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Xerox Research Centre Europe
I currently work in the group 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 and Chris Dance 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: matlab code to demonstrate the upper-bound to the log-sum-exp function
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.
List of publications from 2005 to now
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Guillaume Bouchard
RESEARCH