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 571–580 of 1262 papers

TitleStatusHype
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Contextual memory bandit for pro-active dialog engagement—0
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Contextual Linear Bandits with Delay as Payoff—0
Contextual Information-Directed Sampling—0
Bandit Regret Scaling with the Effective Loss Range—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
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Benchmark Results

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