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 126–150 of 1262 papers

TitleStatusHype
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation—0
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints—0
Optimal Streaming Algorithms for Multi-Armed Bandits—0
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits—0
Contextual Bandits with Arm Request Costs and Delays—0
Online Learning for Function Placement in Serverless ComputingCode0
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?—0
How Does Variance Shape the Regret in Contextual Bandits?—0
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
EVOLvE: Evaluating and Optimizing LLMs For Exploration—0
Stochastic Bandits for Egalitarian Assignment—0
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback—0
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Online Posterior Sampling with a Diffusion Prior—0
Minimax-optimal trust-aware multi-armed bandits—0
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs—0
On Lai's Upper Confidence Bound in Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Stabilizing the Kumaraswamy Distribution—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
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Benchmark Results

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