Get our free extension to see links to code for papers anywhere online!

 Add to Chrome

 Add to Firefox

CatalyzeX Code Finder - Browser extension linking code for ML papers across the web! | Product Hunt Embed
Parallel training of linear models without compromising convergence

Nov 05, 2018
Nikolas Ioannou, Celestine Dünner, Kornilios Kourtis, Thomas Parnell


  Access Paper or Ask Questions

A Distributed Second-Order Algorithm You Can Trust

Jun 20, 2018
Celestine Dünner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi

* appearing at ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, Stockholm, Schweden, PMLR 80, 2018 

  Access Paper or Ask Questions

Snap ML: A Hierarchical Framework for Machine Learning

Jun 18, 2018
Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Haralampos Pozidis


  Access Paper or Ask Questions

Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark

Dec 13, 2017
Celestine Dünner, Thomas Parnell, Kubilay Atasu, Manolis Sifalakis, Haralampos Pozidis

* To appear in the 2017 IEEE International Conference on Big Data (Big Data 2017), December 11-14, 2017, Boston, MA, USA 

  Access Paper or Ask Questions

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

Nov 07, 2017
Celestine Dünner, Thomas Parnell, Martin Jaggi


  Access Paper or Ask Questions

Large-Scale Stochastic Learning using GPUs

Feb 22, 2017
Thomas Parnell, Celestine Dünner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis

* Accepted for publication in ParLearning 2017: The 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics, Orlando, Florida, May 2017 

  Access Paper or Ask Questions

Primal-Dual Rates and Certificates

Jun 02, 2016
Celestine Dünner, Simone Forte, Martin Takáč, Martin Jaggi

* appearing at ICML 2016 - Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48 

  Access Paper or Ask Questions