Approximation Hardness for A Class of Sparse Optimization Problems JMLR 2019 Sample-Optimal Parametric Q-Learning Using Linearly Additive Features ICML 2019 Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python JMLR 2019 Learning low-dimensional state embeddings and metastable clusters from time series data NIPS 2019 State Aggregation Learning from Markov Transition Data NIPS 2019 Towards Coherent and Cohesive Long-form Text Generation NAACL 2019 Online Factorization and Partition of Complex Networks by Random Walk UAI 2019 Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization NIPS 2018 Scalable Bilinear Pi Learning Using State and Action Features ICML 2018 Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems AISTATS 2018 Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model NIPS 2018 Estimation of Markov Chain via Rank-Constrained Likelihood ICML 2018 Diffusion Approximations for Online Principal Component Estimation and Global Convergence NIPS 2017 Finite-sum Composition Optimization via Variance Reduced Gradient Descent AISTATS 2017 Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions ICML 2017 Accelerating Stochastic Composition Optimization JMLR 2017 Accelerating Stochastic Composition Optimization NIPS 2016