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 1101–1110 of 1262 papers

TitleStatusHype
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits—0
Delegating via Quitting Games—0
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content—0
Best arm identification in multi-armed bandits with delayed feedback—0
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits—0
Semiparametric Contextual BanditsCode0
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback—0
Online learning over a finite action set with limited switching—0
Practical Contextual Bandits with Regression Oracles—0
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates—0
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Benchmark Results

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