19 citations · 32 across the 10 of their papers we have counts for
4 papers · 1 filter
Distributed Learning with Sparse Communications by Identification
Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick +1
In distributed optimization for large-scale learning, a major performance limitation comes from the communications between the different entities. When computations are performed b…
A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm
Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick
We develop and analyze an asynchronous algorithm for distributed convex optimization when the objective writes a sum of smooth functions, local to each worker, and a non-smooth fun…
Model Consistency for Learning with Mirror-Stratifiable Regularizers
Jalal Fadili, Guillaume Garrigos, Jérome Malick +1
Low-complexity non-smooth convex regularizers are routinely used to impose some structure (such as sparsity or low-rank) on the coefficients for linear predictors in supervised lea…
On the Proximal Gradient Algorithm with Alternated Inertia
Franck Iutzeler, Jerome Malick
In this paper, we investigate the attractive properties of the proximal gradient algorithm with inertia. Notably, we show that using alternated inertia yields monotonically decreas…