46 citations · 137 across the 23 of their papers we have counts for
25 papers
Robust Stochastic Optimization via Gradient Quantile Clipping
Ibrahim Merad, Stéphane Gaïffas
We introduce a clipping strategy for Stochastic Gradient Descent (SGD) which uses quantiles of the gradient norm as clipping thresholds. We prove that this new strategy provides a…
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
Massil Hihat, Stéphane Gaïffas, Guillaume Garrigos +1
We study multi-product inventory control problems where a manager makes sequential replenishment decisions based on partial historical information in order to minimize its cumulati…
Convergence and concentration properties of constant step-size SGD through Markov chains
Ibrahim Merad, Stéphane Gaïffas
We consider the optimization of a smooth and strongly convex objective using constant step-size stochastic gradient descent (SGD) and study its properties through the prism of Mark…
Robust Methods for High-Dimensional Linear Learning
Ibrahim Merad, Stéphane Gaïffas
We propose statistically robust and computationally efficient linear learning methods in the high-dimensional batch setting, where the number of features may exceed the sample…
Robust supervised learning with coordinate gradient descent
Stéphane Gaïffas, Ibrahim Merad
This paper considers the problem of supervised learning with linear methods when both features and labels can be corrupted, either in the form of heavy tailed data and/or corrupted…
WildWood: a new Random Forest algorithm
Stéphane Gaïffas, Ibrahim Merad, Yiyang Yu
We introduce WildWood (WW), a new ensemble algorithm for supervised learning of Random Forest (RF) type. While standard RF algorithms use bootstrap out-of-bag samples to compute ou…