Towards Uncovering the True Use of Unlabeled Data in Machine Learning

PhD candidate Emanuele Sansone
29 March 2018
March 29, 2018

Time: h 09:30 am
Location: Room Ofek, Polo Ferrari 1 - Via Sommarive 5, Povo (TN) 

PhD Candidate

  • Emanuele Sansone

Abstract of Dissertation

Knowing how to exploit unlabeled data is a fundamental problem in machine learning. This dissertation provides contributions in different contexts, including semi-supervised learning, positive unlabeled learning and representation learning. In particular, we ask (i) whether is possible to learn a classifier in the context of limited data, (ii) whether is possible to scale existing models for positive unlabeled learning, and (iii) whether is possible to train a deep generative model with a single minimization problem.