Cédric ArchambeauHonorary Senior Research AssociateCentre 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 ScientistMachine 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 at ECML-PKDD '12.

**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.

**Teaching**

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.

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. Lakshimanarayan, 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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