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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 101–150 of 1262 papers

TitleStatusHype
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
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
An optimal learning method for developing personalized treatment regimes—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—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
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache—0
A One-Size-Fits-All Solution to Conservative Bandit Problems—0
Approximate Function Evaluation via Multi-Armed Bandits—0
Approximately Stationary Bandits with Knapsacks—0
Adversarial Attacks on Adversarial Bandits—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
A Reinforcement-Learning-Enhanced LLM Framework for Automated A/B Testing in Personalized Marketing—0
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
Adversarial Bandits with Knapsacks—0
A Survey of Risk-Aware Multi-Armed Bandits—0
Adversarial Contextual Bandits Go Kernelized—0
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits—0
Bandits Warm-up Cold Recommender Systems—0
Bandits with Temporal Stochastic Constraints—0
Algorithms for multi-armed bandit problems—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Functional multi-armed bandit and the best function identification problems—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
A General Framework for Off-Policy Learning with Partially-Observed Reward—0
Bandit Algorithms for Prophet Inequality and Pandora's Box—0
Ballooning Multi-Armed Bandits—0
A General Framework for Bandit Problems Beyond Cumulative Objectives—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits—0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
BanditQ: Fair Bandits with Guaranteed Rewards—0
A Gang of Bandits—0
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Benchmark Results

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