My research is in probabilistic machine learning. Machine learning is a key component in data science. It is closely related to artificial intelligence and has a large overlap with statistics. My work focuses on problems in automated-decision making, collaborative filtering, relational learning and natural language processing. The Bayesian paradigm provides a natural framework for dealing with the uncertainty we encounter in any real-life situation. One of the main challenges today is to scale up inference algorithms to the exponential increase of available digital data. Being able to assimilate more data is important as more data means more reliable and nuanced data models. Hence, I recently got interested in distributed approaches and very large scale inference algorithms.
I received the Electrical Engineering degree and the PhD in Applied
Sciences from the Université
catholique de Louvain, respectively in 2001 and 2005. I was a member
of the Machine Learning
Group and the Crypto
Group. Until mid 2005, I was involved in the European project OPTIVIP, in which we developed a visual prosthesis for blind
people. Since then, I contributed to the European projects SCARD, PinView, FUPOL and Fusepool, as well as the UK funded
project VISDEM. At present, I hold an Honorary Senior Research Associate position in the Centre for Computational
Statistics and Machine Learning at University College London. I was also an
active participant of the PASCAL network of
In October 2009, I joined Xerox Research Centre Europe, where I led the Machine Learning group. The team conducted applied research in computational statistics and mechanism design. I am now a Senior Research Scientist with Machine Learning Science at Amazon Berlin. Recently, I served as an Area Chair for NIPS '11, NIPS '13 and AISTATS '14. I was Tutorials Chair at ECML-PKDD
'09 and Industry Track Chair at ECML-PKDD '12.
NIPS workshop on Choice Models and
Preference Learning, Grenada, Spain, 2011.
Workshop on Automated Knowledge Base Construction, Grenoble, France, 2010.
PASCAL2 workshop on
Approximate Inference in Stochastic Processes and Dynamical Systems, Cumberland Lodge, United Kingdom, 2008.
NIPS workshop on Dynamical
Systems, Stochastic Processes and Bayesian Inference, Whistler, Canada, 2006.
Engineering in Computer Science '12 at ENSIMAG: Statistical Principles and Methods.
MSc in Machine Learning '11 (Applied Machine Learning) at UCL: Machine Learning at Xerox -- From statistical
machine translation to large-scale image search.
Tutorial on Probabilistic Graphical Models at PASCAL Bootcamp
MSc in Intelligent Systems '08 at UCL: Advanced
Topics in Machine Learning.
CSML'07 reading group on
Stochastic Differential Equations.
Probabilistic Models in
Noisy Environments - And their Application to a Visual Prosthesis for
C. Archambeau Doctoral dissertation,
Université catholique de Louvain, Louvain-la-Neuve, Belgium,
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