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 931–940 of 1262 papers

TitleStatusHype
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
Safe Exploration for Optimizing Contextual BanditsCode0
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits—0
Bandits with Knapsacks beyond the Worst-Case—0
Ballooning Multi-Armed Bandits—0
Incentivising Exploration and Recommendations for Contextual Bandits with Payments—0
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits—0
Gradient-free Online Learning in Continuous Games with Delayed Rewards—0
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits—0
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Benchmark Results

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