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 1076–1100 of 1262 papers

TitleStatusHype
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Algorithms for multi-armed bandit problems—0
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits—0
Almost Boltzmann Exploration—0
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
An Adaptive Method for Contextual Stochastic Multi-armed Bandits with Rewards Generated by a Linear Dynamical System—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
An Analysis of Reinforcement Learning for Malaria Control—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits—0
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives—0
An Efficient Algorithm for Deep Stochastic Contextual Bandits—0
An Empirical Evaluation of Federated Contextual Bandit Algorithms—0
An Empirical Evaluation of Thompson Sampling—0
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free—0
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits—0
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit—0
An Instrumental Value for Data Production and its Application to Data Pricing—0
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
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Benchmark Results

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