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 101–110 of 1262 papers

TitleStatusHype
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
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Lagrangian Index Policy for Restless Bandits with Average Reward—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child HealthCode0
UCB algorithms for multi-armed bandits: Precise regret and adaptive inference—0
Conservative Contextual Bandits: Beyond Linear Representations—0
Data Acquisition for Improving Model Fairness using Reinforcement Learning—0
Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi—0
Show:102550
← PrevPage 11 of 127Next →

Benchmark Results

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