Scalable Algorithms for Large-scale Machine Learning Problems: Application to Multi-class Classification and Asynchronous Distributed Optimization.


Spécialité : Informatique

26/09/2017 - 14:00 Mr Joshi Bikash Amphithéâtre C005-Site ampère

This thesis focuses on developing scalable algorithms for large scale machine learning. In this work, we present two perspectives to handle large data.
First, we consider the problem of large-scale multiclass classification. We introduce the task of multiclass classification and the challenge of classifying with a large number of classes. To alleviate these challenges, we propose an algorithm which reduces the original multiclass problem to an equivalent binary one. Based on this reduction technique, we introduce a scalable method to tackle the multiclass classification problem for very large number of
classes and perform detailed theoretical and empirical analyses. In the second part, we discuss the problem of distributed machine learning. In this domain, we introduce an asynchronous framework for performing distributed optimization. We present application of the proposed asynchronous framework on two popular domains: matrix factorization for large-scale recommender systems and large-scale binary classification.


  • Mr Franck Iutzeler (Maître de Conférénces - Université Grenoble Alpes )


  • Mr Stephane Canu (Professeur - INSA de Rouen )
  • Mr Thierry Artières (Professeur - Ecole Centrale Marseille )


  • Mr Massih-Reza Amini (Professeur - Université Grenoble Alpes )
  • Mr Jerome Malick (Directeur de recherche - CNRS )