36 citations · 86 across the 14 of their papers we have counts for
20 papers
Implicit Parameter-free Online Learning with Truncated Linear Models
Keyi Chen, Ashok Cutkosky, Francesco Orabona
Parameter-free algorithms are online learning algorithms that do not require setting learning rates. They achieve optimal regret with respect to the distance between the initial po…
Understanding AdamW through Proximal Methods and Scale-Freeness
Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky +1
Adam has been widely adopted for training deep neural networks due to less hyperparameter tuning and remarkable performance. To improve generalization, Adam is typically used in ta…
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…
Online Learning with Optimism and Delay
Genevieve Flaspohler, Francesco Orabona, Judah Cohen +4
Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guar…
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…
Parameter-free Stochastic Optimization of Variationally Coherent Functions
Francesco Orabona, Dávid Pál
We design and analyze an algorithm for first-order stochastic optimization of a large class of functions on . In particular, we consider the \emph{variationally coher…