My research is in statistical machine learning, which aims at (automatically) extracting and processing information from raw data. Machine learning is closely related to artificial intelligence and has a large overlap with statistics. My current work address fundamental problems in data assimilation, structured prediction and personalised content creation. A natural framework for dealing with these problems are stochastic process models and approximate Bayesian inference, the main challenge being to find efficient ways to deal with today's exponential increase of (freely) available digital data under uncertainty. I am also interested in large-scale inference in natural language processing, relational learning, collaborative filtering and computational social science applications. 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 both 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 and PinView, 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 am also an active participant of the PASCAL network of excellence. In October 2009, I joined the Services Innovation Laboratory at Xerox Research Centre Europe, where I am leading the Machine Learning for Services group. The team conducts applied research in computational statistics and mechanism design. We are currently participating in the European projects FUPOL [video] and Fusepool. If you are interested in an internship, phd or post-doc at Xerox Research Centre Europe don't hesitate to get in touch!
Workshops and Teaching Industry Track chair ECML-PKDD 2012. NIPS'11 workshop on Choice Models and Preference Learning. MSc in Machine Learning '11 (Applied Machine Learning): Machine Learning at Xerox -- From statistical machine translation to large-scale image search. Tutorial on Probabilistic Graphical Models at PASACAL Bootcamp 2010: slides and videolecture (2 parts). Workshop on Automated Knowledge Base Construction, Grenoble, France, 2010. Tutorials chair ECML-PKDD 2009. PASCAL2 workshop on Approximate Inference in Stochastic Processes and Dynamical Systems. MSc in Intelligent Systems '08: Advanced Topics in Machine Learning. CSML'07 reading group on Stochastic Differential Equations. NIPS'06 workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference.
Computational Statistics and Machine Learning Papers Plackett-Luce regression: a new Bayesian model for polychotomous data C. Archambeau, F. Caron Uncertainty in Artificial Intelligence (UAI) 28, 2012. Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions Y. Shen, D. Cornford, M. Opper, C. Archambeau Computational Statistics 27:1, 149-176, 2012. Latent IBP compound Dirichlet allocation C. Archambeau, B. Lakshimanarayan, G. Bouchard NIPS 24 workshop on Bayesian nonparametrics: Hope or Hype?, 2011. Sparse Bayesian multi-task learning C. Archambeau, S. Guo., O. Zoeter Neural Information Processing Systems (NIPS) 24, 2011. Robust Bayesian Matrix Factorisation B. Lakshimanarayan, G. Bouchard, C. Archambeau Artificial Intelligence and Statistics (AISTATS) 14. JMLR Workshop and Conference Proceedings 15:425-433, 2011. Approximate Inference for continuous-time Markov processes C. Archambeau, M. Opper In D. Barber, A. T. Cemgil, and S. Chiappa, Inference and Learning in Dynamic Models. Cambridge University Press, 2011. The Sequence Memoizer F. Wood, J. Gasthaus, C. Archambeau, L. James, Y. W. Teh Communications of the ACM, 54(2):91-98, 2011. Multiple Gaussian process models [videolecture] C. Archambeau, F. Bach NIPS 23 workshop on New Directions in Multiple Kernel Learning, 2010. [arXiv] A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems Y. Shen, C. Archambeau, D. Cornford, M. Opper, J. Shawe-Taylor, R. Barillec Journal of Signal Processing Systems, 61(1):51-59, 2010. Stochastic Memoizer for Sequence Data F. Wood, C. Archambeau, J. Gasthaus, L. James, Y. W. Teh In L. Bottou and M. Littman, Proceedings of the 26th International Conference on Machine Learning (ICML), Montreal (Quebec), Canada, June 14-18, 2009, pp. 1129-1136. ACM. The Variational Gaussian Approximation Revisited M. Opper, C. Archambeau Neural Computation 21(3):786-792, 2009. Sparse Probabilistic Projections C. Archambeau, F. Bach In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou (Eds.), Neural Information Processing Systems (NIPS) 21, pp.17-24, 2009. The MIT Press. Mixtures of Robust Probabilistic Principal Component Analyzers C. Archambeau, N. Delannay, M. Verleysen Neurocomputing, 71(7-9):1274-1282, 2008. Elsevier. Improving the robustness to outliers of mixtures of probabilistic PCAs N. Delannay, C. Archambeau, M. Verleysen In T. Wahio, et al. (Eds.), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 12, Lecture notes in Artificial Intelligence (LNAI) 5012:527-535, 2008. Springer. Variational Inference for Diffusion Processes C. Archambeau, M. Opper, Y. Shen, D. Cornford, J. Shawe-Taylor In C. Platt, D. Koller, Y. Singer and S. Roweis (Eds.), Neural Information Processing Systems (NIPS) 20, pp.17-24, 2008. The MIT Press. Evaluation of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems Y. Shen, C. Archambeau, D. Cornford, M. Opper, J. Shawe-Taylor, R. Barillec Proceedings of the 17th IEEE workshop on Machine Learning for Signal Processing (MLSP), Thessaloniki, Greece, 27-28 August, 2007, pp. 306-311. Gaussian Process Approximations of Stochastic Differential Equations C. Archambeau, D. Cornford, M. Opper, J. Shawe-Taylor Journal of Machine Learning Research Workshop and Conference Proceedings, 1:1-16, 2007. Mixtures of Robust Probabilistic Principal Component Analyzers C. Archambeau, N. Delannay, M. Verleysen Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 25-27, 2007, pp. 229-234. D-side. Robust Bayesian Clustering C. Archambeau and M. Verleysen Neural Networks, 20:129-138, 2007. Elsevier. Automatic Adjustment of Discriminant Adaptive Nearest Neighbor N. Delannay, C. Archambeau, M. Verleysen In Y.Y. Tang, P. Wang, G. Lorette and D.S. Yeung (Eds.), Proceedings of the 18th International Conference on Pattern Recognition (ICPR), Hong Kong, P.R.C., 20-24 August, 2006, vol. 2, pp. 525-555. IEEE Computer Society. Robust Probabilistic Projections C. Archambeau, N. Delannay, M. Verleysen In W. W. Cohen and A. Moore (Eds.), Proceedings of the 23rd International Conference on Machine Learning (ICML), Pittsburgh (PA), U.S.A., 25-29 June, 2006, pp. 33-40. ACM. There are a couple of typos in the Appendix. Here's the corrected version: . Manifold Constrained Finite Gaussian Mixtures C. Archambeau and M. Verleysen In J. Cabestany, A. Prieto and F. Sandoval Hernández (Eds.), Computational Intelligence and Bioinspired Systems - 8th International Work-Conference on Artificial Neural Networks (IWANN), Vilanova i la Geltrú (Barcelona), Spain, June 8-10, 2005. Lecture Notes in Computer Science, vol. 3512, pp.820-828. Springer. Supervised Nonparametric Information Theoretic Classification C. Archambeau, T. Butz, V. Popovici, M. Verleysen, J.-P. Thiran In J. Kittler, M. Petrou and M. Nixon (Eds.), Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, U.K., August 23-26, 2004, vol. 3, pp. 414-417. IEEE Computer Society. Flexible and Robust Bayesian Classification by Finite Mixture Models C. Archambeau, F. Vrins, M. Verleysen Proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 28-30, 2004, pp. 75-80. D-side. Towards a Local Separation Performances Estimator using Common ICA contrast Funtions? F. Vrins, C. Archambeau, M. Verleysen Proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 28-30, 2004, pp. 211-216. D-side. Entropy Minima and Distribution Structural Modifications in Blind Separation of Multi-model Sources F. Vrins, C. Archambeau, M. Verleysen In R. Fisher, R. Preuss and U. von Toussaint, Proceedings of the 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt), IPP Garching bei München, Germany, July 25-30, 2004, pp. 589-596. American Institute of Physics (AIP). On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures C. Archambeau, J.A. Lee, M. Verleysen Proceedings of the 11th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 23-25, 2003, pp. 99-106. D-side. Locally Linear Embedding versus Isotop J.A. Lee, C. Archambeau, M. Verleysen Proceedings of the 11th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 23-25, 2003, pp. 527-534. Width Optimization of the Gaussian Kernels in Radial Basis Function Networks N. Benoudjit, C. Archambeau, A. Lendasse, J.A. Lee, M. Verleysen Proceedings of the 10th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 24-26, 2002, pp. 425-432.
Application Papers Connecting Comments and Tags: Improved Modeling of Social Tagging Systems. D. Yin, S. Guo, B. Davison, C. Archambeau, G. Bouchard 6 th ACM Conference on Web Search and Data Mining (WSDM), 2013. Mail2Wiki: low-cost sharing and early curation from email to wikis. B. V. Hanrahan, G. Bouchard, G. Convertino, T. Weksteen, N. Kong, C. Archambeau, E. H. Chi International Conference on Communities and Technologies (C&T) 5, 2011. Mail2Wiki: posting and curating Wiki content from email [demo] B. V. Hanrahan, T. Weksteen, N. Kong, G. Convertino, G. Bouchard, C. Archambeau, E. H. Chi International Conference on Intelligent User Interfaces (IUI), pp 441-442, 2011. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods S. Lise, C. Archambeau, M. Pontil, D. Jones BMC Bioinformatics, 10: 365-382, 2009. Switching Regulatory Models of Cellular Stress Response G. Sanguinetti, A. Ruttor, M. Opper, C. Archambeau Bioinformatics, 25(10): 1280-1286, 2009. Oxford University Press. Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages F.-X. Standaert, C. Archambeau In E. Oswald and P. Rohatgi (Eds.), 10th International Workshop on Cryptographic Hardware and Embedded Systems (CHES), Washington, DC, USA, 10-13 August, 2008. Lecture Notes in Computer Science vol. 5154, pp. 411-425. Springer. Template Attacks in Principal Subspaces C. Archambeau, E. Peeters, F.-X. Standaert, J.-J. Quisquater In L. Goubin and M. Matsui (Eds.), 8th International Workshop on Cryptographic Hardware and Embedded Systems (CHES), Yokohama, Japan, 10-13 October, 2006. Lecture Notes in Computer Science vol. 4249, pp. 1-14. Springer. Towards Security Limits of Side-Channel Attacks F.-X. Standaert, E. Peeters, C. Archambeau, J.-J. Quisquater In L. Goubin and M. Matsui (Eds.), 8th International Workshop on Cryptographic Hardware and Embedded Systems (CHES), Yokohama, Japan, 10-13 October, 2006. Lecture Notes in Computer Science vol. 4249, pp. 30-45. Springer. Local Vector-based Models for Sense Discrimination M.-C. de Marneffe, C. Archambeau, P. Dupont, M. Verleysen In H. Bunt, J. Geertzen and E. Thijsse (Eds.), Proceedings of the 6th International Workshop on Computational Semantics (IWCS), Tilburg, the Netherlands, January 12-14, 2005, pp. 163-174. Prediction of Visual Perceptions with Artificial Neural Networks in a Visual Prosthesis for the Blind C. Archambeau, J. Delbeke, C. Veraart, M. Verleysen Artificial Intelligence in Medicine, 32(3):183-194, 2004. Elsevier. Classification of Visual Sensations Generated Electrically in the Visual Field of the Blind C. Archambeau, J. Delbeke, M. Verleysen In D. D. Feng and E. R. Carson (Eds.), Proceedings of the 5th IFAC Symposium on Modelling and Control in Biomedical Systems, Melbourne, Australia, August 21-23, 2003, pp. 223-228. Elsevier. Phosphene Evaluation in a Visual Prosthesis with Artificial Neural Networks C. Archambeau, A. Lendasse, C. Trullemans, C. Veraart, J. Delbeke, M. Verleysen Proceedings of the 1st European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems (EUNITE), Puerto de la Cruz (Tenerife), Spain, December 13-14, 2001, pp. 509-515. Also published in G. D. Dounias and D. A. Linkens (Eds.), Adaptive Systems and Hybrid Computational Intelligence in Medicine, 2001, pp. 116-122. University of the Aegean.
Thesis Probabilistic Models in Noisy Environments - And their Application to a Visual Prosthesis for the Blind C. Archambeau Doctoral dissertation, Université catholique de Louvain, Louvain-la-Neuve, Belgium, September, 2005.
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.