Guillaume Bouchard

I currently work in the Large Scale Data Mining group at XRCE. My main research interest is probabilistic inference and applications of statistical learning in various domains such as user modeling, device monitoring, computer vision and structural reliability.

I work with Jean-Marc Andreoli applying data mining techniques to several applications, including print infrastructure optimization and content creation modeling.

I'm also member of the PASCAL network of excellence.

New:NIPS 2009 Workshop on the Generative and Discriminative Learning Interface

Research

Generative and discriminative methods

In Machine Learning, generative and discriminative methods have both advantages and drawbacks. My main contributions to this domain is the definition of a hybrid generative-discriminative estimation technique (called Generative-Discriminative Tradeoff) and the proof of its optimality under week conditions. I'm still working on this topic, mainly on the inference which is in general a difficult optimization problem.

Graphical models

Graphical models (a.k.a. Bayesian networks) are graphs that encode conditional independencies between random variables. They provide an efficient "language" to express complex probability distributions. Many real world systems can be modeled using this paradigm. In many applications, I tried the power of this representation to understand, compare and improve existing algorithms using graphical models.

Probabilistic inference

Many machine learning tasks can be expressed in term of inference in a probabilistic model. In many situations (e.g. in graphical models), this problem is intractable but can be efficiently approximated using approximate solutions. Originally comming from the MCMC community, my research focuses nowadays on variational techniques, such as Variational Mean-Field algorithms.

Publications

List of publications from 2005 to now

Education

I received an engineering degree in mathematics from Institut National des Sciences Appliquées and a master in Applied Mathematic with a specialization in probability theory in 2001. I received in May 2005 a PhD in statistics from INRIA Rhône-Alpes and Université Joseph Fourier (France). My PhD supervisors were Gilles Celeux from the SELECT team, and Bill Triggs, from the LEAR project. My grant was provided by the LAVA project.

Code

matlab code to demonstrate the upper-bound to the log-sum-exp function