Search Results for author: Chen-Yu Wei

Found 24 papers, 2 papers with code

Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses

no code implementations18 Jul 2021 Haipeng Luo, Chen-Yu Wei, Chung-Wei Lee

When a simulator is unavailable, we further consider a linear MDP setting and obtain $\widetilde{\mathcal{O}}({T}^{14/15})$ regret, which is the first result for linear MDPs with adversarial losses and bandit feedback.

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

no code implementations10 Feb 2021 Chen-Yu Wei, Haipeng Luo

Specifically, in most cases our algorithm achieves the optimal dynamic regret $\widetilde{\mathcal{O}}(\min\{\sqrt{LT}, \Delta^{1/3}T^{2/3}\})$ where $T$ is the number of rounds and $L$ and $\Delta$ are the number and amount of changes of the world respectively, while previous works only obtain suboptimal bounds and/or require the knowledge of $L$ and $\Delta$.

Multi-Armed Bandits

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

no code implementations8 Feb 2021 Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo

We study infinite-horizon discounted two-player zero-sum Markov games, and develop a decentralized algorithm that provably converges to the set of Nash equilibria under self-play.

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

no code implementations1 Feb 2021 Liyu Chen, Haipeng Luo, Chen-Yu Wei

We resolve the long-standing "impossible tuning" issue for the classic expert problem and show that, it is in fact possible to achieve regret $O\left(\sqrt{(\ln d)\sum_t \ell_{t, i}^2}\right)$ simultaneously for all expert $i$ in a $T$-round $d$-expert problem where $\ell_{t, i}$ is the loss for expert $i$ in round $t$.

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

no code implementations7 Dec 2020 Liyu Chen, Haipeng Luo, Chen-Yu Wei

We study the stochastic shortest path problem with adversarial costs and known transition, and show that the minimax regret is $\widetilde{O}(\sqrt{DT^\star K})$ and $\widetilde{O}(\sqrt{DT^\star SA K})$ for the full-information setting and the bandit feedback setting respectively, where $D$ is the diameter, $T^\star$ is the expected hitting time of the optimal policy, $S$ is the number of states, $A$ is the number of actions, and $K$ is the number of episodes.

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

no code implementations23 Jul 2020 Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain

We develop several new algorithms for learning Markov Decision Processes in an infinite-horizon average-reward setting with linear function approximation.

Linear Last-iterate Convergence in Constrained Saddle-point Optimization

1 code implementation ICLR 2021 Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo

Specifically, for OMWU in bilinear games over the simplex, we show that when the equilibrium is unique, linear last-iterate convergence is achieved with a learning rate whose value is set to a universal constant, improving the result of (Daskalakis & Panageas, 2019b) under the same assumption.

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

no code implementations NeurIPS 2020 Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang

We develop a new approach to obtaining high probability regret bounds for online learning with bandit feedback against an adaptive adversary.

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

no code implementations8 Jun 2020 Mehdi Jafarnia-Jahromi, Chen-Yu Wei, Rahul Jain, Haipeng Luo

Recently, model-free reinforcement learning has attracted research attention due to its simplicity, memory and computation efficiency, and the flexibility to combine with function approximation.


Federated Residual Learning

no code implementations28 Mar 2020 Alekh Agarwal, John Langford, Chen-Yu Wei

We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model.

Federated Learning

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

no code implementations7 Mar 2020 Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, David Kempe

We revisit the problem of online learning with sleeping experts/bandits: in each time step, only a subset of the actions are available for the algorithm to choose from (and learn about).

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

no code implementations4 Mar 2020 Chen-Yu Wei, Haipeng Luo, Alekh Agarwal

We initiate the study of learning in contextual bandits with the help of loss predictors.

Multi-Armed Bandits

Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator

no code implementations2 Oct 2019 James A. Preiss, Sébastien M. R. Arnold, Chen-Yu Wei, Marius Kloft

We study the variance of the REINFORCE policy gradient estimator in environments with continuous state and action spaces, linear dynamics, quadratic cost, and Gaussian noise.

Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case

no code implementations6 Feb 2019 Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang

Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$.

Classification General Classification

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

no code implementations3 Feb 2019 Yifang Chen, Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei

We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret.

Multi-Armed Bandits

Improved Path-length Regret Bounds for Bandits

no code implementations29 Jan 2019 Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei

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.

Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously

no code implementations25 Jan 2019 Julian Zimmert, Haipeng Luo, Chen-Yu Wei

We develop the first general semi-bandit algorithm that simultaneously achieves $\mathcal{O}(\log T)$ regret for stochastic environments and $\mathcal{O}(\sqrt{T})$ regret for adversarial environments without knowledge of the regime or the number of rounds $T$.

Efficient Online Portfolio with Logarithmic Regret

no code implementations NeurIPS 2018 Haipeng Luo, Chen-Yu Wei, Kai Zheng

We study the decades-old problem of online portfolio management and propose the first algorithm with logarithmic regret that is not based on Cover's Universal Portfolio algorithm and admits much faster implementation.

More Adaptive Algorithms for Adversarial Bandits

no code implementations10 Jan 2018 Chen-Yu Wei, Haipeng Luo

We develop a novel and generic algorithm for the adversarial multi-armed bandit problem (or more generally the combinatorial semi-bandit problem).

Tracking the Best Expert in Non-stationary Stochastic Environments

no code implementations NeurIPS 2016 Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu

We study the dynamic regret of multi-armed bandit and experts problem in non-stationary stochastic environments.

Online Reinforcement Learning in Stochastic Games

no code implementations NeurIPS 2017 Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu

We study online reinforcement learning in average-reward stochastic games (SGs).

Efficient Contextual Bandits in Non-stationary Worlds

no code implementations5 Aug 2017 Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford

In this work, we develop several efficient contextual bandit algorithms for non-stationary environments by equipping existing methods for i. i. d.

Multi-Armed Bandits

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