Publications

Preprints

• [arXiv] Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses.
  Haipeng Luo, Chen-Yu Wei, and Chung-Wei Lee.

• [arXiv] The Best of Both Worlds: Stochastic and Adversarial Episodic MDPs with Unknown Transition.
  Tiancheng Jin, Longbo Huang, and Haipeng Luo.

• [arXiv] Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path.
  Liyu Chen, Mehdi Jafarnia-Jahromi, Rahul Jain, and Haipeng Luo.

• [arXiv] Last-iterate Convergence in Extensive-Form Games.
  Chung-Wei Lee, Christian Kroer, and Haipeng Luo.

• [arXiv] Online Learning for Stochastic Shortest Path Model via Posterior Sampling.
  Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain, and Haipeng Luo.

Conference Papers

• [COLT 2021] Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications.
  Liyu Chen, Haipeng Luo, and Chen-Yu Wei.

• [COLT 2021] Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach.
  Chen-Yu Wei and Haipeng Luo.

• [COLT 2021] Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition.
  Liyu Chen, Haipeng Luo, and Chen-Yu Wei.

• [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] Finding the Stochastic Shortest Path with Low Regret: The Adversarial Cost and Unknown Transition Case.
  Liyu Chen 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 Saddled-point Optimization.
  Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, and Haipeng Luo.

• [AISTATS 2021] Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation.
  Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, and Rahul Jain.

• [AISTATS 2021] Active Online Learning with Hidden Shifting Domains.
  Yining Chen, Haipeng Luo, Tengyu Ma, and Chicheng Zhang.

• [ALT 2021] Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds.
  Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, and David Kempe.

• [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.

• [NeurIPS 2020 spotlight] Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition.
  Tiancheng Jin and Haipeng Luo.

• [NeurIPS 2020] Comparator-Adaptive Convex Bandits.
  Dirk van der Hoeven, Ashok Cutkosky, and Haipeng Luo.

• [COLT 2020] Taking a Hint: How to Leverage Loss Predictors in Contextual Bandits?
  Chen-Yu Wei, Haipeng Luo, and Alekh Agarwal.

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

• [ICML 2020] Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition.
  Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, and Tiancheng Yu.

• [ICML 2020] Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes.
  Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma, and Rahul Jain.

• [UAI 2020] Fair Contextual Multi-Armed Bandits: Theory and Experiments.
  Yifang Chen, Alex Cuellar, Haipeng Luo, Jignesh Modi, Heramb Nemlekar, and Stefanos Nikolaidis.

• [NeurIPS 2019 spotlight] Model Selection for Contextual Bandits.
  Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo.

• [NeurIPS 2019] Equipping Experts/Bandits with Long-term Memory.
  Kai Zheng, Haipeng Luo, Ilias Diakonikolas, and Liwei Wang.

• [NeurIPS 2019] Hypothesis Set Stability and Generalization.
  Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, and Karthik Sridharan.

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

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

• [COLT 2019 joint extended abstract] Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information.
  Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner, and Chen-Yu Wei.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Open Problems

• [COLT 2020] Open Problem: Model Selection for Contextual Bandits.
  Dylan Foster, Akshay Krishnamurthy, and Haipeng Luo.

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

PhD Thesis

• Optimal and Adaptive Online Learning.

Misc.

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

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