898 citations · 1.2k across the 19 of their papers we have counts for
7 papers · 1 filter
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Nikolas Ioannou, Celestine Mendler-Dünner, Thomas Parnell
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-o…
Breadth-first, Depth-next Training of Random Forests
Andreea Anghel, Nikolas Ioannou, Thomas Parnell +3
In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures. An exact, state-of-the-art binary decision tree b…
Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
Michael Kaufmann, Kornilios Kourtis, Celestine Mendler-Dünner +2
Distributed machine learning training is one of the most common and important workloads running on data centers today, but it is rarely executed alone. Instead, to reduce costs, co…
Learning to Tune XGBoost with XGBoost
Johanna Sommer, Dimitrios Sarigiannis, Thomas Parnell
In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH…
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning
Dimitrios Sarigiannis, Thomas Parnell, Haris Pozidis
The combined algorithm selection and hyperparameter tuning (CASH) problem is characterized by large hierarchical hyperparameter spaces. Model-free hyperparameter tuning methods can…
5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory
Martino Dazzi, Abu Sebastian, Pier Andrea Francese +3
In-memory computing is an emerging computing paradigm that could enable deeplearning inference at significantly higher energy efficiency and reduced latency. The essential idea is…