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 551–560 of 1262 papers

TitleStatusHype
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms—0
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions—0
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms—0
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs—0
Convex Hull Monte-Carlo Tree Search—0
Bandits Warm-up Cold Recommender Systems—0
Algorithms for multi-armed bandit problems—0
Continuous-Time Multi-Armed Bandits with Controlled Restarts—0
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Benchmark Results

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