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Online Uniform Risk Times Sampling: First Approximation Algorithms, Learning Augmentation with Full Confidence Interval Integration
We propose two online approximation algorithms for this problem, one with and one without learning augmentation, and provide rigorous theoretical performance guarantees for them using competitive ratio analysis.
Contextual Bandits with Budgeted Information Reveal
Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments.
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