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 201–225 of 1262 papers

TitleStatusHype
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
Contextual Bandits for Unbounded Context Distributions—0
Reciprocal Learning—0
Hierarchical Multi-Armed Bandits for the Concurrent Intelligent Tutoring of Concepts and Problems of Varying Difficulty LevelsCode0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous AgentsCode0
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
On Speeding Up Language Model Evaluation—0
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards—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
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
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Benchmark Results

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