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 421–430 of 1262 papers

TitleStatusHype
Efficient Contextual Bandits with Uninformed Feedback Graphs—0
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits—0
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Efficient Generalized Low-Rank Tensor Contextual Bandits—0
Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
Adapting to Misspecification in Contextual Bandits—0
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback—0
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Benchmark Results

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