898 citations · 1.2k across the 19 of their papers we have counts for
4 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…
Elastic CoCoA: Scaling In to Improve Convergence
Michael Kaufmann, Thomas Parnell, Kornilios Kourtis
In this paper we experimentally analyze the convergence behavior of CoCoA and show, that the number of workers required to achieve the highest convergence rate at any point in time…
Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms
Andreea Anghel, Nikolaos Papandreou, Thomas Parnell +2
Gradient boosting decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in many machine…
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…