Research

Publications

(* denotes equal contribution)

1. Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning.
   Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang.
   International Conference on Machine Learning (ICML) 2024 (spotlight, top 15% of all accepted submissions).
2. Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach.
   Shuang Qiu*, Boxiang Lyu*, Qinglin Meng*, Zhuoran Yang, Zhaoran Wang, Michael Jordan.
   Accepted with Minor Revision at Journal of Machine Learning Research (JMLR). [arXiv]
3. Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
   Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo.
   International Conference on Machine Learning (ICML) 2023. [arXiv]
4. Addressing Budget Allocation and Revenue Allocation in Data Market Environment Using an Adaptive Sampling Algorithm
   Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen Kolar.
   International Conference on Machine Learning (ICML) 2023. [arXiv]
5. L-SVRG and L-Katyusha with Adaptive Sampling.
   Boxin Zhao, Boxiang Lyu, Mladen Kolar.
   Transactions on Machine Learning Research (TMLR) [arXiv]
6. 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.
   International Conference on Artificial Intelligence and Statistics (AISTATS) 2023. [arXiv]
7. Pessimism Meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning.
   Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang.
   International Conference on Machine Learning (ICML) 2022. [arXiv]

Preprints

1. A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design.
   Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan.
   Technical Report. [arXiv]
2. Personalized Federated Learning with Multiple Known Clusters.
   Boxiang Lyu, Filip Hanzely, Mladen Kolar.
   Technical Report. [arXiv]