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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 76–100 of 1262 papers

TitleStatusHype
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
Adaptive Endpointing with Deep Contextual Multi-armed Bandits—0
A Correction of Pseudo Log-Likelihood Method—0
Almost Boltzmann Exploration—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits—0
An Adaptive Method for Contextual Stochastic Multi-armed Bandits with Rewards Generated by a Linear Dynamical System—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
An Analysis of Reinforcement Learning for Malaria Control—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits—0
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives—0
An Efficient Algorithm for Deep Stochastic Contextual Bandits—0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
An Empirical Evaluation of Thompson Sampling—0
Adaptively Learning to Select-Rank in Online Platforms—0
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free—0
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
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Benchmark Results

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