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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 651–675 of 1262 papers

TitleStatusHype
Offline Contextual Bandits for Wireless Network Optimization—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit—0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
(Almost) Free Incentivized Exploration from Decentralized Learning AgentsCode0
Federated Linear Contextual Bandits—0
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and GeneralizationCode0
The Pareto Frontier of model selection for general Contextual Bandits—0
Linear Contextual Bandits with Adversarial Corruptions—0
Towards the D-Optimal Online Experiment Design for Recommender SelectionCode0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Dynamic pricing and assortment under a contextual MNL demand—0
Stateful Offline Contextual Policy Evaluation and Learning—0
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits—0
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Query-Reward Tradeoffs in Multi-Armed Bandits—0
Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Batched Thompson Sampling—0
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Benchmark Results

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