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
Transfer Learning with Partially Observable Offline Data via Causal Bounds—0
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback—0
Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits—0
Provably Optimal Algorithms for Generalized Linear Contextual Bandits—0
Pure Exploration in Asynchronous Federated Bandits—0
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler—0
Combinatorial Pure Exploration of Causal Bandits—0
Pure Exploration under Mediators' Feedback—0
QoS-Aware Multi-Armed Bandits—0
Quantile Multi-Armed Bandits with 1-bit Feedback—0
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Benchmark Results

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