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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 91–100 of 1262 papers

TitleStatusHype
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Semi-Parametric Batched Global Multi-Armed Bandits with Covariates—0
Functional multi-armed bandit and the best function identification problems—0
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models—0
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal TransportabilityCode0
Heterogeneous Multi-Agent Bandits with Parsimonious Hints—0
Multi-agent Multi-armed Bandits with Minimum Reward Guarantee FairnessCode0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Continuous K-Max Bandits—0
Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits—0
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Benchmark Results

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