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Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 351–375 of 1262 papers

TitleStatusHype
Best Arm Identification in Restless Markov Multi-Armed Bandits—0
Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits—0
Best arm identification in multi-armed bandits with delayed feedback—0
Best Arm Identification in Linked Bandits—0
Best-Arm Identification in Correlated Multi-Armed Bandits—0
An Efficient Algorithm for Deep Stochastic Contextual Bandits—0
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme—0
Diffusion Approximations for Thompson Sampling—0
Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives—0
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits—0
Differentially Private Kernelized Contextual Bandits—0
Discrete Choice Multi-Armed Bandits—0
Disentangling Exploration from Exploitation—0
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication—0
Be Greedy in Multi-Armed Bandits—0
Distributed Differential Privacy in Multi-Armed Bandits—0
Distributed Exploration in Multi-Armed Bandits—0
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards—0
Meta-Learning Bandit Policies by Gradient Ascent—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified