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

12:00
Tuesday
26
Sep
2017
  • Thesis defence
Organized by: 
Bikash Joshi
Speaker: 
Bikash Joshi
Teams: 

Venue :

in the Site Ampère, amphi C005 (near Gabriel Fauré tram stop) 

Jury :

  • Stephane Canu, professeur à l'INSA de Rouen, rapporteur,
  • Thierry Artieres, professeur à l'Ecole Centrale Marseille, rapporteur,
  • Marianne Clausel, professeur à l'Université de Nancy Lorraine, examinateur,
  • Jérôme Malick, directeur de recherche CNRS au Laboratoire Jean Kuntzmann, Univ. Grenoble Alpes, examinateur,
  • Massih-Reza Amini, professeur à l'Université Grenoble Alpes, directeur de thèse,
  • Franck Iutzeler, maître de conférences à l'Université Grenoble Alpes,co-encadrant de thèse.

 

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.