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 251–275 of 1262 papers

TitleStatusHype
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Adversarial Bandits with Knapsacks—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content—0
Combinatorial Semi-Bandits with Knapsacks—0
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications—0
Adversarial Attacks on Linear Contextual Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Combinatorial Pure Exploration of Multi-Armed Bandits—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits—0
Adversarial Attacks on Cooperative Multi-agent Bandits—0
A Classification View on Meta Learning Bandits—0
Combinatorial Multi-Armed Bandits with Filtered Feedback—0
Combinatorial Multi-armed Bandits for Real-Time Strategy Games—0
A Reinforcement-Learning-Enhanced LLM Framework for Automated A/B Testing in Personalized Marketing—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
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Benchmark Results

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