106 citations · 996 across the 72 of their papers we have counts for
9 papers · 1 filter
Fair Contextual Multi-Armed Bandits: Theory and Experiments
Yifang Chen, Alex Cuellar, Haipeng Luo +3
When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting…
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
Chi Jin, Tiancheng Jin, Haipeng Luo +2
We consider the problem of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses. We propose an…
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo +2
Model-free reinforcement learning is known to be memory and computation efficient and more amendable to large scale problems. In this paper, two model-free algorithms are introduce…
Model selection for contextual bandits
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
We introduce the problem of model selection for contextual bandits, where a learner must adapt to the complexity of the optimal policy while balancing exploration and exploitation.…
Equipping Experts/Bandits with Long-term Memory
Kai Zheng, Haipeng Luo, Ilias Diakonikolas +1
We propose the first reduction-based approach to obtaining long-term memory guarantees for online learning in the sense of Bousquet and Warmuth, 2002, by reducing the problem to ac…
Hypothesis Set Stability and Generalization
Dylan J. Foster, Spencer Greenberg, Satyen Kale +3
We present a study of generalization for data-dependent hypothesis sets. We give a general learning guarantee for data-dependent hypothesis sets based on a notion of transductive R…
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
Yifang Chen, Chung-Wei Lee, Haipeng Luo +1
We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\ma…
Improved Path-length Regret Bounds for Bandits
Sébastien Bubeck, Yuanzhi Li, Haipeng Luo +1
We study adaptive regret bounds in terms of the variation of the losses (the so-called path-length bounds) for both multi-armed bandit and more generally linear bandit. We first sh…
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Julian Zimmert, Haipeng Luo, Chen-Yu Wei
We develop the first general semi-bandit algorithm that simultaneously achieves regret for stochastic environments and regret for adve…