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 626–650 of 1262 papers

TitleStatusHype
Bandit Learning with Delayed Impact of Actions—0
Fair Contextual Multi-Armed Bandits: Theory and Experiments—0
Fair Exploration via Axiomatic Bargaining—0
Fairness and Privacy Guarantees in Federated Contextual Bandits—0
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits—0
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Fairness in Learning: Classic and Contextual Bandits—0
Fairness of Exposure in Stochastic Bandits—0
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Faster Maximum Inner Product Search in High Dimensions—0
Faster Q-Learning Algorithms for Restless Bandits—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Federated Linear Bandits with Finite Adversarial Actions—0
Federated Linear Contextual Bandits—0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
Federated Linear Contextual Bandits with User-level Differential Privacy—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Federated Online Sparse Decision Making—0
Federated Learning for Heterogeneous Bandits with Unobserved Contexts—0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health—0
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Benchmark Results

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