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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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 21–30 of 1262 papers

Title	Date	Tasks	Status	Hype	Score
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits	Dec 1, 2020	ClusteringMulti-Armed Bandits	CodeCode Available	1	5
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits	Oct 7, 2021	Multi-Armed BanditsThompson Sampling	CodeCode Available	1	5
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks	May 10, 2021	Efficient ExplorationMulti-Armed Bandits	CodeCode Available	1	5
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation	Apr 2, 2020	Multi-Armed Bandits	CodeCode Available	1	5
A Modern Introduction to Online Learning	Dec 31, 2019	AllMulti-Armed Bandits	CodeCode Available	1	5
An empirical evaluation of active inference in multi-armed bandits	Jan 21, 2021	BIG-bench Machine LearningDecision Making	CodeCode Available	1	5
Anytime-valid off-policy inference for contextual bandits	Oct 19, 2022	counterfactualMulti-Armed Bandits	CodeCode Available	1	5
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Bandits	Oct 31, 2022	Multi-Armed Bandits	CodeCode Available	1	5
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits	Oct 2, 2024	Instruction FollowingMath	CodeCode Available	1	5
Federated Multi-Armed Bandits	Jan 28, 2021	Federated LearningMulti-Armed Bandits	CodeCode Available	1	5

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	NeuralLinear FullPosterior-MR	Cumulative regret	1.92	—	Unverified
2	Linear FullPosterior-MR	Cumulative regret	1.82	—	Unverified