• Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo. Model Selection for Contextual Bandits. [arXiv]

  • Kai Zheng, Haipeng Luo, Ilias Diakonikolas, and Liwei Wang. Equipping Experts/Bandits with Long-term Memory. [arXiv]

  • Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri and Karthik Sridharan. Hypothesis Set Stability and Generalization. [arXiv]

Conference Papers

  • Sébastien Bubeck, Yuanzhi Li, Haipeng Luo and Chen-Yu Wei. Improved Path-length Regret Bounds for Bandits. COLT 2019. [arXiv]

  • Yifang Chen, Chung-Wei Lee, Haipeng Luo and Chen-Yu Wei. A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free. COLT 2019. [arXiv]

  • Julian Zimmert, Haipeng Luo and Chen-Yu Wei. Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. ICML 2019, long talk. [arXiv]

  • Haipeng Luo, Chen-Yu Wei and Kai Zheng. Efficient Online Portfolio with Logarithmic Regret. NeurIPS 2018, spotlight. [arXiv]

  • Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri and Karthik Sridharan. Logistic Regression: The Importance of Being Improper. COLT 2018, Best Student Paper Award. [pdf]

  • Chen-Yu Wei and Haipeng Luo. More Adaptive Algorithms for Adversarial Bandits. COLT 2018. [pdf]

  • Haipeng Luo, Chen-Yu Wei, Alekh Agarwal and John Langford. Efficient Contextual Bandits in Non-stationary Worlds. COLT 2018. [pdf]

  • Dylan J. Foster, Alekh Agarwal, Miroslav Dudik, Haipeng Luo and Robert E. Schapire. Practical Contextual Bandits with Regression Oracles. ICML 2018. [arXiv]

  • Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis and Jennifer Wortman Vaughan. Oracle-Efficient Online Learning and Auction Design. FOCS 2017. [arXiv]

  • Alekh Agarwal, Haipeng Luo, Behnam Neyshabur and Robert E. Schapire. Corralling a Band of Bandit Algorithms. COTL 2017. [pdf]

  • Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy and Robert E. Schapire. Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. NeurIPS 2016. [arXiv]

  • Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi and John Langford. Efficient Second Order Online Learning via Sketching. NeurIPS 2016. [arXiv]

  • Elad Hazan and Haipeng Luo. Variance-Reduced and Projection-Free Stochastic Optimization. ICML 2016. [arXiv]

  • Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo and Robert E. Schapire. Fast Convergence of Regularized Learning in Games. NeurIPS 2015, Best Paper Award. [arXiv]

  • Alina Beygelzimer, Elad Hazan, Satyen Kale and Haipeng Luo. Online Gradient Boosting. NeurIPS 2015. [arXiv]

  • Alina Beygelzimer, Satyen Kale and Haipeng Luo. Optimal and Adaptive Algorithms for Online Boosting. ICML 2015, Best Paper Award. [arXiv] [short version at IJCAI 2016, sister conference best paper track]

  • Haipeng Luo and Robert E. Schapire. Achieving All with No Parameters: AdaNormalHedge. COLT 2015. [pdf]

  • Haipeng Luo and Robert E. Schapire. A Drifting-Games Analysis for Online Learning and Applications to Boosting. NeurIPS 2014. [arXiv]

  • Haipeng Luo, Patrick Haffner and Jean-Francois Paiement. Accelerated Parallel Optimization Methods for Large Scale Machine Learning. OPT workshop at NeurIPS 2014. [arXiv]

  • Haipeng Luo and Robert E. Schapire. Towards Minimax Online Learning with Unknown Time Horizon. ICML 2014. [arXiv]

Open Problems

  • Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo and Robert E. Schapire. Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT 2017. [pdf], [A solution by Allen-Zhu, Bubeck and Li]

PhD Thesis

  • Optimal and Adaptive Online Learning. [pdf]


  • Weijia Song, Zhen Xiao, Qi Chen and Haipeng Luo. Adaptive Resource Provisioning for the Cloud Using Online Bin Packing. IEEE Transactions on Computers, 63:2647-2660, 2013. [pdf]

  • Zhen Xiao, Qi Chen and Haipeng Luo. Automatic Scaling of Internet Applications for Cloud Computing Services. IEEE Transactions on Computers, 63:1111-1123, 2012. [pdf]