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 1031–1040 of 1262 papers

TitleStatusHype
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
A Contextual Combinatorial Bandit Approach to Negotiation—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
A Correction of Pseudo Log-Likelihood Method—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Active Search for Sparse Signals with Region Sensing—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
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Benchmark Results

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