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 176–200 of 1262 papers

TitleStatusHype
Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation—0
Online Learning for Autonomous Management of Intent-based 6G Networks—0
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare—0
Scalable Exploration via Ensemble++Code0
Satisficing Exploration for Deep Reinforcement Learning—0
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards—0
On Speeding Up Language Model Evaluation—0
Honor Among Bandits: No-Regret Learning for Online Fair Division—0
A Contextual Combinatorial Bandit Approach to Negotiation—0
Classical Bandit Algorithms for Entanglement Detection in Parameterized Qubit States—0
Jump Starting Bandits with LLM-Generated Prior KnowledgeCode0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes—0
Towards Bayesian Data Selection—0
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
An Adaptive Method for Contextual Stochastic Multi-armed Bandits with Rewards Generated by a Linear Dynamical System—0
Linear Contextual Bandits with Hybrid Payoff: RevisitedCode0
Towards Domain Adaptive Neural Contextual Bandits—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Asymptotically Optimal Regret for Black-Box Predict-then-Optimize—0
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning—0
A conversion theorem and minimax optimality for continuum contextual bandits—0
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits—0
Adaptively Learning to Select-Rank in Online Platforms—0
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Benchmark Results

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