106 citations · 525 across the 44 of their papers we have counts for
7 papers · 1 filter
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…
Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio
Francesco Orabona, Kwang-Sung Jun
A classic problem in statistics is the estimation of the expectation of random variables from samples. This gives rise to the tightly connected problems of deriving concentration i…
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…
On the Initialization for Convex-Concave Min-max Problems
Mingrui Liu, Francesco Orabona
Convex-concave min-max problems are ubiquitous in machine learning, and people usually utilize first-order methods (e.g., gradient descent ascent) to find the optimal solution. One…
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…
On the Last Iterate Convergence of Momentum Methods
Xiaoyu Li, Mingrui Liu, Francesco Orabona
SGD with Momentum (SGDM) is a widely used family of algorithms for large-scale optimization of machine learning problems. Yet, when optimizing generic convex functions, no advantag…
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…