6 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Dynamic Regret Reduces to Kernelized Static Regret
Andrew Jacobsen, Alessandro Rudi, Francesco Orabona +1
We study dynamic regret in online convex optimization, where the objective is to achieve low cumulative loss relative to an arbitrary benchmark sequence. By observing that competin…
Self-Directed Learning of Convex Labelings on Graphs
Georgy Sokolov, Maximilian Thiessen, Margarita Akhmejanova +2
We study the problem of classifying the nodes of a given graph in the self-directed learning setup. This learning setting is a variant of online learning, where rather than an adve…
An Equivalence Between Static and Dynamic Regret Minimization
Andrew Jacobsen, Francesco Orabona
We study the problem of dynamic regret minimization in online convex optimization, in which the objective is to minimize the difference between the cumulative loss of an algorithm…
On Multilabel Classification and Ranking with Partial Feedback
Claudio Gentile, Francesco Orabona
We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and relies on upper-confidence bounds…