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Publications
Preprints
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei and Mengxiao Zhang. Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs. [arXiv]
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei and Mengxiao Zhang. Linear Last-iterate Convergence for Matrix Games and Stochastic Games. [arXiv]
Tiancheng Jin and Haipeng Luo. Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition. [arXiv]
Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo and Rahul Jain. Learning Infinite-horizon Average-reward MDPs
with Linear Function Approximation. [arXiv]
Yining Chen, Haipeng Luo, Tengyu Ma and Chicheng Zhang. Active Online Domain Adaptation. [arXiv]
Dirk van der Hoeven, Ashok Cutkosky and Haipeng Luo. Comparator-Adaptive Convex Bandits. [arXiv]
Mehdi Jafarnia-Jahromi, Chen-Yu Wei, Rahul Jain and Haipeng Luo. A Model-free Learning Algorithm for Infinite-horizon
Average-reward MDPs with Near-optimal Regret. [arXiv]
Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo and David Kempe. Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds. [arXiv]
Conference Papers
Chen-Yu Wei, Haipeng Luo and Alekh Agarwal. Taking a Hint: How to Leverage Loss Predictors in Contextual Bandits? COLT 2020. [arXiv]
Chung-Wei Lee, Haipeng Luo and Mengxiao Zhang. A Closer Look at Small-loss Bounds for Bandits with Graph Feedback. COLT 2020. [arXiv]
Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra and Tiancheng Yu. Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. ICML 2020. [arXiv]
Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma and Rahul Jain. Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes. ICML 2020. [arXiv]
Yifang Chen, Alex Cuellar, Haipeng Luo, Jignesh Modi, Heramb Nemlekar and Stefanos Nikolaidis. Fair Contextual Multi-Armed Bandits: Theory and Experiments. UAI 2020. [arXiv]
Dylan J. Foster, Akshay Krishnamurthy and Haipeng Luo. Model Selection for Contextual Bandits. NeurIPS 2019, spotlight. [arXiv]
Kai Zheng, Haipeng Luo, Ilias Diakonikolas and Liwei Wang. Equipping Experts/Bandits with Long-term Memory. NeurIPS 2019. [arXiv]
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri and Karthik Sridharan. Hypothesis Set Stability and Generalization. NeurIPS 2019. [arXiv]
Sébastien Bubeck, Yuanzhi Li, Haipeng Luo and Chen-Yu Wei. Improved Path-length Regret Bounds for Bandits. COLT 2019. [pdf]
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. [pdf]
Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner and Chen-Yu Wei. Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information. COLT 2019, joint extended abstract. [pdf]
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
Dylan Foster, Akshay Krishnamurthy and Haipeng Luo. Open Problem: Model Selection for Contextual Bandits. COLT 2020. [arXiv]
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
Misc.
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]
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