Online Mirror Descent
PulseAugur coverage of Online Mirror Descent — every cluster mentioning Online Mirror Descent across labs, papers, and developer communities, ranked by signal.
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New bandit algorithms tackle fairness and continuous K-Max problems · 6 sources tracked
Researchers have developed new algorithms for bandit problems, which are used in applications like recommendation systems. For continuous K-Max bandits, DCK-UCB achieves a sublinear regret bound of $\widetilde{O}(T^{3/4…
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New research details approximation costs in Online Mirror Descent
A new paper explores the impact of approximation errors in Online Mirror Descent (OMD), a core algorithm for optimization and machine learning. The research reveals a complex relationship between the smoothness of the r…
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Prudent-Banker algorithm ensures safety in delayed bandit feedback
Researchers have introduced Prudent-Banker, a new algorithm designed for adversarial multi-armed bandits that maintains safety guarantees even with delayed feedback. This novel approach combines a delay-adapted Online M…