Learning distributed jointly sparse systems by collaborative LMS. - Yuantao Gu; Mengdi Wang 001 (ICASSP, 2014)

Multi-task nonconvex optimization with total budget constraint: A distributed algorithm using Monte Carlo estimates. - Mengdi Wang 001; Yunjian Xu; Yuntao Gu (DSP, 2014)

Stabilization of Stochastic Iterative Methods for Singular and Nearly Singular Linear Systems. - Mengdi Wang 001; Dimitri P. Bertsekas (Math. Oper. Res., 2014)

Averaging random projection: A fast online solution for large-scale constrained stochastic optimization. - Jialin Liu 003; Yuantao Gu; Mengdi Wang 001 (ICASSP, 2015)

Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints. - Mengdi Wang 001; Yichen Chen; Jialin Liu 003; Yuantao Gu (CoRR, 2015)

A Distributed Tracking Algorithm for Reconstruction of Graph Signals. - Xiaohan Wang; Mengdi Wang 001; Yuantao Gu (IEEE J. Sel. Top. Signal Process., 2015)

Incremental constraint projection methods for variational inequalities. - Mengdi Wang 001; Dimitri P. Bertsekas (Math. Program., 2015)

An online primal-dual method for discounted Markov decision processes. - Mengdi Wang 001; Yichen Chen (CDC, 2016)

A stochastic compositional gradient method using Markov samples. - Mengdi Wang 001; Ji Liu 002 (WSC, 2016)

Stochastic First-Order Methods with Random Constraint Projection. - Mengdi Wang 001; Dimitri P. Bertsekas (SIAM J. Optim., 2016)

Finite-sum Composition Optimization via Variance Reduced Gradient Descent. - Xiangru Lian; Mengdi Wang 001; Ji Liu 002 (AISTATS, 2017)

Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions. - Yichen Chen; Dongdong Ge; Mengdi Wang 001; Zizhuo Wang 001; Yinyu Ye 001; Hao Yin (ICML, 2017)

Diffusion Approximations for Online Principal Component Estimation and Global Convergence. - Chris Junchi Li; Mengdi Wang 001; Tong Zhang 001 (NIPS, 2017)

Dynamic Factorization and Partition of Complex Networks. - Lin F. Yang; Vladimir Braverman; Tuo Zhao; Mengdi Wang 001 (CoRR, 2017)

Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions. - Mengdi Wang 001; Ethan X. Fang; Han Liu 001 (Math. Program., 2017)

Vanishing Price of Decentralization in Large Coordinative Nonconvex Optimization. - Mengdi Wang 001 (SIAM J. Optim., 2017)

Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems. - Jason Ge; Zhaoran Wang 001; Mengdi Wang 001; Han Liu 001 (AISTATS, 2018)

Efficient Deep Learning Inference Based on Model Compression. - Qing Zhang; Mengru Zhang; Mengdi Wang 001; Wanchen Sui; Chen Meng; Jun Yang 052; Weidan Kong; Xiaoyuan Cui; Wei Lin 016 (CVPR Workshops, 2018)

Estimation of Markov Chain via Rank-constrained Likelihood. - Xudong Li 004; Mengdi Wang 001; Anru Zhang (ICML, 2018)

Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. - Minshuo Chen; Lin Yang 011; Mengdi Wang 001; Tuo Zhao (NeurIPS, 2018)

Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. - Aaron Sidford; Mengdi Wang 001; Xian Wu 009; Lin Yang 011; Yinyu Ye 001 (NeurIPS, 2018)

Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. - Aaron Sidford; Mengdi Wang 001; Xian Wu 009; Yinyu Ye 001 (SODA, 2018)

State Compression of Markov Processes via Empirical Low-Rank Estimation. - Anru Zhang; Mengdi Wang 001 (CoRR, 2018)

Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. - Tianyi Lin; Chenyou Fan; Mengdi Wang 001; Michael I. Jordan (CoRR, 2018)

Diffusion Approximations for Online Principal Component Estimation and Global Convergence. - Chris Junchi Li; Mengdi Wang 001; Han Liu 001; Tong Zhang 001 (CoRR, 2018)

State Aggregation Learning from Markov Transition Data. - Yaqi Duan; Zheng Tracy Ke; Mengdi Wang 001 (CoRR, 2018)

Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks. - Mengdi Wang 001; Qing Zhang; Jun Yang 052; Xiaoyuan Cui; Wei Lin 016 (CoRR, 2018)

Near-optimal stochastic approximation for online principal component estimation. - Chris Junchi Li; Mengdi Wang 001; Han Liu 001; Tong Zhang 001 (Math. Program., 2018)

Learning to Control in Metric Space with Optimal Regret. - Chengzhuo Ni; Lin F. Yang; Mengdi Wang 001 (Allerton, 2019)

Sample-Optimal Parametric Q-Learning Using Linearly Additive Features. - Lin Yang 011; Mengdi Wang 001 (ICML, 2019)

Characterizing Deep Learning Training Workloads on Alibaba-PAI. - Mengdi Wang 001; Chen Meng; Guoping Long; Chuan Wu 001; Jun Yang 052; Wei Lin 016; Yangqing Jia (IISWC, 2019)

Maximum Likelihood Tensor Decomposition of Markov Decision Process. - Chengzhuo Ni; Mengdi Wang 001 (ISIT, 2019)

Learning low-dimensional state embeddings and metastable clusters from time series data. - Yifan Sun 009; Yaqi Duan; Hao Gong; Mengdi Wang 001 (NeurIPS, 2019)

Online Factorization and Partition of Complex Networks by Random Walk. - Lin F. Yang; Zheng Yu; Vladimir Braverman; Tuo Zhao; Mengdi Wang 001 (UAI, 2019)

Sample-Optimal Parametric Q-Learning with Linear Transition Models. - Lin F. Yang; Mengdi Wang 001 (CoRR, 2019)

Reinforcement Leaning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. - Lin F. Yang; Mengdi Wang 001 (CoRR, 2019)

Feature-Based Q-Learning for Two-Player Stochastic Games. - Zeyu Jia; Lin F. Yang; Mengdi Wang 001 (CoRR, 2019)

Voting-Based Multi-Agent Reinforcement Learning. - Yue Xu; Zengde Deng; Mengdi Wang 001; Wenjun Xu 001; Anthony Man-Cho So; Shuguang Cui (CoRR, 2019)

Continuous Control with Contexts, Provably. - Simon S. Du; Ruosong Wang; Mengdi Wang 001; Lin F. Yang (CoRR, 2019)

Approximation Hardness for A Class of Sparse Optimization Problems. - Yichen Chen; Yinyu Ye 001; Mengdi Wang 001 (J. Mach. Learn. Res., 2019)

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. - Jason Ge; Xingguo Li; Haoming Jiang; Han Liu 001; Tong Zhang 001; Mengdi Wang 001; Tuo Zhao (J. Mach. Learn. Res., 2019)

Blessing of massive scale: spatial graphical model estimation with a total cardinality constraint approach. - Ethan X. Fang; Han Liu 001; Mengdi Wang 001 (Math. Program., 2019)

Multilevel Stochastic Gradient Methods for Nested Composition Optimization. - Shuoguang Yang; Mengdi Wang 001; Ethan X. Fang (SIAM J. Optim., 2019)

Sketching Transformed Matrices with Applications to Natural Language Processing. - Yingyu Liang; Zhao Song 002; Mengdi Wang 001; Lin Yang 011; Xin Yang 017 (AISTATS, 2020)

Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. - Aaron Sidford; Mengdi Wang 001; Lin Yang 011; Yinyu Ye 001 (AISTATS, 2020)

Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. - Tianyi Lin; Chengyou Fan; Mengdi Wang 001; Michael I. Jordan (ACC, 2020)

A History-Based Auto-Tuning Framework for Fast and High-Performance DNN Design on GPU. - Jiandong Mu; Mengdi Wang 001; Lanbo Li; Jun Yang 052; Wei Lin 016; Wei Zhang 012 (DAC, 2020)

Model-Based Reinforcement Learning with Value-Targeted Regression. - Alex Ayoub; Zeyu Jia; Csaba Szepesvári; Mengdi Wang 001; Lin Yang 011 (ICML, 2020)

Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. - Lin Yang 011; Mengdi Wang 001 (ICML, 2020)

Model-Based Reinforcement Learning with Value-Targeted Regression. - Zeyu Jia; Lin Yang 011; Csaba Szepesvári; Mengdi Wang 001 (L4DC, 2020)

Fast Training of Deep Learning Models over Multiple GPUs. - Xiaodong Yi 001; Ziyue Luo; Chen Meng; Mengdi Wang 001; Guoping Long; Chuan Wu 001; Jun Yang 052; Wei Lin 016 (Middleware, 2020)

Generalized Leverage Score Sampling for Neural Networks. - Jason D. Lee; Ruoqi Shen; Zhao Song 002; Mengdi Wang 001; Zheng Yu (NeurIPS, 2020)

Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. - Zhuoran Yang; Chi Jin 001; Zhaoran Wang 001; Mengdi Wang 001; Michael I. Jordan (NeurIPS, 2020)

Variational Policy Gradient Method for Reinforcement Learning with General Utilities. - Junyu Zhang; Alec Koppel; Amrit Singh Bedi; Csaba Szepesvári; Mengdi Wang 001 (NeurIPS, 2020)

Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation. - Yaqi Duan; Mengdi Wang 001 (CoRR, 2020)

Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. - Zhuoran Yang; Chi Jin 001; Zhaoran Wang 001; Mengdi Wang 001; Michael I. Jordan (CoRR, 2020)

Randomized Linear Programming Solves the Markov Decision Problem in Nearly Linear (Sometimes Sublinear) Time. - Mengdi Wang 001 (Math. Oper. Res., 2020)

A Single Timescale Stochastic Approximation Method for Nested Stochastic Optimization. - Saeed Ghadimi; Andrzej Ruszczynski; Mengdi Wang 001 (SIAM J. Optim., 2020)

Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains. - Yaqi Duan; Mengdi Wang 001; Zaiwen Wen; Yaxiang Yuan (SIAM J. Matrix Anal. Appl., 2020)

Spectral State Compression of Markov Processes. - Anru Zhang; Mengdi Wang 001 (IEEE Trans. Inf. Theory, 2020)

Online Sparse Reinforcement Learning. - Botao Hao; Tor Lattimore; Csaba Szepesvári; Mengdi Wang 001 (AISTATS, 2021)

Generalization Bounds for Stochastic Saddle Point Problems. - Junyu Zhang; Mingyi Hong 001; Mengdi Wang 001; Shuzhong Zhang (AISTATS, 2021)

Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang 001; Alec Koppel (ACC, 2021)

Intermittent Communications in Decentralized Shadow Reward Actor-Critic. - Amrit Singh Bedi; Alec Koppel; Mengdi Wang 001; Junyu Zhang (CDC, 2021)

Towards Compact CNNs via Collaborative Compression. - Yuchao Li; Shaohui Lin; Jianzhuang Liu; Qixiang Ye; Mengdi Wang 001; Fei Chao 001; Fan Yang 016; Jincheng Ma; Qi Tian 001; Rongrong Ji (CVPR, 2021)

Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. - Botao Hao; Yaqi Duan; Tor Lattimore; Csaba Szepesvári; Mengdi Wang 001 (ICML, 2021)

Bootstrapping Fitted Q-Evaluation for Off-Policy Inference. - Botao Hao; Xiang Ji; Yaqi Duan; Hao Lu; Csaba Szepesvári; Mengdi Wang 001 (ICML, 2021)

Learning Good State and Action Representations via Tensor Decomposition. - Chengzhuo Ni; Anru R. Zhang; Yaqi Duan; Mengdi Wang 001 (ISIT, 2021)

On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. - Junyu Zhang; Chengzhuo Ni; Zheng Yu; Csaba Szepesvári; Mengdi Wang 001 (NeurIPS, 2021)

Bootstrapping Statistical Inference for Off-Policy Evaluation. - Botao Hao; Xiang Ji; Yaqi Duan; Hao Lu; Csaba Szepesvári; Mengdi Wang 001 (CoRR, 2021)

1×N Block Pattern for Network Sparsity. - Mingbao Lin; Yuchao Li; Yuxin Zhang 002; Bohong Chen 001; Fei Chao 001; Mengdi Wang 001; Shen Li; Jun Yang 052; Rongrong Ji (CoRR, 2021)

MARL with General Utilities via Decentralized Shadow Reward Actor-Critic. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang 001; Alec Koppel (CoRR, 2021)

You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient. - Shaokun Zhang; Xiawu Zheng; Chenyi Yang; Yuchao Li; Yan Wang 059; Fei Chao 001; Mengdi Wang 001; Shen Li; Jun Yang 052; Rongrong Ji (CoRR, 2021)

Voting-Based Multiagent Reinforcement Learning for Intelligent IoT. - Yue Xu; Zengde Deng; Mengdi Wang 001; Wenjun Xu 001; Anthony Man-Cho So; Shuguang Cui (IEEE Internet Things J., 2021)

Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang 001; Alec Koppel (IEEE J. Sel. Areas Inf. Theory, 2021)

Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang 001; Alec Koppel (AAAI, 2022)

Neural Bandits for Protein Sequence Optimization. - Chenyu Wang; Joseph C. Kim; Le Cong; Mengdi Wang 001 (CISS, 2022)

Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. - Ming Yin 003; Yaqi Duan; Mengdi Wang 001; Yu-Xiang Wang 003 (ICLR, 2022)

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. - Xuezhou Zhang; Yuda Song 001; Masatoshi Uehara; Mengdi Wang 001; Alekh Agarwal; Wen Sun 002 (ICML, 2022)

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. - Hui Yuan 002; Chengzhuo Ni; Huazheng Wang; Xuezhou Zhang; Le Cong; Csaba Szepesvári; Mengdi Wang 001 (NeurIPS, 2022)

Offline stochastic shortest path: Learning, evaluation and towards optimality. - Ming Yin 003; Wenjing Chen; Mengdi Wang 001; Yu-Xiang Wang 003 (UAI, 2022)

Parameter-Efficient Sparsity for Large Language Models Fine-Tuning. - Yuchao Li; Fuli Luo; Chuanqi Tan; Mengdi Wang 001; Songfang Huang; Shen Li; Junjie Bai (CoRR, 2022)

Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. - Xiang Ji; Minshuo Chen; Mengdi Wang 001; Tuo Zhao (CoRR, 2022)

Communication Efficient Distributed Learning for Kernelized Contextual Bandits. - Chuanhao Li; Huazheng Wang; Mengdi Wang 001; Hongning Wang (CoRR, 2022)

Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization. - Kaixuan Huang; Yu Wu; Xuezhou Zhang; Shenyinying Tu; Qingyun Wu; Mengdi Wang 001; Huazheng Wang (CoRR, 2022)

Representation Learning for General-sum Low-rank Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Chi Jin 001; Mengdi Wang 001 (CoRR, 2022)

Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality. - Le Xie 001; Tong Huang; Xiangtian Zheng 002; Yan Liu 002; Mengdi Wang 001; Vijay Vittal; P. R. Kumar 001; Srinivas Shakkottai; Yi Cui (CoRR, 2022)

Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP. - Jinghan Wang; Mengdi Wang 001; Lin F. Yang (CoRR, 2022)

Learning Markov Models Via Low-Rank Optimization. - Ziwei Zhu; Xudong Li 004; Mengdi Wang 001; Anru Zhang (Oper. Res., 2022)

Energy system digitization in the era of AI: A three-layered approach toward carbon neutrality. - Le Xie 001; Tong Huang; Xiangtian Zheng 002; Yan Liu 002; Mengdi Wang 001; Vijay Vittal; P. R. Kumar 001; Srinivas Shakkottai; Yi Cui (Patterns, 2022)

Byzantine-Robust Online and Offline Distributed Reinforcement Learning. - Yiding Chen; Xuezhou Zhang; Kaiqing Zhang; Mengdi Wang 001; Xiaojin Zhu 001 (AISTATS, 2023)

Provable Benefits of Representational Transfer in Reinforcement Learning. - Alekh Agarwal; Yuda Song 001; Wen Sun 002; Kaiwen Wang; Mengdi Wang 001; Xuezhou Zhang (COLT, 2023)

Representation Learning for Low-rank General-sum Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Zihan Ding; Chi Jin 001; Mengdi Wang 001 (ICLR, 2023)

Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. - Ming Yin 003; Mengdi Wang 001; Yu-Xiang Wang 003 (ICLR, 2023)

Deep Reinforcement Learning for Cost-Effective Medical Diagnosis. - Zheng Yu; Yikuan Li; Joseph C. Kim; Kaixuan Huang; Yuan Luo 001; Mengdi Wang 001 (ICLR, 2023)

STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. - Souradip Chakraborty; Amrit S. Bedi; Alec Koppel; Mengdi Wang 001; Furong Huang; Dinesh Manocha (ICML, 2023)

Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. - Minshuo Chen; Yu Bai 017; H. Vincent Poor; Mengdi Wang 001 (NeurIPS, 2023)

Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. - Nikki Lijing Kuang; Ming Yin 003; Mengdi Wang 001; Yu-Xiang Wang 003; Yian Ma (NeurIPS, 2023)

Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. - Hui Yuan 002; Kaixuan Huang; Chengzhuo Ni; Minshuo Chen; Mengdi Wang 001 (NeurIPS, 2023)

Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective. - Zeyu Zhang; Yi Su; Hui Yuan 002; Yiran Wu; Rishab Balasubramanian; Qingyun Wu; Huazheng Wang; Mengdi Wang 001 (NeurIPS, 2023)

Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. - Minshuo Chen; Kaixuan Huang; Tuo Zhao; Mengdi Wang 001 (CoRR, 2023)

Visual Adversarial Examples Jailbreak Large Language Models. - Xiangyu Qi; Kaixuan Huang; Ashwinee Panda; Mengdi Wang 001; Prateek Mittal (CoRR, 2023)

Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. - Kaiqi Zhang 002; Zixuan Zhang; Minshuo Chen; Mengdi Wang 001; Tuo Zhao; Yu-Xiang Wang 003 (CoRR, 2023)

Scaling In-Context Demonstrations with Structured Attention. - Tianle Cai; Kaixuan Huang; Jason D. Lee; Mengdi Wang 001 (CoRR, 2023)

Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems. - Xiang Ji; Huazheng Wang; Minshuo Chen; Tuo Zhao; Mengdi Wang 001 (CoRR, 2023)

Aligning Agent Policy with Externalities: Reward Design via Bilevel RL. - Souradip Chakraborty; Amrit Singh Bedi; Alec Koppel; Dinesh Manocha; Huazheng Wang; Furong Huang; Mengdi Wang 001 (CoRR, 2023)

Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources. - Yikuan Li; Chengsheng Mao; Kaixuan Huang; Hanyin Wang; Zheng Yu; Mengdi Wang 001; Yuan Luo 001 (CoRR, 2023)

Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. - Zhenghao Xu; Xiang Ji; Minshuo Chen; Mengdi Wang 001; Tuo Zhao (CoRR, 2023)

Federated Multi-Level Optimization over Decentralized Networks. - Shuoguang Yang; Xuezhou Zhang; Mengdi Wang 001 (CoRR, 2023)

Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks. - Zihao Li; Xiang Ji; Minshuo Chen; Mengdi Wang 001 (CoRR, 2023)

Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? - Lei Zhao; Mengdi Wang 001; Yu Bai 017 (CoRR, 2023)

Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning. - Zihao Li; Boyi Liu 001; Zhuoran Yang; Zhaoran Wang 001; Mengdi Wang 001 (J. Mach. Learn. Res., 2023)

Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition. - Chengzhuo Ni; Yaqi Duan; Munther A. Dahleh; Mengdi Wang 001; Anru R. Zhang (J. Mach. Learn. Res., 2023)

1xN Pattern for Pruning Convolutional Neural Networks. - Mingbao Lin; Yuxin Zhang 002; Yuchao Li; Bohong Chen 001; Fei Chao 001; Mengdi Wang 001; Shen Li; Yonghong Tian 001; Rongrong Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Primal-Dual First-Order Methods for Affinely Constrained Multi-block Saddle Point Problems. - Junyu Zhang; Mengdi Wang 001; Mingyi Hong 001; Shuzhong Zhang (SIAM J. Optim., 2023)

Visual Adversarial Examples Jailbreak Aligned Large Language Models. - Xiangyu Qi; Kaixuan Huang; Ashwinee Panda; Peter Henderson 002; Mengdi Wang 001; Prateek Mittal (AAAI, 2024)

Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization. - Jiahao Qiu; Hui Yuan 002; Jinghong Zhang; Wentao Chen; Huazheng Wang; Mengdi Wang 001 (AAAI, 2024)

PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback. - Souradip Chakraborty; Amrit Singh Bedi; Alec Koppel; Huazheng Wang; Dinesh Manocha; Mengdi Wang 001; Furong Huang (ICLR, 2024)

Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. - Jiacheng Guo; Minshuo Chen; Huan Wang 014; Caiming Xiong; Mengdi Wang 001; Yu Bai 017 (ICLR, 2024)

MaxMin-RLHF: Alignment with Diverse Human Preferences. - Souradip Chakraborty; Jiahao Qiu; Hui Yuan 002; Alec Koppel; Dinesh Manocha; Furong Huang; Amrit S. Bedi; Mengdi Wang 001 (ICML, 2024)

Information-Directed Pessimism for Offline Reinforcement Learning. - Alec Koppel; Sujay Bhatt; Jiacheng Guo; Joe Eappen; Mengdi Wang 001; Sumitra Ganesh (ICML, 2024)

Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications. - Boyi Wei; Kaixuan Huang; Yangsibo Huang; Tinghao Xie; Xiangyu Qi; Mengzhou Xia; Prateek Mittal; Mengdi Wang 001; Peter Henderson 002 (ICML, 2024)

Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective. - Lei Zhao; Mengdi Wang 001; Yu Bai 017 (ICML, 2024)

Conversational Dueling Bandits in Generalized Linear Models. - Shuhua Yang; Hui Yuan 002; Xiaoying Zhang; Mengdi Wang 001; Hong Zhang 037; Huazheng Wang (KDD, 2024)

A Theoretical Perspective for Speculative Decoding Algorithm. - Ming Yin 003; Minshuo Chen; Kaixuan Huang; Mengdi Wang 001 (NeurIPS, 2024)

Transfer Q-star : Principled Decoding for LLM Alignment. - Souradip Chakraborty; Soumya Suvra Ghosal; Ming Yin 003; Dinesh Manocha; Mengdi Wang 001; Amrit Singh Bedi; Furong Huang (NeurIPS, 2024)

Gradient Guidance for Diffusion Models: An Optimization Perspective. - Yingqing Guo; Hui Yuan 002; Yukang Yang; Minshuo Chen; Mengdi Wang 001 (NeurIPS, 2024)

Offline Multitask Representation Learning for Reinforcement Learning. - Haque Ishfaq; Thanh Nguyen-Tang; Songtao Feng; Raman Arora; Mengdi Wang 001; Ming Yin 003; Doina Precup (NeurIPS, 2024)

Fast Best-of-N Decoding via Speculative Rejection. - Hanshi Sun; Momin Haider; Ruiqi Zhang; Huitao Yang; Jiahao Qiu; Ming Yin 003; Mengdi Wang 001; Peter L. Bartlett; Andrea Zanette (NeurIPS, 2024)

Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. - Zixuan Zhang; Kaiqi Zhang 002; Minshuo Chen; Yuma Takeda; Mengdi Wang 001; Tuo Zhao; Yu-Xiang Wang 003 (NeurIPS, 2024)

Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer. - Yitao Lu; Qian Chu; Zhen Li 013; Mengdi Wang 001; Robert A. Gatenby; Qingpeng Zhang (Briefings Bioinform., 2024)

MaxMin-RLHF: Towards Equitable Alignment of Large Language Models with Diverse Human Preferences. - Souradip Chakraborty; Jiahao Qiu; Hui Yuan 002; Alec Koppel; Furong Huang; Dinesh Manocha; Amrit Singh Bedi; Mengdi Wang 001 (CoRR, 2024)

Regularized DeepIV with Model Selection. - Zihao Li; Hui Lan; Vasilis Syrgkanis; Mengdi Wang 001; Masatoshi Uehara (CoRR, 2024)

Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory. - Hengyu Fu; Zhuoran Yang; Mengdi Wang 001; Minshuo Chen (CoRR, 2024)

Diffusion Model for Data-Driven Black-Box Optimization. - Zihao Li; Hui Yuan 002; Kaixuan Huang; Chengzhuo Ni; Yinyu Ye 001; Minshuo Chen; Mengdi Wang 001 (CoRR, 2024)

AI Risk Management Should Incorporate Both Safety and Security. - Xiangyu Qi; Yangsibo Huang; Yi Zeng 005; Edoardo Debenedetti; Jonas Geiping; Luxi He; Kaixuan Huang; Udari Madhushani; Vikash Sehwag; Weijia Shi; Boyi Wei; Tinghao Xie; et al. (CoRR, 2024)

SAIL: Self-Improving Efficient Online Alignment of Large Language Models. - Mucong Ding; Souradip Chakraborty; Vibhu Agrawal; Zora Che; Alec Koppel; Mengdi Wang 001; Amrit S. Bedi; Furong Huang (CoRR, 2024)

Relative-Translation Invariant Wasserstein Distance. - Binshuai Wang; Qiwei Di; Ming Yin 003; Mengdi Wang 001; Quanquan Gu; Peng Wei (CoRR, 2024)

AIME: AI System Optimization via Multiple LLM Evaluators. - Bhrij Patel; Souradip Chakraborty; Wesley A. Suttle; Mengdi Wang 001; Amrit Singh Bedi; Dinesh Manocha (CoRR, 2024)

LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds. - James Beetham; Souradip Chakraborty; Mengdi Wang 001; Furong Huang; Amrit Singh Bedi; Mubarak Shah (CoRR, 2024)

On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. - Amrit Singh Bedi; Anjaly Parayil; Junyu Zhang; Mengdi Wang 001; Alec Koppel (J. Mach. Learn. Res., 2024)

A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions. - Yanyi Chu; Dan Yu; Yupeng Li; Kaixuan Huang; Yue Shen; Le Cong; Jason Zhang; Mengdi Wang 001 (Nat. Mac. Intell., 2024)

Author Correction: A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions. - Yanyi Chu; Dan Yu; Yupeng Li; Kaixuan Huang; Yue Shen; Le Cong; Jason Zhang; Mengdi Wang 001 (Nat. Mac. Intell., 2024)

Adversarial Attacks on Online Learning to Rank with Stochastic Click Models. - Zichen Wang; Rishab Balasubramanian; Hui Yuan 002; Chenyu Song; Mengdi Wang 001; Huazheng Wang (Trans. Mach. Learn. Res., 2024)

Boosting the Convergence of Reinforcement Learning-Based Auto-Pruning Using Historical Data. - Jiandong Mu; Mengdi Wang 001; Feiwen Zhu; Jun Yang 052; Wei Lin 016; Wei Zhang 012 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2024)

Teamwork Reinforcement Learning With Concave Utilities. - Zheng Yu; Junyu Zhang; Zheng Wen; Andrea Tacchetti; Mengdi Wang 001; Ian Gemp (IEEE Trans. Mob. Comput., 2024)

Redefining the Game: MVAE-DFDPnet's Low-Dimensional Embeddings for Superior Drug-Protein Interaction Predictions. - Liang-Yong Xia; Yu Wu; Longfei Zhao; Leying Chen; Shiyi Zhang; Mengdi Wang 001; Jie Luo 010 (IEEE J. Biomed. Health Informatics, 2024)

Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. - Minshuo Chen; Jie Meng; Yu Bai 017; Yinyu Ye 001; H. Vincent Poor; Mengdi Wang 001 (IEEE Trans. Inf. Theory, 2024)

Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment. - Soumya Suvra Ghosal; Souradip Chakraborty; Vaibhav Singh; Tianrui Guan; Mengdi Wang 001; Ahmad Beirami; Furong Huang; Alvaro Velasquez; Dinesh Manocha; Amrit Singh Bedi (CVPR, 2025)

A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement. - Hui Yuan 002; Yifan Zeng; Yue Wu; Huazheng Wang; Mengdi Wang 001; Liu Leqi (ICLR, 2025)

Collab: Controlled Decoding using Mixture of Agents for LLM Alignment. - Souradip Chakraborty; Sujay Bhatt; Udari Madhushani Sehwag; Soumya Suvra Ghosal; Jiahao Qiu; Mengdi Wang 001; Dinesh Manocha; Furong Huang; Alec Koppel; Sumitra Ganesh (ICLR, 2025)

Deep Reinforcement Learning for Efficient and Fair Allocation of Healthcare Resources. - Yikuan Li; Chengsheng Mao; Kaixuan Huang; Hanyin Wang; Zheng Yu; Mengdi Wang 001; Yuan Luo 001 (IJCAI, 2025)

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. - Kaixuan Huang; Jiacheng Guo; Zihao Li; Xiang Ji; Jiawei Ge 003; Wenzhe Li; Yingqing Guo; Tianle Cai; Hui Yuan 002; Runzhe Wang; Yue Wu; Ming Yin 003; et al. (CoRR, 2025)

NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models. - Lawrence Liu; Inesh Chakrabarti; Yixiao Li; Mengdi Wang 001; Tuo Zhao; Lin F. Yang (CoRR, 2025)

OTC: Optimal Tool Calls via Reinforcement Learning. - Hongru Wang 003; Cheng Qian 008; Wanjun Zhong; Xiusi Chen; Jiahao Qiu; Shijue Huang; Bowen Jin; Mengdi Wang 001; Kam-Fai Wong; Heng Ji 001 (CoRR, 2025)

On Path to Multimodal Historical Reasoning: HistBench and HistAgent. - Jiahao Qiu; Fulian Xiao; Yimin Wang; Yuchen Mao; Yijia Chen; Xinzhe Juan; Siran Wang; Xuan Qi; Tongcheng Zhang; Zixin Yao; Jiacheng Guo; Yifu Lu; et al. (CoRR, 2025)

Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution. - Jiahao Qiu; Xuan Qi; Tongcheng Zhang; Xinzhe Juan; Jiacheng Guo; Yifu Lu; Yimin Wang; Zixin Yao; Qihan Ren; Xun Jiang; Xing Zhou; Dongrui Liu; et al. (CoRR, 2025)

Toward a Theory of Agents as Tool-Use Decision-Makers. - Hongru Wang 003; Cheng Qian 008; Manling Li; Jiahao Qiu; Boyang Xue; Mengdi Wang 001; Heng Ji 001; Kam-Fai Wong (CoRR, 2025)

Does Thinking More always Help? Understanding Test-Time Scaling in Reasoning Models. - Soumya Suvra Ghosal; Souradip Chakraborty; Avinash Reddy; Yifu Lu; Mengdi Wang 001; Dinesh Manocha; Furong Huang; Mohammad Ghavamzadeh; Amrit Singh Bedi (CoRR, 2025)

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes. - Jiahao Qiu; Xinzhe Juan; Yimin Wang; Ling Yang; Xuan Qi; Tongcheng Zhang; Jiacheng Guo; Yifu Lu; Zixin Yao; Hongru Wang 003; Shilong Liu; Xun Jiang; et al. (CoRR, 2025)

A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence. - Huan-ang Gao; Jiayi Geng; Wenyue Hua; Mengkang Hu; Xinzhe Juan; Hongzhang Liu; Shilong Liu; Jiahao Qiu; Xuan Qi; Yiran Wu; Hongru Wang 003; Han Xiao; et al. (CoRR, 2025)

Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025. - Jiahao Qiu; Jingzhe Shi; Xinzhe Juan; Zelin Zhao; Jiayi Geng; Shilong Liu; Hongru Wang 003; Sanfeng Wu; Mengdi Wang 001 (CoRR, 2025)