Master Internship proposal 1 – April to september 2018

Project:	Large-scale Structured Prediction with theoretical guarantees
	
	Standard ML techniques learn real valued functions. However nowadays many machine learning problems require to learn functions with structured output e.g. automatic text translation (string -> string), image captioning (image -> string), speech recognition, prediction on graphs, learning on manifolds, learning to rank documents, protein folding, etc. In this context current research [1] focuses on developing ad hoc algorithms for each structured problem, often without theoretical guarantees. 

Recently, [2] proposed a unifying theoretical framework to address general structured prediction problems by introducing a novel learning strategy with strong statistical guarantees.  In particular the derived methods are expressed in terms of an optimization problem in the output space. 

The goal of this internship is to extend the results in [2] by designing novel optimization strategies to efficiently solve structured prediction problems in practice and deal with large scale datasets. The work is at the intersection of algorithms, statistics and optimization, and may focus primarily on any these three aspects depending on the candidate.

Students interested in this project should contact Alessandro Rudi or Francis Bach to discuss further.

This internship may be extended to a PhD within the SIERRA team of Département d’Informatique de l’Ecole Normale Supérieure.
	
Pre-requisites:	Machine learning classes (theory, kernel methods)
Convex optimization

Length:	4-6 months

Compensation:	Approximately 1200 per month

Laboratory:	INRIA - Département d’Informatique de l’ENS
	2, rue Simone Iff, 75012 Paris

Contact:	Alessandro Rudi (alessandro.rudi@inria.fr), Francis Bach (francis.bach@inria.fr)

[1] Gükhan H. Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar, and S. V. N. Vishwanathan. 2007. Predicting Structured Data (Neural Information Processing). The MIT Press.

[2] Ciliberto, C., Rudi, A. and Rosasco, L. A Consistent Regularization Approach for Structured Prediction. 
In Advances in Neural Information Processing Systems 2016. (pp. 4412-4420). Barcelona