SOTAVerified

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 191–200 of 1262 papers

TitleStatusHype
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Multi-agent Multi-armed Bandits with Minimum Reward Guarantee FairnessCode0
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardsCode0
Multi-Armed Bandits in Brain-Computer InterfacesCode0
Bandit-Based Monte Carlo Optimization for Nearest NeighborsCode0
Multi-Armed Bandits with Network InterferenceCode0
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed BanditsCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Model selection for contextual banditsCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
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Benchmark Results

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