25 citations · 125 across the 27 of their papers we have counts for
3 papers · 1 filter
Parallel training of linear models without compromising convergence
Nikolas Ioannou, Celestine Dünner, Kornilios Kourtis +1
In this paper we analyze, evaluate, and improve the performance of training generalized linear models on modern CPUs. We start with a state-of-the-art asynchronous parallel trainin…
A Distributed Second-Order Algorithm You Can Trust
Celestine Dünner, Aurelien Lucchi, Matilde Gargiani +3
Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate…
Snap ML: A Hierarchical Framework for Machine Learning
Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis +5
We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learn…