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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 151–175 of 1262 papers

TitleStatusHype
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
Variance-Aware Linear UCB with Deep Representation for Neural Contextual BanditsCode0
Multi-armed Bandits with Missing OutcomeCode0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Structure Matters: Dynamic Policy Gradient—0
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
PageRank Bandits for Link PredictionCode0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
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
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?—0
Contextual Bandits with Arm Request Costs and Delays—0
Online Learning for Function Placement in Serverless ComputingCode0
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
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
EVOLvE: Evaluating and Optimizing LLMs For Exploration—0
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits—0
Stochastic Bandits for Egalitarian Assignment—0
DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback—0
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Benchmark Results

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