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 421–430 of 1262 papers

TitleStatusHype
Improving Fairness in Adaptive Social Exergames via Shapley Bandits—0
Practical Contextual Bandits with Feedback Graphs—0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Bandit Social Learning: Exploration under Myopic Behavior—0
Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation—0
Adversarial Rewards in Universal Learning for Contextual Bandits—0
Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and SensingCode0
Leveraging User-Triggered Supervision in Contextual Bandits—0
On Private and Robust Bandits—0
Multiplier Bootstrap-based Exploration—0
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Benchmark Results

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