An Information-Theoretic Analysis for Thompson Sampling with Many Actions. - Shi Dong 003; Benjamin Van Roy (NeurIPS, 2018)

An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces. - Shi Dong 003; Benjamin Van Roy (CoRR, 2018)

On the Performance of Thompson Sampling on Logistic Bandits. - Shi Dong 003; Tengyu Ma 001; Benjamin Van Roy (COLT, 2019)

Comments on the Du-Kakade-Wang-Yang Lower Bounds. - Benjamin Van Roy; Shi Dong 003 (CoRR, 2019)

Provably Efficient Reinforcement Learning with Aggregated States. - Shi Dong 003; Benjamin Van Roy; Zhengyuan Zhou (CoRR, 2019)

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State. - Shi Dong 003; Benjamin Van Roy; Zhengyuan Zhou (CoRR, 2021)

Inclusive Artificial Intelligence. - Dilip Arumugam; Shi Dong 003; Benjamin Van Roy (CoRR, 2022)

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States. - Shi Dong 003; Benjamin Van Roy; Zhengyuan Zhou (J. Mach. Learn. Res., 2022)

Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models. - Wanqiao Xu; Shi Dong 003; Dilip Arumugam; Benjamin Van Roy (CoRR, 2023)

RLHF and IIA: Perverse Incentives. - Wanqiao Xu; Shi Dong 003; Xiuyuan Lu; Grace Lam; Zheng Wen 002; Benjamin Van Roy (CoRR, 2023)

Posterior Sampling for Continuing Environments. - Wanqiao Xu; Shi Dong 003; Benjamin Van Roy (RLJ, 2024)