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

Toward Solving 2-TBSG Efficiently. - Zeyu Jia; Zaiwen Wen; Yinyu Ye 001 (CoRR, 2019)

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

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

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

Towards solving 2-TBSG efficiently. - Zeyu Jia; Zaiwen Wen; Yinyu Ye 001 (Optim. Methods Softw., 2020)

Identification of Key Gene Modules and Hub Genes of Hypertension Based on WGCNA Algorithm. - Zongjin Li; Changxin Song; Zeyu Jia; Dong Chen; Yan Liang (BIBE, 2021)

Intrinsic Dimension Estimation. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (CoRR, 2021)

The application of machine learning algorithms in predicting the length of stay following femoral neck fracture. - Hao Zhong 003; Bingpu Wang; Dawei Wang; Zirui Liu 001; Cong Xing; Yu Wu; Qiang Gao; Shibo Zhu; Haodong Qu; Zeyu Jia; Zhigang Qu; Guangzhi Ning; et al. (Int. J. Medical Informatics, 2021)

Search Direction Correction with Normalized Gradient Makes First-Order Methods Faster. - Yifei Wang 005; Zeyu Jia; Zaiwen Wen (SIAM J. Sci. Comput., 2021)

Rate of convergence of the smoothed empirical Wasserstein distance. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (CoRR, 2022)

Intrinsic Dimension Estimation Using Wasserstein Distance. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (J. Mach. Learn. Res., 2022)

Bandwidth Optimization of MEMS Accelerometers in Fluid Medium Environment. - Xiang Xu; Shuang Wu; Weidong Fang; Zhe Yu; Zeyu Jia; Xiaoxu Wang; Jian Bai; Qianbo Lu (Sensors, 2022)

Linear Reinforcement Learning with Ball Structure Action Space. - Zeyu Jia; Randy Jia; Dhruv Madeka; Dean P. Foster (ALT, 2023)

Entropic characterization of optimal rates for learning Gaussian mixtures. - Zeyu Jia; Yury Polyanskiy; Yihong Wu 001 (COLT, 2023)

When is Agnostic Reinforcement Learning Statistically Tractable? - Zeyu Jia; Gene Li; Alexander Rakhlin; Ayush Sekhari; Nati Srebro (NeurIPS, 2023)

Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. - Zeyu Jia; Alexander Rakhlin; Ayush Sekhari; Chen-Yu Wei (COLT, 2024)

How Does Variance Shape the Regret in Contextual Bandits? - Zeyu Jia; Jian Qian; Alexander Rakhlin; Chen-Yu Wei (NeurIPS, 2024)

On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy. - Zeyu Jia; Alexander Rakhlin; Yury Polyanskiy (COLT, 2025)

Ensuring resilience in active distribution networks: A security-constrained robust approach with energy storage and demand response. - Shulin Liu; Zeyu Jia; Tingting Liu (J. Comput. Methods Sci. Eng., 2025)

Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective. - Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning. - Yurun Yuan; Fan Chen; Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits. - Fan Chen; Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Gaussian Sequence Model: Sample Complexities of Testing, Estimation and LFHT. - Zeyu Jia; Yury Polyanskiy (CoRR, 2025)