Cedric Archambeau

Cédric Archambeau

Honorary Senior Research Associate
Centre for Computational Statistics and Machine Learning
Department of Statistical Science and Department of Computer Science
University College London, United Kingdom
C,Archambeau#cs,ucl,ac,uk

Senior Research Scientist
Machine Learning Science
Amazon, Berlin, Germany
cedrica#amazon,com

[Google Scholar] [DBLP]

My research is in probabilistic machine learning. Machine learning plays a key role in artificial intelligence and data science, and it has a large overlap with statistics. The Bayesian paradigm provides a natural framework for learning from data under uncertainty and to automate decision-making. My recent work aims to democratise machine learning by scaling up (approximate) inference algorithms to handle massive data sets and by automating the process of tuning learning parameters through Bayesian optimisation. I am also interested in learning data representations and, more generally, machine reasoning.

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 excellence.

In October 2009, I joined Xerox Research Centre Europe, where I led the Machine Learning group. The team conducted applied research in machine learning, computational statistics and mechanism design, with applications in customer care, transportation and governmental services. I am now a Senior Research Scientist with Machine Learning Science at Amazon, Berlin, where I lead the algorithms group. Recently, I served as an Area Chair for NIPS '11, NIPS '13, AISTATS '14, AISTATS '15 and ICML '15. I was Tutorials Chair at ECML-PKDD '09 and Industry Track Chair for ECML-PKDD '12, and taught a lecture on Bayesian optimisation at MLSS 2016.



Teaching

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 2010: videolecture (2 parts).

MSc in Intelligent Systems '08 at UCL: Advanced Topics in Machine Learning.

CSML'07 reading group on Stochastic Differential Equations.


Workshops

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.



Publications

Online Optimization and Regret Guarantees for Non-additive Long-term Constraints.
R. Jenatton, J. Huang,C. Archambeau
Technical report, February 2016.

Online Dual Decomposition for Performance and Delivery-based Distributed Ad Allocation.
J. Huang, R. Jenatton,C. Archambeau
Accepted at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Applied Data Science Track), 2016.

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints.
R. Jenatton, J. Huang,C. Archambeau
Accepted at the International Conference on Machine Learning (ICML), 2016.

Incremental Variational Inference applied to Latent Dirichlet Allocation. [slides]
C. Archambeau, B. Ermis
NIPS workshop on Advances in Approximate Bayesian Inference, December 2015.

One-Pass Ranking Models for Low-Latency Product Recommendations.
A. Freno, M. Saveski, R. Jenatton, C. Archambeau
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1789-1798, 2015.

Incremental Variational Inference for Latent Dirichlet Allocation.
C. Archambeau, B. Ermis
Technical report, July 2015.

Online Inference for Relation Extraction with a Reduced Feature Set.
M. Rabinovich, C. Archambeau
Technical report, April 2015.

Latent IBP compound Dirichlet Allocation.
C. Archambeau, B. Lakshminarayan, G. Bouchard
IEEE transactions in Pattern Analysis and Machine Intelligence (PAMI) 37(2):321-333, 2015.

Overlapping Trace Norms in Multi-View Learning.
B. Behmardi, C. Archambeau, G. Bouchard
Technical report, April 2014.

Towards Crowd-based Customer Service: A Mixed-Initiative Tool for Managing Q&A Sites.
T. Piccardi, G. Convertino, M. Zancanaro, J.Wang, C. Archambeau
Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), pp. 2725-2734, 2014.

Log-linear Language Models based on Structured Sparsity.
A. Nelakanti, C. Archambeau, J. Mairal, F. Bach, G. Bouchard
Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 233-243, 2013.

Bringing Representativeness into Social Media Monitoring and Analysis.
M. Kaschesky, P. Sobkowicz, J. M. Hernández-Lobato, G. Bouchard, C. Archambeau, N. Scharioth, R. Manchin, A. Gschwend, R. Riedl
46th Hawaii International Conference on System Sciences (HICSS), pp. 2003-2012, 2013

Error Prediction with Partial Feedback.
W. Darling, C. Archambeau, S. Mirkin, G. Bouchard
In H. Blockeel, K. Kersting, S. Nijssen, F. Zelezny (Eds.), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Lecture Notes in Computer Science (LNCS), 8189:80-94, 2013.

Connecting Comments and Tags: Improved Modeling of Social Tagging Systems.
D. Yin, S. Guo, B. Davison, C. Archambeau, G. Bouchard
In S. Leonardi, A. Panconesi, P. Ferragina, A. Gionis (Eds.), 6th ACM Conference on Web Search and Data Mining (WSDM), pp. 547-556, 2013.

Plackett-Luce regression: a new Bayesian model for polychotomous data
C. Archambeau, F. Caron
In N. de Freitas, K. P. Murphy (Eds.), Uncertainty in Artificial Intelligence (UAI) 28, pp. 84-92, 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
In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. C. N. Pereira, K. Q. Weinberger (Eds.), Neural Information Processing Systems (NIPS) 24, pp. 1755-1763, 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.

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
In M. Foth, J. Kjeldskov, J. Paay (Eds.), Proceedings of the International Conference on Communities and Technologies (C&T) 5, pp. 98-107, 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
In P. Pu, M. J. Pazzani, E. Andre, D. Riecken (Eds.), Proceedings of the International Conference on Intelligent User Interfaces (IUI), pp 441-442, 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.

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.

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.

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.

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: .

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.

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.

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.

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).

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.

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.

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.

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.

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.

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