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 251–275 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
Generalized Linear Bandits with Limited AdaptivityCode0
Sequential Decision Making with Expert Demonstrations under Unobserved HeterogeneityCode0
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
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning—0
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—0
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits—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
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Benchmark Results

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