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 591–600 of 1262 papers

TitleStatusHype
Instance-optimal PAC Algorithms for Contextual Bandits—0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
From Bandits to Experts: On the Value of Side-Observations—0
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Inference for Batched Bandits—0
Contextual Causal Bayesian Optimisation—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
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Benchmark Results

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