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 101–110 of 1262 papers

TitleStatusHype
Efficient Kernel UCB for Contextual BanditsCode0
Empirical Likelihood for Contextual BanditsCode0
Causally Abstracted Multi-armed BanditsCode0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox OptimizationCode0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Federated Multi-armed Bandits with PersonalizationCode0
Federated Neural BanditsCode0
Addressing the Long-term Impact of ML Decisions via Policy RegretCode0
Batched Multi-armed Bandits ProblemCode0
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Benchmark Results

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