19 citations · 35 across the 2 of their papers we have counts for
4 papers · 1 filter
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.…
Adaptive Catalyst for Smooth Convex Optimization
Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko +3
In this paper, we present a generic framework that allows accelerating almost arbitrary non-accelerated deterministic and randomized algorithms for smooth convex optimization probl…
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…
Privacy Preserving Randomized Gossip Algorithms
Filip Hanzely, Jakub Konečný, Nicolas Loizou +2
In this work we present three different randomized gossip algorithms for solving the average consensus problem while at the same time protecting the information about the initial p…