36 citations · 94 across the 19 of their papers we have counts for
4 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…
A High Probability Analysis of Adaptive SGD with Momentum
Xiaoyu Li, Francesco Orabona
Stochastic Gradient Descent (SGD) and its variants are the most used algorithms in machine learning applications. In particular, SGD with adaptive learning rates and momentum is th…
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes
Xiaoyu Li, Francesco Orabona
Stochastic gradient descent is the method of choice for large scale optimization of machine learning objective functions. Yet, its performance is greatly variable and heavily depen…
Online Learning for Changing Environments using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright +1
A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed "meta" algorithms that convert any onlin…