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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 126–150 of 1262 papers

TitleStatusHype
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy—0
Optimal Multi-Objective Best Arm Identification with Fixed Confidence—0
Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Multilinguality in LLM-Designed Reward Functions for Restless Bandits: Effects on Task Performance and Fairness—0
Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy—0
Neural Risk-sensitive Satisficing in Contextual Bandits—0
Differentially Private Kernelized Contextual Bandits—0
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation—0
On The Statistical Complexity of Offline Decision-Making—0
An Instrumental Value for Data Production and its Application to Data Pricing—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
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
Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi—0
Data Acquisition for Improving Model Fairness using Reinforcement Learning—0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
Achieving PAC Guarantees in Mechanism Design through Multi-Armed Bandits—0
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen—0
Off-policy estimation with adaptively collected data: the power of online learning—0
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions—0
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Benchmark Results

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