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 1241–1250 of 1262 papers

TitleStatusHype
Towards Distribution-Free Multi-Armed Bandits with Combinatorial Strategies—0
From Bandits to Experts: A Tale of Domination and Independence—0
On Finding the Largest Mean Among Many—0
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling—0
A Gang of Bandits—0
Dynamic Ad Allocation: Bandits with Budgets—0
Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
Risk-Aversion in Multi-armed Bandits—0
Thompson Sampling for Contextual Bandits with Linear PayoffsCode0
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Benchmark Results

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