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Two-Point Bandit Feedback
Two-Point Bandit Feedback
PulseAugur coverage of Two-Point Bandit Feedback — every cluster mentioning Two-Point Bandit Feedback across labs, papers, and developer communities, ranked by signal.
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Papers · 30d
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RECENT · PAGE 1/1 · 2 TOTAL
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New algorithm achieves optimal regret for decentralized Riemannian optimization
Researchers have developed a new method for decentralized online optimization on Riemannian manifolds, specifically addressing strongly geodesically convex functions. This work establishes the first static regret bound …
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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, …