BIMSA > Yuan Zhou
Associate Professor Yuan Zhou

Affiliation: YMSC , BIMSA

Group: Artificial Intelligence and Machine Learning

Office: A6-106

Email: zhouyuan@bimsa.cn

Research Field: Machine learning theory, operations research & management, theoretical computer science

Education Experience

  • 2009 - 2014      Carnegie Mellon University      Computer Science      Ph.D
  • 2009 - 2013      Carnegie Mellon University      Computer Science      M.Sc.
  • 2009 - 2009      Tsinghua University      Computer Science      Bachelor

Work Experience

  • 2021 -      Yau Mathematical Sciences Center, Tsinghua University      Associate Professor
  • 2019 - 2021      Department of ISE, University of Illinois Urbana-Champaign      Assistant Professor
  • 2016 - 2019      Computer Science Department, Indiana University at Bloomington      Assistant Professor
  • 2014 - 2016      Department of Mathematics, Massachusetts Institute of Technology      Instructor in Applied Mathematics

Publication

  • [1] Zihan Zhang, Xiangyang Ji, Yuan Zhou, Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits, The Thirteenth International Conference on Learning Representations (ICLR) (2025)
  • [2] Xuefeng Zhang, Haowei Lin, Muhan Zhang, Yuan Zhou, Jianzhu Ma, A data-driven group retrosynthesis planning model inspired by neurosymbolic programming, Nature Communications, 16 (2025)
  • [3] Xi Chen, David Simichi-Levi, Zishuo Zhao, Yuan Zhou, Bayesian Mechanism Design for Blockchain Transaction Fee Allocation, accepted by Operations Research (2024)
  • [4] Xi Chen, Jiameng Lyu, Xuan Zhang, Yuan Zhou, Fairness-aware Online Price Discrimination with Nonparametric Demand Models, accepted by Operations Research (2024)
  • [5] Jiameng Lyu, Jinxing Xie, Shilin Yuan, Yuan Zhou, A Minibatch-SGD-based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy, Management Science (2024)
  • [6] Boxiao Chen, Yining Wang, Yuan Zhou, Optimal Policies for Dynamic Pricing and Inventory Control with Nonparametric Censored Demands, Management Science, 70(5), 3362-3380 (2024)
  • [7] Xi Chen, Jiameng Lyu, Yining Wang, and Yuan Zhou, Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing, Production and Operations Management, 33(2024), 2, 494-511
  • [8] Yingkai Li, Yining Wang, and Yuan Zhou, Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits, IEEE Transactions on Information Theory, 70(2024), 1, 372-388
  • [9] Yijie Wang, Yuan Zhou, Xiaoqing Huang, Kun Huang, Jie Zhang, and Jianzhu Ma, Learning Sparse Group Models through Boolean Relaxation, The Eleventh International Conference on Learning Representations (ICLR), Virtual Event(2023)
  • [10] Jinpeng Zhang, Yufeng Zheng, Chuheng Zhang, Li Zhao, Lei Song, Yuan Zhou, and Jiang Bian, Robust Situational Reinforcement Learning in Face of Context Disturbances, Proceedings of the International Conference on Machine Learning, PMLR, 202, 41973-41989 (2023)
  • [11] Boxiao Chen, David Simchi-Levi, Yining Wang, and Yuan Zhou, Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information, Management Science, 68(2022), 8, 5684-5703
  • [12] Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, and Jian Peng, Off-Policy Reinforcement Learning with Delayed Rewards, Proceedings of the International Conference on Machine Learning, PMLR, 162, 8280-8303 (2022)
  • [13] Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, and Jian Peng, Proximal Exploration for Model-guided Protein Sequence Design, Proceedings of the International Conference on Machine Learning, PMLR, 162, 18520-18536 (2022)
  • [14] Zihan Zhang, Yuhang Jiang, Yuan Zhou, and Xiangyang Ji, Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning, Advances in Neural Information Processing Systems, 35(2022), 24586-24596
  • [15] Zhizhou Ren, Ruihan Guo, Yuan Zhou, and Jian Peng, Learning Long-term Reward Redistribution via Randomized Return Decomposition, The Tenth International Conference on Learning Representations (ICLR), Virtual Event(2022)
  • [16] Tanmay Gangwani, Yuan Zhou, and Jian Peng, Imitation Learning from Observations under Transition Model Disparity, The Tenth International Conference on Learning Representations (ICLR), Virtual Event(2022)
  • [17] Xi Chen, Yining Wang, and Yuan Zhou, Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models, Mathematics of Operations Research, 46(2021), 4, 1639-1657
  • [18] Yufei Ruan, Jiaqi Yang, and Yuan Zhou, Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design, Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (STOC), Rome, Italy(2021), 74-87
  • [19] Xi Chen, Chao Shi, Yining Wang, and Yuan Zhou, Dynamic Assortment Planning Under Nested Logit Models, Production and Operations Management, 30(2021), 1, 85-102
  • [20] Tanmay Gangwani, Jian Peng, and Yuan Zhou, Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity, Proceedings of the Conference on Robot Learning, PMLR, 155, 2206-2215 (2021)
  • [21] Zihan Zhang, Yuan Zhou, and Xiangyang Ji, Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity, Proceedings of the International Conference on Machine Learning, PMLR, 139, 12653-12662 (2021)
  • [22] Guangyu Xi, Chao Tao, and Yuan Zhou, Near-Optimal MNL Bandits Under Risk Criteria, Proceedings of the AAAI Conference on Artificial Intelligence, 35(2021), 12, 10397-10404
  • [23] Kefan Dong, Jian Peng, Yining Wang, and Yuan Zhou, Root-n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank, Proceedings of the Conference on Learning Theory, PMLR, 125, 1554-1557 (2020)
  • [24] Kefan Dong, Yingkai Li, Qin Zhang, and Yuan Zhou, Multinomial Logit Bandit with Low Switching Cost, Proceedings of the International Conference on Machine Learning, PMLR, 119, 2607-2615 (2020)
  • [25] Xi Chen, Yining Wang, and Yuan Zhou, Dynamic Assortment Optimization with Changing Contextual Information, Journal of Machine Learning Research, 21(2020), 1-44
  • [26] Zihan Zhang, Yuan Zhou, and Xiangyang Ji, Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition, Advances in Neural Information Processing Systems, 33(2020), 15198-15207
  • [27] Nikolai Karpov, Qin Zhang, and Yuan Zhou, Collaborative Top Distribution Identifications with Limited Interaction, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), Durham, NC, USA(2020), 160-171
  • [28] Tanmay Gangwani, Yuan Zhou, and Jian Peng, Learning Guidance Rewards with Trajectory-space Smoothing, Advances in Neural Information Processing Systems, 33(2020), 822-832
  • [29] Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang, and Yuan Zhou, Adaptive Double-Exploration Tradeoff for Outlier Detection, Proceedings of the AAAI Conference on Artificial Intelligence, 34(2020), 04, 6837-6844
  • [30] Xi Chen, Tengyu Ma, Jiawei Zhang, and Yuan Zhou, Optimal Design of Process Flexibility for General Production Systems, Operations Research, 67(2019), 2, 516-531
  • [31] Chao Tao, Saúl A. Blanco, Jean Peng, and Yuan Zhou, Thresholding Bandit with Optimal Aggregate Regret, Advances in Neural Information Processing Systems, 32(2019), 11664-11673
  • [32] Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, and Jian Peng, Exploration via Hindsight Goal Generation, Advances in Neural Information Processing Systems(2019)
  • [33] Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, and Jian Peng, Off-policy evaluation and learning from logged bandit feedback: Error reduction via surrogate policy, The Seventh International Conference on Learning Representations (ICLR), Virtual Event(2019)
  • [34] Chao Tao, Qin Zhang, and Yuan Zhou, Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits, IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), Baltimore, Maryland(2019)
  • [35] Yining Wang, Xi Chen, and Yuan Zhou, Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models, NeurIPS 2018(2018)
  • [36] Jiecao Chen, Qin Zhang, and Yuan Zhou, Tight Bounds for Collaborative PAC Learning via Multiplicative Weights, NeurIPS 2018(2018)
  • [37] Chao Tao, Saúl A. Blanco, and Yuan Zhou, Best Arm Identification in Linear Bandits with Linear Dimension Dependency, Proceedings of the 35th International Conference on Machine Learning, PMLR(2018)
  • [38] Xue Chen, and Zhou Yuan, Parameterized Algorithms for Constraint Satisfaction Problems Above Average with Global Cardinality Constraints,, Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)(2017)
  • [39] Jiecao Chen, Xi Chen, Qin Zhang, and Yuan Zhou, Adaptive Multiple-Arm Identification, Proceedings of the 34th International Conference on Machine Learning, PMLR(2017)
  • [40] Konstantin Makarychev, Yury Makarychev, and Yuan Zhou, Satisfiability of Ordering CSPs Above Average Is Fixed-Parameter Tractable, Proceedings of the IEEE 56th Annual Symposium on Foundations of Computer Science (FOCS)(2015)
  • [41] Xi Chen, Jiawei Zhang, Yuan Zhou, Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders, Operations Research, 63(2015), 5, 1159-1176
  • [42] Ryan O’Donnell, Li-Yang Tan, and Yuan Zhou, Hypercontractive inequalities via SOS, with an application to Vertex-Cover, Manuel Kauers, Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)(2014)
  • [43] Venkatesan Guruswami, Ali Kemal Sinop, and Yuan Zhou, Constant Factor Lasserre Gaps for Graph Partitioning Problems, SIAM Journal on Optimization, 24(2014), 4, 1698-1717
  • [44] Yuan Zhou, Xi Chen, and Jian Li, Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing, Proceedings of the 31st International Conference on Machine Learning(2014)
  • [45] Ryan O’Donnell, John Wright, Chenggang Wu, and Yuan Zhou, Hardness of Robust Graph Isomorphism, Lasserre Gaps, and Asymmetry of Random Graphs, Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)(2014)
  • [46] Ryan O’Donnell, and Yuan Zhou, Approximability and proof complexity, Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)(2013)
  • [47] Aditya Bhaskara, Moses Charikar, Venkatesan Guruswami, Aravindan Vijayaraghavan, and Yuan Zhou, Polynomial integrality gaps for strong SDP relaxations of Densest k-Subgraph, Proceedings of the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)(2012)
  • [48] Julia Chuzhoy, Yury Makarychev, Aravindan Vijayaraghavan, and Yuan Zhou, Approximation Algorithms and Hardness of the k-Route Cut Problem, Proceedings of the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)(2012)
  • [49] Boaz Barak, Fernando Brandao, Aram Harrow, Jonathan Kelner, David Steurer, and Yuan Zhou, Hypercontractivity, Sum-of-Squares Proofs, and their Applications, Proceedings of the 44th Annual ACM Symposium on Theory of computing (STOC)(2012)
  • [50] Venkatesan Guruswami, and Yuan Zhou, Tight Inapproximability Bounds for Almost-satisfiable Horn SAT and Exact Hitting Set, Proceedings of the 22nd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)(2011)

 

Update Time: 2025-06-10 23:13:56