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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 1262 papers

TitleStatusHype
Top-k Combinatorial Bandits with Full-Bandit Feedback—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Adversarial Attacks on Adversarial Bandits—0
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits—0
Collaborative Min-Max Regret in Grouped Multi-Armed Bandits—0
Approximately Stationary Bandits with Knapsacks—0
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits—0
Parallel Best Arm Identification in Heterogeneous Environments—0
Approximate Function Evaluation via Multi-Armed Bandits—0
Bandits with Knapsacks beyond the Worst-Case—0
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents—0
Clustered Linear Contextual Bandits with Knapsacks—0
A One-Size-Fits-All Solution to Conservative Bandit Problems—0
Classical Bandit Algorithms for Entanglement Detection in Parameterized Qubit States—0
Censored Semi-Bandits for Resource Allocation—0
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Achieving User-Side Fairness in Contextual Bandits—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System—0
Causal Contextual Bandits with Targeted Interventions—0
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON—0
Causal Bandits: Online Decision-Making in Endogenous Settings—0
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Benchmark Results

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