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 251–275 of 1262 papers

TitleStatusHype
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits—0
Incentivized Exploration via Filtered Posterior Sampling—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
FLASH: Federated Learning Across Simultaneous Heterogeneities—0
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits—0
Stochastic contextual bandits with graph feedback: from independence number to MAS number—0
Efficient Contextual Bandits with Uninformed Feedback Graphs—0
Contextual Multinomial Logit Bandits with General Value Functions—0
Replicability is Asymptotically Free in Multi-armed Bandits—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Tree Ensembles for Contextual Bandits—0
Fairness of Exposure in Online Restless Multi-armed BanditsCode0
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic BanditsCode0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Fairness and Privacy Guarantees in Federated Contextual Bandits—0
Off-Policy Evaluation of Slate Bandit Policies via Optimizing AbstractionCode0
Multi-Armed Bandits with Interference—0
Query-Efficient Correlation Clustering with Noisy Oracle—0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints—0
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual BanditsCode0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
Show:102550
← PrevPage 11 of 51Next →

Benchmark Results

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