Publications

Conference Papers:

2024:

• [NeurIPS 2024] No-Regret Learning for Fair Multi-Agent Social Welfare Optimization.
  Mengxiao Zhang, Ramiro Deo-Campo Vuong, and Haipeng Luo.

• [NeurIPS 2024] Provably Efficient Interactive-Grounded Learning with Personalized Reward.
  Mengxiao Zhang*, Yuheng Zhang*, Haipeng Luo, and Paul Mineiro.

• [NeurIPS 2024] Contextual Multinomial Logit Bandits with General Value Functions.
  Mengxiao Zhang and Haipeng Luo.

• [COLT 2024] Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics.
  Brendan Lucier*, Sarath Pattathil*, Aleksandrs Slivkins*, and Mengxiao Zhang*.

• [ICML 2024] Efficient Contextual Bandits with Uninformed Feedback Graphs.
  Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, and Paul Mineiro.

• [AISTATS 2024] Online Learning in Contextual Second-Price Pay-Per-Click Auctions.
  Mengxiao Zhang and Haipeng Luo.

2023:

• [NeurIPS 2023] Practical Contextual Bandits with Feedback Graphs.
  Mengxiao Zhang*, Yuheng Zhang*, Olga Vrousgou, Haipeng Luo, and Paul Mineiro.

• [AISTATS 2023] No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution.
  Mengxiao Zhang, Shi Chen, Haipeng Luo, and Yingfei Wang.

• [ALT 2023] Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs.
  Haipeng Luo*, Hanghang Tong*, Mengxiao Zhang*, and Yuheng Zhang*.

2022:

• [COLT 2022] Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits.
  Haipeng Luo*, Mengxiao Zhang*, Peng Zhao*, and Zhi-Hua Zhou*.

• [COLT 2022] Adaptive Bandit Convex Optimization with Heterogeneous Curvature.
  Haipeng Luo*, Mengxiao Zhang*, and Peng Zhao*.

• [ICML 2022] No-Regret Learning in Time-Varying Zero-Sum Games.
  Mengxiao Zhang*, Peng Zhao*, Haipeng Luo, and Zhi-Hua Zhou.

2021:

• [COLT 2021] Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games.
  Chen-Yu Wei, Chung-Wei Lee*, Mengxiao Zhang*, and Haipeng Luo

• [ICML 2021] Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously.
  Chung-Wei Lee*, Haipeng Luo*, Chen-Yu Wei*, Mengxiao Zhang*, and Xiaojin Zhang*

• [ICLR 2021] Linear Last-iterate Convergence in Constrained Saddle-point Optimization.
  Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, and Haipeng Luo.

2020:

• [NeurIPS 2020 Oral] Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs.
  Chung-Wei Lee*, Haipeng Luo*, Chen-Yu Wei*, and Mengxiao Zhang*.

• [COLT 2020] A Closer Look at Small-loss Bounds for Bandits with Graph Feedback.
  Chung-Wei Lee*, Haipeng Luo*, and Mengxiao Zhang*.

Workshop:

• [SIGIR eCom 2022 Spotlight Talk] Advancing Query Rewriting in E-Commerce via Shopping Intent Learning.
  Mengxiao Zhang, Yongning Wu, Raif Rustamov, Hongyu Zhu, Haoran Shi, Yuqi Wu, Lei Tang, Zuohua Zhang and Chu Wang.

Preprint:

• [SSRN] Coordination under Unknown Demand Distribution: Online Learning for Two-Echelon Supply Chains.
  Shi Chen*, Haipeng Luo*, Yingfei Wang*, and Mengxiao Zhang*.

• [arXiv] Defective Convolutional Networks.
  Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Di He, and Liwei Wang.

• [arXiv] Randomness in Deconvolutional Networks for Visual Representation.
  Kun He, Jingbo Wang, Haochuan Li, Yao Shu, Mengxiao Zhang, Man Zhu, Liwei Wang, and John E. Hopcroft.