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Jincheng Mei
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2020 – today
- 2024
- [i16]Shicong Cen, Jincheng Mei, Hanjun Dai, Dale Schuurmans, Yuejie Chi, Bo Dai:
Beyond Expectations: Learning with Stochastic Dominance Made Practical. CoRR abs/2402.02698 (2024) - [i15]Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. CoRR abs/2402.17235 (2024) - [i14]Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai, Tong Yang, Sherry Yang, Dale Schuurmans, Yuejie Chi, Bo Dai:
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF. CoRR abs/2405.19320 (2024) - [i13]Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. CoRR abs/2405.21043 (2024) - 2023
- [c22]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, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo:
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML 2023: 17135-17175 - [c21]Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. ICML 2023: 24325-24360 - [c20]Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvári, Dale Schuurmans:
Ordering-based Conditions for Global Convergence of Policy Gradient Methods. NeurIPS 2023 - [i12]Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
The Role of Baselines in Policy Gradient Optimization. CoRR abs/2301.06276 (2023) - [i11]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, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo:
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR abs/2305.13185 (2023) - 2022
- [c19]Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar A Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans:
Understanding and Leveraging Overparameterization in Recursive Value Estimation. ICLR 2022 - [c18]Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
The Role of Baselines in Policy Gradient Optimization. NeurIPS 2022 - [c17]Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games. NeurIPS 2022 - [c16]Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand, Martha White, Hengshuai Yao, Mohsen Rohani, Jun Luo:
Understanding and mitigating the limitations of prioritized experience replay. UAI 2022: 1561-1571 - [i10]Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Effect of Log-Barrier Regularization in Decentralized Softmax Gradient Play in Multiagent Systems. CoRR abs/2202.00872 (2022) - [i9]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, Csaba Szepesvári:
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR abs/2205.14211 (2022) - 2021
- [c15]Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
Leveraging Non-uniformity in First-order Non-convex Optimization. ICML 2021: 7555-7564 - [c14]Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. ICML 2021: 11362-11371 - [c13]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. NeurIPS 2021: 19339-19351 - [i8]Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. CoRR abs/2104.02293 (2021) - [i7]Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans:
Leveraging Non-uniformity in First-order Non-convex Optimization. CoRR abs/2105.06072 (2021) - [i6]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. CoRR abs/2110.15572 (2021) - 2020
- [c12]Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand:
Frequency-based Search-control in Dyna. ICLR 2020 - [c11]Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
On the Global Convergence Rates of Softmax Policy Gradient Methods. ICML 2020: 6820-6829 - [c10]Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
Escaping the Gravitational Pull of Softmax. NeurIPS 2020 - [i5]Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand:
Frequency-based Search-control in Dyna. CoRR abs/2002.05822 (2020) - [i4]Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
On the Global Convergence Rates of Softmax Policy Gradient Methods. CoRR abs/2005.06392 (2020) - [i3]Jincheng Mei, Yangchen Pan, Martha White, Amir-massoud Farahmand, Hengshuai Yao:
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities. CoRR abs/2007.09569 (2020)
2010 – 2019
- 2019
- [c9]Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Müller:
On Principled Entropy Exploration in Policy Optimization. IJCAI 2019: 3130-3136 - [c8]Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller:
Maximum Entropy Monte-Carlo Planning. NeurIPS 2019: 9516-9524 - 2018
- [c7]Chenjun Xiao, Jincheng Mei, Martin Müller:
Memory-Augmented Monte Carlo Tree Search. AAAI 2018: 1455-1462 - 2017
- [c6]Min Yang, Jincheng Mei, Heng Ji, Wei Zhao, Zhou Zhao, Xiaojun Chen:
Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters. EMNLP 2017: 527-533 - 2016
- [c5]Jincheng Mei, Hao Zhang, Bao-Liang Lu:
On the Reducibility of Submodular Functions. AISTATS 2016: 186-194 - [c4]Min Yang, Jincheng Mei, Fei Xu, Wenting Tu, Ziyu Lu:
Discovering Author Interest Evolution in Topic Modeling. SIGIR 2016: 801-804 - [i2]Jincheng Mei, Hao Zhang, Bao-Liang Lu:
On the Reducibility of Submodular Functions. CoRR abs/1601.00393 (2016) - 2015
- [c3]Jincheng Mei, Kang Zhao, Bao-Liang Lu:
On Unconstrained Quasi-Submodular Function Optimization. AAAI 2015: 1191-1197 - 2014
- [c2]Kang Zhao, Hongtao Lu, Jincheng Mei:
Locality Preserving Hashing. AAAI 2014: 2874-2881 - [c1]Jincheng Mei, Bao-Liang Lu
:
Saliency Level Set Evolution. ICONIP (2) 2014: 170-177 - [i1]Jincheng Mei, Kang Zhao, Bao-Liang Lu:
Unconstrained Quasi-Submodular Function Optimization. CoRR abs/1408.4389 (2014)
Coauthor Index
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