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 881–890 of 1262 papers

TitleStatusHype
Gaussian Gated Linear NetworksCode0
Distributionally Robust Batch Contextual Bandits—0
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition—0
Meta-Learning Bandit Policies by Gradient Ascent—0
Online Learning in Iterated Prisoner's Dilemma to Mimic Human BehaviorCode0
Contextual Bandits with Side-Observations—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Locally Differentially Private (Contextual) Bandits LearningCode0
(Locally) Differentially Private Combinatorial Semi-Bandits—0
To update or not to update? Delayed Nonparametric Bandits with Randomized Allocation—0
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Benchmark Results

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