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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 226–250 of 1262 papers

TitleStatusHype
Residual Loss Prediction: Reinforcement Learning With No Incremental FeedbackCode0
Semiparametric Contextual BanditsCode0
Sequential Decision Making with Expert Demonstrations under Unobserved HeterogeneityCode0
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed BanditsCode0
Simulated Contextual Bandits for Personalization Tasks from Recommendation DatasetsCode0
Approximating a Target Distribution using Weight QueriesCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
Stochastic Rising BanditsCode0
Adversarial Attacks on Combinatorial Multi-Armed BanditsCode0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
The Assistive Multi-Armed BanditCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With RenegingCode0
Thompson Sampling via Local UncertaintyCode0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Towards the D-Optimal Online Experiment Design for Recommender SelectionCode0
Networked Restless Bandits with Positive ExternalitiesCode0
Two-Stage Neural Contextual Bandits for Personalised News RecommendationCode0
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits—0
Adversarial Contextual Bandits Go Kernelized—0
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity—0
A Survey of Risk-Aware Multi-Armed Bandits—0
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Adversarial Bandits with Knapsacks—0
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Benchmark Results

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