Locality Preserving Hashing. - Kang Zhao; Hongtao Lu; Jincheng Mei (AAAI, 2014)

Saliency Level Set Evolution. - Jincheng Mei; Bao-Liang Lu (ICONIP (2), 2014)

On Unconstrained Quasi-Submodular Function Optimization. - Jincheng Mei; Kang Zhao; Bao-Liang Lu (AAAI, 2015)

On the Reducibility of Submodular Functions. - Jincheng Mei; Hao Zhang; Bao-Liang Lu (AISTATS, 2016)

Discovering Author Interest Evolution in Topic Modeling. - Min Yang 007; Jincheng Mei; Fei Xu; Wenting Tu; Ziyu Lu (SIGIR, 2016)

Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters. - Min Yang 007; Jincheng Mei; Heng Ji; Wei Zhao 033; Zhou Zhao 001; Xiaojun Chen 006 (EMNLP, 2017)

Memory-Augmented Monte Carlo Tree Search. - Chenjun Xiao; Jincheng Mei; Martin Müller 003 (AAAI, 2018)

On Principled Entropy Exploration in Policy Optimization. - Jincheng Mei; Chenjun Xiao; Ruitong Huang; Dale Schuurmans; Martin Müller 003 (IJCAI, 2019)

Maximum Entropy Monte-Carlo Planning. - Chenjun Xiao; Ruitong Huang; Jincheng Mei; Dale Schuurmans; Martin Müller 003 (NeurIPS, 2019)

Frequency-based Search-control in Dyna. - Yangchen Pan; Jincheng Mei; Amir-massoud Farahmand (ICLR, 2020)

On the Global Convergence Rates of Softmax Policy Gradient Methods. - Jincheng Mei; Chenjun Xiao; Csaba Szepesvári; Dale Schuurmans (ICML, 2020)

Escaping the Gravitational Pull of Softmax. - Jincheng Mei; Chenjun Xiao; Bo Dai 001; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2020)

Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities. - Jincheng Mei; Yangchen Pan; Martha White; Amir-massoud Farahmand; Hengshuai Yao (CoRR, 2020)

Leveraging Non-uniformity in First-order Non-convex Optimization. - Jincheng Mei; Yue Gao; Bo Dai 001; Csaba Szepesvári; Dale Schuurmans (ICML, 2021)

On the Optimality of Batch Policy Optimization Algorithms. - Chenjun Xiao; Yifan Wu; Jincheng Mei; Bo Dai 001; Tor Lattimore; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (ICML, 2021)

Understanding the Effect of Stochasticity in Policy Optimization. - Jincheng Mei; Bo Dai 001; Chenjun Xiao; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2021)

Understanding and Leveraging Overparameterization in Recursive Value Estimation. - Chenjun Xiao; Bo Dai 001; Jincheng Mei; Oscar A. Ramirez; Ramki Gummadi; Chris Harris 001; Dale Schuurmans (ICLR, 2022)

The Role of Baselines in Policy Gradient Optimization. - Jincheng Mei; Wesley Chung; Valentin Thomas; Bo Dai 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2022)

On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games. - Runyu Zhang 001; Jincheng Mei; Bo Dai 001; Dale Schuurmans; Na Li 002 (NeurIPS, 2022)

Understanding and mitigating the limitations of prioritized experience replay. - Yangchen Pan; Jincheng Mei; Amir-massoud Farahmand; Martha White; Hengshuai Yao; Mohsen Rohani; Jun Luo 009 (UAI, 2022)

On the Effect of Log-Barrier Regularization in Decentralized Softmax Gradient Play in Multiagent Systems. - Runyu Zhang 001; Jincheng Mei; Bo Dai 001; Dale Schuurmans; Na Li 002 (CoRR, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - Tadashi Kozuno; Wenhao Yang; Nino Vieillard; Toshinori Kitamura; Yunhao Tang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Michal Valko; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (CoRR, 2022)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - Toshinori Kitamura; Tadashi Kozuno; Yunhao Tang; Nino Vieillard; Michal Valko; Wenhao Yang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

Stochastic Gradient Succeeds for Bandits. - Jincheng Mei; Zixin Zhong; Bo Dai 001; Alekh Agarwal; Csaba Szepesvári; Dale Schuurmans (ICML, 2023)

Ordering-based Conditions for Global Convergence of Policy Gradient Methods. - Jincheng Mei; Bo Dai 001; Alekh Agarwal; Mohammad Ghavamzadeh; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2023)

Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. - Fengdi Che; Chenjun Xiao; Jincheng Mei; Bo Dai 001; Ramki Gummadi; Oscar A. Ramirez; Christopher K. Harris; A. Rupam Mahmood; Dale Schuurmans (ICML, 2024)

Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates. - Jincheng Mei; Bo Dai 001; Alekh Agarwal; Sharan Vaswani; Anant Raj; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2024)

Beyond Expectations: Learning with Stochastic Dominance Made Practical. - Shicong Cen; Jincheng Mei; Hanjun Dai; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (CoRR, 2024)

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. - Tong Yang 007; Jincheng Mei; Hanjun Dai; Zixin Wen; Shicong Cen; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (CoRR, 2024)

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. - Tong Yang 007; Jincheng Mei; Hanjun Dai; Zixin Wen; Shicong Cen; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (AISTATS, 2025)

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF. - Shicong Cen; Jincheng Mei; Katayoon Goshvadi; Hanjun Dai; Tong Yang 007; Sherry Yang 001; Dale Schuurmans; Yuejie Chi; Bo Dai 001 (ICLR, 2025)

Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation. - Max Qiushi Lin; Jincheng Mei; Matin Aghaei; Michael Lu; Bo Dai 001; Alekh Agarwal; Dale Schuurmans; Csaba Szepesvári; Sharan Vaswani (CoRR, 2025)