3 citations · 3 across the 2 of their papers we have counts for
3 papers
stat.ML2021
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Jeffrey Negrea, Blair Bilodeau, Nicolò Campolongo +2
Quantile (and, more generally, KL) regret bounds, such as those achieved by NormalHedge (Chaudhuri, Freund, and Hsu 2009) and its variants, relax the goal of competing against the…
cs.LG2021★ 3 cited
A closer look at temporal variability in dynamic online learning
Nicolò Campolongo, Francesco Orabona
This work focuses on the setting of dynamic regret in the context of online learning with full information. In particular, we analyze regret bounds with respect to the temporal var…
cs.LG2020
Temporal Variability in Implicit Online Learning
Nicolò Campolongo, Francesco Orabona
In the setting of online learning, Implicit algorithms turn out to be highly successful from a practical standpoint. However, the tightest regret analyses only show marginal improv…