Conference Papers:


  • [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.


  • [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*.


  • [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.


  • [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.


  • [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*.


  • [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.


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

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

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

  • [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.