My goal is to advance machine intelligence to benefit the people. Machine learning has a large overlap with statistics, plays a central role in data science, and is fuelling the AI revolution we are experiencing today. My research interests lie in probabilistic machine learning and Bayesian decision making. My recent work focusses on learning representations, meta-learning, and continual learning.
I received the Electrical Engineering degree and the PhD in Applied Sciences from the UCLouvain, respectively in 2001 and 2005. As a member of the Machine Learning Group and the Crypto Group, I worked on the European projects OPTIVIP, in which I developed neural networks embedded in a visual prosthesis for the blind, and SCARD, where I evaluated the robustness of cryptographic hardware against machine learning-based side channel attacks exploiting electro-magnetic radiation. Next, I did a Post-doc with John Shawe-Taylor at University College London and collaborated closely with Manfred Opper. I was also an active participant of the PASCAL European network of excellence. Until December 2015, I held a Honorary Senior Research Associate position in the Centre for Computational Statistics and Machine Learning.
In October 2009, I joined Xerox Research Centre Europe (now Naver Labs Europe), where I led the Machine Learning group. My team conducted applied research in machine learning, computational statistics and mechanism design, with applications in customer care, transportation and governmental services. I joined Amazon, Berlin, as an Applied Science Manager in October 2013, where I was in charge of delivering zero-parameter machine learning algorithms. I am now a Principal Applied Scientist at Amazon Web Services, where I oversee the product-related science powering Amazon SageMaker and lead long-term science initiatives in the area of automated machine learning. I served as an Area Chair for NIPS '11, NIPS '13, AISTATS '14, AISTATS '15, ICML '15, NIPS '17, ICML '18, NIPS '18, IJCAI '19, NIPS '19, AISTATS '20, IJCAI '20, NIPS '20, ICML '21, ICLR '21, and NIPS '21. I was Tutorials Chair at ECML-PKDD '09 and Industry Track Chair for ECML-PKDD '12. I taught several lectures at the University of Oxford since 2017, as well as a lecture on Bayesian optimisation at the Machine Learning Summer School '16 and the Data Science Summer School '17.
Lectures and selected presentations
Mini-symposium on Bayesian Methods in Science and Engineering at the SIAM Conference on Computational Science and Engineering: Bayesian Optimization by Density-Ratio Estimation.
CVPR 2020 tutorial
From HPO to NAS: Automated Deep Learning: Automated HP and Architecture Tuning. (recording)
Computational Statistics and Machine Learning Seminars, Oxford, 2019: Learning Representations to Accelerate Hyperparameter Tuning.
Machine Learning module of the (OxWaSP) Centre for Doctoral Training, Oxford, 2019: Bayesian Optimisation and Variational Inference.
DALI 2018 workshop on Goals and Principles of Representation Learning, Lanzarote, 2018: Learning Reperesentations for Hyperparameter Transfer Learning.
Congrès MATh.en.Jeans, Potsdam, 2018: L'Apprentissage Statistiqueet son Application en Industrie.
Machine Learning module of the (OxWaSP) Centre for Doctoral Training, Oxford, 2018: Bayesian Optimisation and Variational Inference.
NIPS workshop on Advances in Approximate Bayesian Inference (AABI), Long Beach, 2017: Approximate Bayesian Inference in Industry: Two Applications at Amazon.
Machine Learning Tutorial at Imperial College, London, 2017: Bayesian Optimisation.
Data Science Summer School (DS3), Paris, 2017: Tutorial on Bayesian Optimisation; Amazon: A Playground for Machine Learning.
Machine Learning Summer School (MLSS 2016, Arequipa): Bayesian Optimisation.
Peyresq Summer School in Signal and Image Processing '16: Classification and Clustering.
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.
Gaussian Process Approximations (GPA) workshop, Berlin, Germany, 2017.
NIPS workshop on Learning Semantics, Montreal, Canada, 2014.
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.
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,
WARNING! Material on this web
site is presented to ensure timely dissemination of technical work.
Copyright and all rights therein are retained by authors or by other
copyright holders, notwithstanding that they have offered their works
here electronically. All persons copying this information are expected
to adhere to the terms and constraints invoked by each author's
copyright. These works may not be reposted without the explicit
permission of the copyright holder. Copyright holders claiming that the
material available above is not in accordance with copyright terms and
constraints are invited to contact the author by e-mail and ask him to
remove the links to specific manuscripts.