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 921–930 of 1262 papers

TitleStatusHype
Residual Bootstrap Exploration for Bandit Algorithms—0
On conditional versus marginal bias in multi-armed bandits—0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Coordination without communication: optimal regret in two players multi-armed bandits—0
Tight Lower Bounds for Combinatorial Multi-Armed Bandits—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles—0
Adversarial Attacks on Linear Contextual Bandits—0
Inference for Batched Bandits—0
Selfish Robustness and Equilibria in Multi-Player Bandits—0
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Benchmark Results

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