Cédric Archambeau

Director of Artificial Intelligence
Helsing.ai
cedric,archambeau#helsing,ai

Fellow, Robust Machine Learning Program
European Laboratory for Learning and Intelligent Systems (ELLIS)
Berlin Unit

Associate Member
Department of Statistics
University of Oxford, United Kingdom

Formerly Honorary Senior Research Associate
Centre for Computational Statistics and Machine Learning
University College London, United Kingdom

[Google Scholar] [Semantic Scholar] [DBLP] [LinkedIn]

Follow me on Twitter: @cedapprox
Follow me on Mastodon: @cedapprox@sigmoid.social



About me

Currently Director of Artificial Intelligence at Helsing, I was a Principal Applied Scientist at Amazon and the Chief Scientist for Amazon SageMaker until June 2023. Since 2017, I am an associate member of the department of Statistics at the University of Oxford. I was elected Fellow in the Robust Machine Learning Program of the European Lab for Learning and Intelligent Systems in 2018. I am Action Editor of the Transactions on Machine Learning Research, a reviewer of the Journal on Machine Learning Research, and regularly serve as Area Chair of top tier conferences in machine learning, such as NeurIPS, ICML, and ICLR.

I received the Electrical Engineering degree and the PhD in Applied Sciences from UCLouvain, respectively in 2001 and 2005, where I was 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, in which I demonstrated weaknesses 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 at the TU Berlin on problems in data assimilation and approximate Bayesian inference. 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.

I joined Xerox Research Centre Europe (now Naver Labs Europe) in October 2009, where I led the Machine Learning group. I conducted applied research in machine learning, natural language understanding, and mechanism design, with applications in customer care, transportation, and governmental services. I joined Amazon, Berlin, in October 2013 to develop zero-parameter machine learning algorithms. As an Principal Applied Scientist at Amazon Web Services (AWS), I oversaw the product-related science powering Amazon SageMaker and led long-term science initiatives in the area of automated machine learning, continual learning, and responsible AI. My work at AWS lay the foundation of Amazon SageMaker Automatic Model Tuning, Amazon SageMaker Autopilot, Amazon SageMaker Clarify, and AWS Clean Rooms.



Open source libraries:

Syne Tune, a library for asynchronous hyperparameter and neural architecture optimization. Our goal is to make machine learning more reproducible by covering a broad range of optimisers, offering multi-fidelity and multi-objective algorithms, and making it easy to run experiments on the cloud. We just released a new version with better documentation!

Renate, a continual learning library to automatically retrain and retune deep neural networks!

Fortuna, a library for uncertainty quantification to help deploy deep learning in a more responsibly and safely!



Lectures and selected presentations

Machine Learning module of the StatML Centre for Doctoral Training, Oxford, 2024: Bayesian Optimization and A Primer on Foundation Models.

ELLIS Robust ML workshop, Helsinki, 2024: Explaining Probabilistic Models with Distributional Values.

Machine Learning module of the StatML Centre for Doctoral Training, Oxford, 2022: Algorithms for Automated Hyperparameter and Neural Architecture Optimization.

Department of Statistics, University of Oxford, 2022: Open (Practical) Problems in Machine Learning Automation.

Machine Learning module of the StatML Centre for Doctoral Training, Oxford, 2021: Algorithms for Automated Hyperparameter and Neural Architecture Optimization and Variational Inference.

Mini-symposium on Bayesian Methods in Science and Engineering at the SIAM Conference on Computational Science and Engineering: Bayesian Optimization by Density-Ratio Estimation. Virtual, 2021.

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 Statistique et son Application en Industrie.

Machine Learning module of the OxWaSP Centre for Doctoral Training, Oxford, 2018: Bayesian Optimisation and Variational Inference.

NeurIPS 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 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 and seminars

ELLIS AutoML seminars. This is a virtual seminar series; everyone is welcome to join!

Gaussian Process Approximations (GPA) workshop, Berlin, Germany, 2017.

NeurIPS workshop on Learning Semantics, Montreal, Canada, 2014.

NeurIPS 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.

NeurIPS workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, Whistler, Canada, 2006.


Service to the community

I am Action Editor of the Transactions on Machine Learning Research, a new venue for dissemination of machine learning research that is intended to complement the Journal of Machine Learning Research. I also served as the Tutorials Chair of ECML-PKDD '09 and the Industry Track Chair for ECML-PKDD '12. I was a reserve member of the High-level Expert Group on Artificial Intelligence for the European Commission.

Conference area chair: NeurIPS '11, NeurIPS '13, AISTATS '14, AISTATS '15, ICML '15, NeurIPS '17, ICML '18, NeurIPS '18, IJCAI '19, NeurIPS '19, AISTATS '20, IJCAI '20, NeurIPS '20, ICML '21, ICLR '21, NeurIPS '21, NeurIPS '22, ICLR '22, AISTATS '23, ICML '23, NeurIPS '23, ICLR '24, and ICML '24.

Journal reviewer: Journal of Machine Learning Research, Neural Networks, IEEE transactions on Pattern Analysis and Machine Intelligence, IEEE transactions on Neural Networks and Learning Systems, IEEE transactions on Signal Processing, IEEE transactions on Image Processing, Neuroimage, Neurocomputing, and Pattern Recognition.



Publications

Structural Pruning of Pre-trained Language Models via Neural Architecture Search.
A. Klein, J. Golebiowski, X. Ma, V. Perrone, C.Archambeau
Technical report, 2024.

Explaining Probabilistic Models with Distributional Values.
L. Franceschi, M. Donini, C. Archambeau, M. Seeger
Accepted at the International Conference on Machine Learning (ICML), 2024.

On the Choice of Learning Rate for Local SGD.
L. Balles, P. Teja S, C.Archambeau
Transactions on Machine Learning Research (TMLR), 2024.

A Negative Result on Gradient Matching for Selective Backprop.
L. Balles, C. Archambeau, G. Zappella
NeurIPS workshop on Failure Modes in the Age of Foundation Models, 2023.

Optimizing Hyperparameters with Conformal Quantile Regression.
D. Salinas, J. Golebiowski, A. Klein, M. Seeger, C. Archambeau
International Conference on Machine Learning (ICML), 2023.

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography.
L. Franceschi, C. Zor, M. B. Zafar, G. Detommaso, C. Archambeau, T. Madl, M. Donini, M. Seeger
International Workshop on Trustworthy Machine Learning for Healthcare (TML4H) at ICLR, 2023.

Renate: A Library for Real-world Continual Learning.
M. Wistuba, M. Ferianc, L. Balles, C. Archambeau, G. Zappella
Technical report, 2023.

Fortuna: A Library for Uncertainty Quantification in Deep Learning.
G. Detommaso, A. Gasparin, M. Donini, M. Seeger, A. G. Wilson, C. Archambeau
Journal of Machine Learning Research, Open Source Software Track, 2023.

Geographical Erasure in Language Generation.
P. Schwobel, J. Golebiowski, M. Donini, C. Archambeau, D. Pruthi
Findings of the Association for Computational Linguistics: EMNLP 2023.

PASHA: Efficient HPO and NAS with Progressive Resource Allocation.
O. Bohdal, L. Balles, M. Wistuba, B. Ermis, C. Archambeau, G. Zappella
International Conference on Representation Learning (ICLR), 2023.

Hyperparameter Optimization.
A. Klein, M. Seeger, C. Archambeau
In Dive Into Deep Learning, vol. 2 (Chapter 19), 2022.

Differentially private gradient boosting on linear learners for tabular data analysis.
S. Rho, C. Archambeau, S. Aydore, B. Ermis, M. Kearns, A. Roth, S. Tang, Y.-X. Wang, S. Wu
NeurIPS workshop on Trustworthy and Socially Responsible Machine Learning, 2022.

Memory Efficient Continual Learning with Transformers.
B. Ermis, G. Zappella, M. Wistuba, A. Rawal, C. Archambeau
Annual Conference on Advances in Neural Information Processing Systems (NeurIPS), 2022.

Private Synthetic Data for Multitask Learning and Marginal Queries.
G. Vietri, C. Archambeau, S. Aydore, W. Brown, M. Kearns, A. Roth, A. Siva, S. Tang, S. Wu
Annual Conference on Advances in Neural Information Processing Systems (NeurIPS), 2022.

Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors.
G. Detommaso, A. Gasparin, A. Wilson, C. Archambeau
Technical report, 2022.

Automatic Termination for Hyperparameter Optimization.
A. Makarova, H. Shen, V. Perrone, A. Klein, J. B. Faddoul, A. Krause, M. Seeger, C. Archambeau
Conference on Automated Machine Learning (Main Track), 2022. (best paper award)

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. [GitHub]
D. Salinas, M. Seeger, A. Klein, V. Perrone, M. Wistuba, C. Archambeau
Conference on Automated Machine Learning (Main Track), 2022.

PASHA: Efficient HPO with Progressive Resource Allocation. [Code]
O. Bohdal, L. Balles, B. Ermis, C. Archambeau, G. Zappella
Conference on Automated Machine Learning (Late-Breaking Workshop Track), 2022.

Gradient-Matching Coresets for Rehearsal-Based Continual Learning.
L. Balles, G. Zappella, C. Archambeau
Technical report, 2022.

Continual Learning with Transformers for Image Classification.
B. Ermis, G. Zappella, M. Wistuba, A. Rawal, C. Archambeau
CVPR workshop on Continual Learning in Computer Vision, 2022.

Memory-efficient Continual Learning for Neural Text Classification.
B. Ermis, G. Zappella, M. Wistuba, C. Archambeau
Technical report, 2022.

Diverse Counterfactual Explanations for Anomaly Detection in Time Series.
D. Sulem, M. Donini, M. B. Zafar, F. X. Aubet, J. Gasthaus, T. Januschowski, S. Das, K. Kenthapadi, C. Archambeau
Technical report, 2021.

Gradient-matching Coresets for Continual Learning.
L. Balles, G. Zappella, C. Archambeau
NeurIPS workshop on Distribution Shifts: Connecting Methods and Applications, 2021.

Meta-Forecasting by Combining Global Deep Representations with Local Adaptation.
R. Grazzi, V. Flunkert, D. Salinas, T. Januschowski, M. Seeger, C. Archambeau
Technical report, 2021.

Multi-objective Asynchronous Successive Halving.
R. Schmucker, M. Donini, M. B. Zafar, D. Salinas, C. Archambeau
Technical report, 2021.

On the Lack of Robustness of Deep Neural Text Classifiers.
M. B. Zafar, M. Donini, D. Slack, C. Archambeau, S. Das, K. Kenthapadi
Annual Meeting of the Association for Computational Linguistics (ACL), 2021. Findings.

Towards Robust Episodic Meta-Learning.
B. Ermis, G. Zappella, C. Archambeau
Uncertainty in Artificial Intelligence (UAI), 2021.

BORE: Bayesian Optimization by Density-Ratio Estimation.
L. Tiao, A. Klein, M. Seeger, E. Bonilla, C. Archambeau, F. Ramos
International Conference on Machine Learning (ICML), 2021. (long presentation)

Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio.
J. Siems, A. Klein, C. Archambeau, M. Mahsereci
ICML AutoML workshop, 2021.

A Resource-efficient Method for Repeated HPO and NAS Problems.
G. Zappella, D. Salinas, C. Archambeau
ICML AutoML workshop, 2021.

A Multi-objective Perspective on Jointly Tuning Hardware and Hyperparameters.
D. Salinas, V. Perrone, O. Cruchant, C. Archambeau
ICLR NAS workshop, 2021.

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution.
A. Makarova, H. Shen, V. Perrone, A. Kelin, J.B. Faddoul, A. Krause, M. Seeger, C. Archambeau
ICLR NAS workshop, 2021.

Amazon SageMaker Automatic Model Tuning: Black-box Optimization at Scale.
V. Perrone, H. Shen, A. Zolic, I. Shcherbatyi, A. Ahmed, T. Bansal, M. Donini, F. Winkelmolen, R. Jenatton, J. B. Faddoul, B. Pogorzelska, M. Miladinovic, K. Kenthapadi, M. Seeger, C. Archambeau
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. Industry track.

Fair Bayesian Optimization.
V. Perrone, M. Donini, B. Zafar, K. Kenthapadi, C. Archambeau
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021.

Hyperparameter Transfer Learning with Adaptive Complexity.
S. Horvath, A. Klein, C. Archambeau
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

Bayesian Optimization by Density Ratio Estimation.
L. Tiao, A. Klein, C. Archambeau, E. Bonilla, F. Ramos, M. Seeger
NeurIPS workshop on Meta-learning, December 2020. (selected for oral presentation)

Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization.
G. Guinet, V. Perrone, C. Archambeau
NeurIPS workshop on Meta-learning, 2020.

Multi-Objective Multi-Fidelity Hyperparameter Optimization with application to Fairness.
R. Schmucker, M. Donini, V. Perrone, B. Zafar, C. Archambeau
NeurIPS workshop on Meta-learning, 2020.

Model-based Asynchronous Hyperparameter and Neural Architecture Search.
L. C. Tiao, A. Klein, T. Lienart, C. Archambeau, M. Seeger
Technical report, 2020.

LEEP: A New Measure to Evaluate Transferability of Learned Representations.
C. V. Nguyen, T. Hassner, M. Seeger, C. Archambeau
International Conference on Machine Learning (ICML), 2020.

Bayesian Optimization with Fairness Constraints.
V. Perrone, M. Donini, K. Kenthapadi, C. Archambeau
ICML workshop on AutoML, 2020. (best paper award)

Cost-aware Bayesian Optimization.
E. Hans Lee, V. Perrone, C. Archambeau, M. Seeger
ICML workshop on AutoML, 2020.

Constrained Bayesian Optimization with Max-Value Entropy Search.
V. Perrone, I.Shcherbatyi, R.Jenatton, C.Archambeau, M.Seeger
NeurIPS workshop on Meta-learning, 2019.

Learning Search Spaces for Bayesian Optimization: Another View of Hyperparameter Transfer Learning.
V. Perrone, H. Shen, M. Seeger, C. Archambeau, R. Jenatton
Annual Conference on Advances in Neural Information Processing Systems (NeurIPS), 2019.

Scalable Hyperparameter Transfer Learning.
V. Perrone, R. Jenatton, M. Seeger, C. Archambeau
Annual Conference on Advances in Neural Information Processing Systems (NeurIPS), 2018.

A Simple Transfer Learning Extension of Hyperband.
L. Valkov, R. Jenatton, F. Winkelmolen, C. Archambeau
NeurIPS workshop on Meta-Learning, 2018.

Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start.
V. Perrone, R. Jenatton, M. Seeger, C. Archambeau
NeurIPS workshop on Meta-Learning, 2017.

An interpretable latent variable model for attribute applicability in the Amazon catalogue.
T. Rukat, D. Lange, C. Archambeau
NeurIPS Symposium on Interpretable Machine Learning, 2017.

Bayesian Optimization with Tree-structured Dependencies.
R. Jenatton, C. Archambeau, J. Gonzalez, M. Seeger
International Conference on Machine Learning (ICML), 2017.

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

Online Dual Decomposition for Performance and Delivery-based Distributed Ad Allocation.
J. Huang, R. Jenatton, C. Archambeau
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 117-126, 2016.

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

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

Online Inference for Relation Extraction with a Reduced Feature Set.
M. Rabinovich, C. Archambeau
Technical report, 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
NeurIPS 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 (NeurIPS) 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
NeurIPS 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 (NeurIPS) 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 (NeurIPS) 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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