b'Haipeng Luo'

Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously

In this work, we develop linear bandit algorithms that automatically ada...
02/11/2021 ∙ by Chung-Wei Lee, et al. ∙ 0 ∙ share

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Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach

We propose a black-box reduction that turns a certain reinforcement lear...
02/10/2021 ∙ by Chen-Yu Wei, et al. ∙ 0 ∙ share

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Finding the Stochastic Shortest Path with Low Regret: The Adversarial Cost and Unknown Transition Case

We make significant progress toward the stochastic shortest path problem...
02/10/2021 ∙ by Liyu Chen, et al. ∙ 0 ∙ share

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Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games

We study infinite-horizon discounted two-player zero-sum Markov games, a...
02/08/2021 ∙ by Chen-Yu Wei, et al. ∙ 0 ∙ share

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Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications

We resolve the long-standing "impossible tuning" issue for the classic e...
02/01/2021 ∙ by Liyu Chen, et al. ∙ 0 ∙ share

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Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition

We study the stochastic shortest path problem with adversarial costs and...
12/07/2020 ∙ by Liyu Chen, et al. ∙ 0 ∙ share

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Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation

We develop several new algorithms for learning Markov Decision Processes...
07/23/2020 ∙ by Chen-Yu Wei, et al. ∙ 12 ∙ share

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Comparator-adaptive Convex Bandits

We study bandit convex optimization methods that adapt to the norm of th...
07/16/2020 ∙ by Dirk van der Hoeven, et al. ∙ 8 ∙ share

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Active Online Domain Adaptation

Online machine learning systems need to adapt to domain shifts. Meanwhil...
06/25/2020 ∙ by Yining Chen, et al. ∙ 9 ∙ share

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Open Problem: Model Selection for Contextual Bandits

In statistical learning, algorithms for model selection allow the learne...
06/19/2020 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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Linear Last-iterate Convergence for Matrix Games and Stochastic Games

Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point opt...
06/16/2020 ∙ by Chung-Wei Lee, et al. ∙ 0 ∙ share

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Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs

We develop a new approach to obtaining high probability regret bounds fo...
06/14/2020 ∙ by Chung-Wei Lee, et al. ∙ 0 ∙ share

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Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition

This work studies the problem of learning episodic Markov Decision Proce...
06/10/2020 ∙ by Tiancheng Jin, et al. ∙ 0 ∙ share

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A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret

Recently, model-free reinforcement learning has attracted research atten...
06/08/2020 ∙ by Mehdi Jafarnia-Jahromi, et al. ∙ 12 ∙ share

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Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds

We revisit the problem of online learning with sleeping experts/bandits:...
03/07/2020 ∙ by Ehsan Emamjomeh-Zadeh, et al. ∙ 0 ∙ share

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Taking a hint: How to leverage loss predictors in contextual bandits?

We initiate the study of learning in contextual bandits with the help of...
03/04/2020 ∙ by Chen-Yu Wei, et al. ∙ 0 ∙ share

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A Closer Look at Small-loss Bounds for Bandits with Graph Feedback

We study small-loss bounds for the adversarial multi-armed bandits probl...
02/02/2020 ∙ by Chung-Wei Lee, et al. ∙ 0 ∙ share

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Fair Contextual Multi-Armed Bandits: Theory and Experiments

When an AI system interacts with multiple users, it frequently needs to ...
12/13/2019 ∙ by Yifang Chen, et al. ∙ 0 ∙ share

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Learning Adversarial MDPs with Bandit Feedback and Unknown Transition

We consider the problem of learning in episodic finite-horizon Markov de...
12/03/2019 ∙ by Tiancheng Jin, et al. ∙ 0 ∙ share

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Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes

Model-free reinforcement learning is known to be memory and computation ...
10/15/2019 ∙ by Chen-Yu Wei, et al. ∙ 0 ∙ share

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Model selection for contextual bandits

We introduce the problem of model selection for contextual bandits, wher...
06/03/2019 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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Equipping Experts/Bandits with Long-term Memory

We propose the first reduction-based approach to obtaining long-term mem...
05/30/2019 ∙ by Kai Zheng, et al. ∙ 0 ∙ share

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Hypothesis Set Stability and Generalization

We present an extensive study of generalization for data-dependent hypot...
04/09/2019 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free

We propose the first contextual bandit algorithm that is parameter-free,...
02/03/2019 ∙ by Yifang Chen, et al. ∙ 0 ∙ share

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Improved Path-length Regret Bounds for Bandits

We study adaptive regret bounds in terms of the variation of the losses ...
01/29/2019 ∙ by Sébastien Bubeck, et al. ∙ 8 ∙ share

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Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously

We develop the first general semi-bandit algorithm that simultaneously a...
01/25/2019 ∙ by Julian Zimmert, et al. ∙ 0 ∙ share

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Efficient Online Portfolio with Logarithmic Regret

We study the decades-old problem of online portfolio management and prop...
05/18/2018 ∙ by Haipeng Luo, et al. ∙ 0 ∙ share

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Logistic Regression: The Importance of Being Improper

Learning linear predictors with the logistic loss---both in stochastic a...
03/25/2018 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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Practical Contextual Bandits with Regression Oracles

A major challenge in contextual bandits is to design general-purpose alg...
03/03/2018 ∙ by Dylan J. Foster, et al. ∙ 0 ∙ share

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More Adaptive Algorithms for Adversarial Bandits

We develop a novel and generic algorithm for the adversarial multi-armed...
01/10/2018 ∙ by Chen-Yu Wei, et al. ∙ 0 ∙ share

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Efficient Contextual Bandits in Non-stationary Worlds

Most contextual bandit algorithms minimize regret to the best fixed poli...
08/05/2017 ∙ by Haipeng Luo, et al. ∙ 0 ∙ share

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Corralling a Band of Bandit Algorithms

We study the problem of combining multiple bandit algorithms (that is, o...
12/19/2016 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Haipeng Luo

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