Search Results for author: Nikolaos Papandreou

Found 4 papers, 1 papers with code

SnapBoost: A Heterogeneous Boosting Machine

2 code implementations NeurIPS 2020 Thomas Parnell, Andreea Anghel, Malgorzata Lazuka, Nikolas Ioannou, Sebastian Kurella, Peshal Agarwal, Nikolaos Papandreou, Haralampos Pozidis

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 sense.

Differentially Private Stochastic Coordinate Descent

no code implementations12 Jun 2020 Georgios Damaskinos, Celestine Mendler-Dünner, Rachid Guerraoui, Nikolaos Papandreou, Thomas Parnell

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private.

Breadth-first, Depth-next Training of Random Forests

no code implementations15 Oct 2019 Andreea Anghel, Nikolas Ioannou, Thomas Parnell, Nikolaos Papandreou, Celestine Mendler-Dünner, Haris Pozidis

In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures.

Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms

no code implementations12 Sep 2018 Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro De Palma, Haralampos Pozidis

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 learning tasks.

Bayesian Optimization Benchmarking

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