4 papers
Distributed Online Learning for Joint Regret with Communication Constraints
Dirk van der Hoeven, Hédi Hadiji, Tim van Erven
We consider distributed online learning for joint regret with communication constraints. In this setting, there are multiple agents that are connected in a graph. Each round, an ad…
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven
We provide a new adaptive method for online convex optimization, MetaGrad, that is robust to general convex losses but achieves faster rates for a broad class of special functions,…
Comparator-adaptive Convex Bandits
Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo
We study bandit convex optimization methods that adapt to the norm of the comparator, a topic that has only been studied before for its full-information counterpart. Specifically,…
Exploiting the Surrogate Gap in Online Multiclass Classification
Dirk van der Hoeven
We present Gaptron, a randomized first-order algorithm for online multiclass classification. In the full information setting we show expected mistake bounds with respect to the log…