Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. - Boxiang Lyu; Zhaoran Wang 001; Mladen Kolar; Zhuoran Yang (ICML, 2022)

Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach. - Boxiang Lyu; Qinglin Meng; Shuang Qiu; Zhaoran Wang 001; Zhuoran Yang; Michael I. Jordan (CoRR, 2022)

Personalized Federated Learning with Multiple Known Clusters. - Boxiang Lyu; Filip Hanzely; Mladen Kolar (CoRR, 2022)

One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning. - Pedro Cisneros-Velarde; Boxiang Lyu; Sanmi Koyejo; Mladen Kolar (CoRR, 2022)

A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. - Rui Ai 002; Boxiang Lyu; Zhaoran Wang 001; Zhuoran Yang; Michael I. Jordan (CoRR, 2022)

One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning. - Pedro Cisneros-Velarde; Boxiang Lyu; Sanmi Koyejo; Mladen Kolar (AISTATS, 2023)

Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions. - Boxiang Lyu; Zhe Feng; Zachary Robertson; Sanmi Koyejo (ICML, 2023)

Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. - Boxin Zhao; Boxiang Lyu; Raul Castro Fernandez; Mladen Kolar (ICML, 2023)

L-SVRG and L-Katyusha with Adaptive Sampling. - Boxin Zhao; Boxiang Lyu; Mladen Kolar (Trans. Mach. Learn. Res., 2023)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. - Dake Zhang; Boxiang Lyu; Shuang Qiu; Mladen Kolar; Tong Zhang 001 (ICML, 2024)

Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning. - Shuang Qiu; Dake Zhang; Rui Yang 010; Boxiang Lyu; Tong Zhang 001 (CoRR, 2024)

An Instrumental Value for Data Production and its Application to Data Pricing. - Rui Ai 002; Boxiang Lyu; Zhaoran Wang 001; Zhuoran Yang; Haifeng Xu (CoRR, 2024)