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ENTITY Logarithmic High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback

Logarithmic High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback

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  1. TOOL · CL_139637 ·

    New theory achieves logarithmic high-probability regret in online convex optimization

    Researchers have developed a new theoretical framework for online convex optimization (OCO) that achieves logarithmic high-probability regret. This advancement addresses the challenge of learning with limited feedback, …