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 51–100 of 1262 papers

TitleStatusHype
Adaptive, Robust and Scalable Bayesian Filtering for Online Learning—0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
ADARES: Adaptive Resource Management for Virtual Machines—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health—0
Bandits with Knapsacks beyond the Worst-Case—0
Adversarial Attacks on Adversarial Bandits—0
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
Adversarial Attacks on Cooperative Multi-agent Bandits—0
Adversarial Attacks on Linear Contextual Bandits—0
Adversarial Bandits with Knapsacks—0
Adversarial Contextual Bandits Go Kernelized—0
Approximate Function Evaluation via Multi-Armed Bandits—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
Approximately Stationary Bandits with Knapsacks—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds—0
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Adaptive Exploration in Linear Contextual Bandit—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON—0
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
Adaptive Endpointing with Deep Contextual Multi-armed Bandits—0
A Correction of Pseudo Log-Likelihood Method—0
Almost Boltzmann Exploration—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits—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
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
An Empirical Evaluation of Thompson Sampling—0
Adaptively Learning to Select-Rank in Online Platforms—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
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—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