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 841–850 of 1262 papers

TitleStatusHype
Lenient Regret for Multi-Armed Bandits—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
Greedy Bandits with Sampled Context—0
Multi-Armed Bandits for Minesweeper: Profiting from Exploration-Exploitation Synergy—0
Minimax Policy for Heavy-tailed Bandits—0
Competing Bandits: The Perils of Exploration Under Competition—0
Self-Tuning Bandits over Unknown Covariate-Shifts—0
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits—0
Optimal Learning for Structured BanditsCode0
Quantum exploration algorithms for multi-armed banditsCode0
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Benchmark Results

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