19 citations · 32 across the 10 of their papers we have counts for
5 papers · 1 filter
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications
Franck Iutzeler, Jérôme Malick
Nonsmoothness is often a curse for optimization; but it is sometimes a blessing, in particular for applications in machine learning. In this paper, we present the specific structur…
First-order Optimization for Superquantile-based Supervised Learning
Yassine Laguel, Jérôme Malick, Zaid Harchaoui
Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training dist…
Randomized Progressive Hedging methods for Multi-stage Stochastic Programming
Gilles Bareilles, Yassine Laguel, Dmitry Grishchenko +2
Progressive Hedging is a popular decomposition algorithm for solving multi-stage stochastic optimization problems. A computational bottleneck of this algorithm is that all scenario…
Proximal Gradient methods with Adaptive Subspace Sampling
Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick
Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions.…
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning. The basic premise of t…