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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 251–300 of 1262 papers

TitleStatusHype
Leveraging (Biased) Information: Multi-armed Bandits with Offline Data—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback—0
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Disentangling Exploration from Exploitation—0
Causally Abstracted Multi-armed BanditsCode0
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks—0
Sequential Decision Making with Expert Demonstrations under Unobserved HeterogeneityCode0
Generalized Linear Bandits with Limited AdaptivityCode0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem—0
A Correction of Pseudo Log-Likelihood Method—0
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making—0
Transfer in Sequential Multi-armed Bandits via Reward Samples—0
Phasic Diversity Optimization for Population-Based Reinforcement Learning—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning—0
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits—0
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—0
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
Batched Nonparametric Contextual Bandits—0
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace RecoveryCode0
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement—0
Optimistic Information Directed Sampling—0
Multi-Armed Bandits with Abstention—0
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health—0
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits—0
Incentivized Exploration via Filtered Posterior Sampling—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
FLASH: Federated Learning Across Simultaneous Heterogeneities—0
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits—0
Replicability is Asymptotically Free in Multi-armed Bandits—0
Contextual Multinomial Logit Bandits with General Value Functions—0
Efficient Contextual Bandits with Uninformed Feedback Graphs—0
Stochastic contextual bandits with graph feedback: from independence number to MAS number—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Tree Ensembles for Contextual Bandits—0
Fairness of Exposure in Online Restless Multi-armed BanditsCode0
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic BanditsCode0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Fairness and Privacy Guarantees in Federated Contextual Bandits—0
Off-Policy Evaluation of Slate Bandit Policies via Optimizing AbstractionCode0
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Benchmark Results

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