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Nikolas Ioannou

3 accepted papers

2020

SnapBoost: A Heterogeneous Boosting Machine

NeurIPS 2020poster

Modern gradient boosting software frameworks, such as XGBoost and LightGBM, implement Newton descent in a functional space. At each boosting iteration, their goal is to find the base hypothesis, selected from some base hypothesis class, that is closest to the Newton descent direction in a Euclidean…

2019

SySCD: A System-Aware Parallel Coordinate Descent Algorithm

NeurIPS 2019spotlight

In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm with convergence guarantees that exhibits strong scalability. We start by studying a state-of-the-art parallel implementation of SCD and identify scalability as well as system-level performance bottlenecks of the…

2018

Snap ML: A Hierarchical Framework for Machine Learning

NeurIPS 2018poster

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 learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems. We p…