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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 391–400 of 1262 papers

TitleStatusHype
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization—0
Thompson sampling for improved exploration in GFlowNets—0
Kernel ε-Greedy for Multi-Armed Bandits with Covariates—0
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler—0
You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits—0
Multi-Fidelity Multi-Armed Bandits Revisited—0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity—0
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Benchmark Results

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