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cs.LG2025
Exploiting Curvature in Online Convex Optimization with Delayed Feedback
Hao Qiu, Emmanuel Esposito, Mengxiao Zhang
In this work, we study the online convex optimization problem with curved losses and delayed feedback. When losses are strongly convex, existing approaches obtain regret bounds of…
cs.LG2025
When Lower-Order Terms Dominate: Adaptive Expert Algorithms for Heavy-Tailed Losses
Antoine Moulin, Emmanuel Esposito, Dirk van der Hoeven
We consider the problem setting of prediction with expert advice with possibly heavy-tailed losses, i.e. the only assumption on the losses is an upper bound on their second moments…