Celestine Dünner — accepted AI-conference papers

2018

Snap ML: A Hierarchical Framework for Machine Learning

Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Gummadi Ravi +2

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…

Cited by 31 Source ↗PDF ↗code ↗Scholar ↗

2017

Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems

Celestine Dünner, Thomas Parnell, Martin Jaggi

NeurIPS 2017poster

We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems. Our scheme allows to efficiently employ compute accelerators such as GPUs and FPGAs for the training of large-scale machine learning models, when the training data exc…

Cited by 18 Source ↗PDF ↗Scholar ↗

2016

Primal-Dual Rates and Certificates

Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi

ICML 2016poster

We propose an algorithm-independent framework to equip existing optimization methods with primal-dual certificates. Such certificates and corresponding rate of convergence guarantees are important for practitioners to diagnose progress, in particular in machine learning applications. We obtain new p…

Cited by 73 Source ↗PDF ↗Scholar ↗