Provable Benefits of Representation Learning in Linear Bandits. - Jiaqi Yang 001; Wei Hu 014; Jason D. Lee; Simon S. Du (CoRR, 2020)

Fully Gap-Dependent Bounds for Multinomial Logit Bandit. - Jiaqi Yang 001 (AISTATS, 2021)

Impact of Representation Learning in Linear Bandits. - Jiaqi Yang 001; Wei Hu 014; Jason D. Lee; Simon Shaolei Du (ICLR, 2021)

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature. - Kefan Dong; Jiaqi Yang 001; Tengyu Ma 001 (NeurIPS, 2021)

Going Beyond Linear RL: Sample Efficient Neural Function Approximation. - Baihe Huang; Kaixuan Huang; Sham M. Kakade; Jason D. Lee; Qi Lei; Runzhe Wang; Jiaqi Yang 001 (NeurIPS, 2021)

Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. - Baihe Huang; Kaixuan Huang; Sham M. Kakade; Jason D. Lee; Qi Lei; Runzhe Wang; Jiaqi Yang 001 (NeurIPS, 2021)

Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP. - Zihan Zhang; Jiaqi Yang 001; Xiangyang Ji; Simon S. Du (NeurIPS, 2021)

Linear bandits with limited adaptivity and learning distributional optimal design. - Yufei Ruan; Jiaqi Yang 001; Yuan Zhou 007 (STOC, 2021)

Variance-Aware Confidence Set: Variance-Dependent Bound for Linear Bandits and Horizon-Free Bound for Linear Mixture MDP. - Zihan Zhang; Jiaqi Yang 001; Xiangyang Ji; Simon S. Du (CoRR, 2021)

Nearly Minimax Algorithms for Linear Bandits with Shared Representation. - Jiaqi Yang 001; Qi Lei; Jason D. Lee; Simon S. Du (CoRR, 2022)