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 581–590 of 1262 papers

TitleStatusHype
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions—0
A Survey of Risk-Aware Multi-Armed Bandits—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Selectively Contextual Bandits—0
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications—0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
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Benchmark Results

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