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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–900 of 1262 papers

TitleStatusHype
Risk-Aware Algorithms for Adversarial Contextual Bandits—0
Risk-aware linear bandits with convex loss—0
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions—0
Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents—0
Robust Contextual Linear Bandits—0
Exploiting Heterogeneity in Robust Federated Best-Arm Identification—0
Robust Generalization of Quadratic Neural Networks via Function Identification—0
Robust Multi-Agent Multi-Armed Bandits—0
Robustness Guarantees for Mode Estimation with an Application to Bandits—0
Robust Pareto Set Identification with Contaminated Bandit Feedback—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks—0
Rotting Bandits—0
Rotting bandits are not harder than stochastic ones—0
Safe Linear Leveling Bandits—0
Safety-Aware Algorithms for Adversarial Contextual Bandit—0
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
Sample complexity of partition identification using multi-armed bandits—0
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning—0
Satisficing Exploration for Deep Reinforcement Learning—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable Discrete Sampling as a Multi-Armed Bandit Problem—0
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees—0
Scale Free Adversarial Multi Armed Bandits—0
Scaling Multi-Armed Bandit Algorithms—0
Second Order Bounds for Contextual Bandits with Function Approximation—0
Selecting the best system and multi-armed bandits—0
Selective Harvesting over Networks—0
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes—0
Selectively Contextual Bandits—0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
Selfish Robustness and Equilibria in Multi-Player Bandits—0
Self-Supervised Contextual Bandits in Computer Vision—0
Self-Tuning Bandits over Unknown Covariate-Shifts—0
Semantic Parsing for Planning Goals as Constrained Combinatorial Contextual Bandits—0
Semi-Parametric Batched Global Multi-Armed Bandits with Covariates—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
Sequential Batch Learning in Finite-Action Linear Contextual Bandits—0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems—0
Sequential Design for Ranking Response Surfaces—0
Sequential Monte Carlo Bandits—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features—0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms—0
Shuffle Private Linear Contextual Bandits—0
Simple Regret Minimization for Contextual Bandits—0
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition—0
Skyline Identification in Multi-Armed Bandits—0
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Benchmark Results

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