Width optimization of the Gaussian kernels in Radial Basis Function Networks. - Nabil Benoudjit; Cédric Archambeau; Amaury Lendasse; John Aldo Lee; Michel Verleysen (ESANN, 2002)

On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures. - Cédric Archambeau; John Aldo Lee; Michel Verleysen (ESANN, 2003)

Locally Linear Embedding versus Isotop. - John Aldo Lee; Cédric Archambeau; Michel Verleysen (ESANN, 2003)

Flexible and Robust Bayesian Classification by Finite Mixture Models. - Cédric Archambeau; Frédéric Vrins; Michel Verleysen (ESANN, 2004)

Towards a Local Separation Performances Estimator Using Common ICA Contrast Functions? - Frédéric Vrins; Cédric Archambeau; Michel Verleysen (ESANN, 2004)

Supervised Nonparametric Information Theoretic Classification. - Cédric Archambeau; Torsten Butz; Vlad Popovici; Michel Verleysen; Jean-Philippe Thiran (ICPR (3), 2004)

Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind. - Cédric Archambeau; Jean Delbeke; Claude Veraart; Michel Verleysen (Artif. Intell. Medicine, 2004)

Manifold Constrained Variational Mixtures. - Cédric Archambeau; Michel Verleysen (ICANN (2), 2005)

Manifold Constrained Finite Gaussian Mixtures. - Cédric Archambeau; Michel Verleysen (IWANN, 2005)

Probabilistic models in noisy environments : and their application to a visual prosthesis for the blind/. - Cédric Archambeau (2005)

Template Attacks in Principal Subspaces. - Cédric Archambeau; Eric Peeters; François-Xavier Standaert; Jean-Jacques Quisquater (CHES, 2006)

Robust probabilistic projections. - Cédric Archambeau; Nicolas Delannay; Michel Verleysen (ICML, 2006)

Automatic Adjustment of Discriminant Adaptive Nearest Neighbor. - Nicolas Delannay; Cédric Archambeau; Michel Verleysen (ICPR (2), 2006)

Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures. - Cédric Archambeau; Maurizio Valle; Alex Assenza; Michel Verleysen (ISCAS, 2006)

Variational Inference for Diffusion Processes. - Cédric Archambeau; Manfred Opper; Yuan Shen 001; Dan Cornford; John Shawe-Taylor (NIPS, 2007)

Towards Security Limits in Side-Channel Attacks. - François-Xavier Standaert; Eric Peeters; Cédric Archambeau; Jean-Jacques Quisquater (IACR Cryptol. ePrint Arch., 2007)

Gaussian Process Approximations of Stochastic Differential Equations. - Cédric Archambeau; Dan Cornford; Manfred Opper; John Shawe-Taylor (Gaussian Processes in Practice, 2007)

Robust Bayesian clustering. - Cédric Archambeau; Michel Verleysen (Neural Networks, 2007)

Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages. - François-Xavier Standaert; Cédric Archambeau (CHES, 2008)

Sparse probabilistic projections. - Cédric Archambeau; Francis R. Bach (NIPS, 2008)

Improving the Robustness to Outliers of Mixtures of Probabilistic PCAs. - Nicolas Delannay; Cédric Archambeau; Michel Verleysen (PAKDD, 2008)

Mixtures of robust probabilistic principal component analyzers. - Cédric Archambeau; Nicolas Delannay; Michel Verleysen (Neurocomputing, 2008)

A stochastic memoizer for sequence data. - Frank D. Wood; Cédric Archambeau; Jan Gasthaus; Lancelot James; Yee Whye Teh (ICML, 2009)

Switching regulatory models of cellular stress response. - Guido Sanguinetti; Andreas Ruttor; Manfred Opper; Cédric Archambeau (Bioinform., 2009)

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. - Stefano Lise; Cédric Archambeau; Massimiliano Pontil; David T. Jones (BMC Bioinform., 2009)

The Variational Gaussian Approximation Revisited. - Manfred Opper; Cédric Archambeau (Neural Comput., 2009)

A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. - Yuan Shen 001; Cédric Archambeau; Dan Cornford; Manfred Opper; John Shawe-Taylor; Remi Louis Barillec (J. Signal Process. Syst., 2010)

Mail2Wiki: low-cost sharing and early curation from email to wikis. - Benjamin V. Hanrahan; Guillaume Bouchard; Gregorio Convertino; Thiébaud Weksteen; Nicholas Kong; Cédric Archambeau; Ed H. Chi (C&T, 2011)

Mail2Wiki: posting and curating Wiki content from email. - Benjamin V. Hanrahan; Thiébaud Weksteen; Nicholas Kong; Gregorio Convertino; Guillaume Bouchard; Cédric Archambeau; Ed H. Chi (IUI, 2011)

Sparse Bayesian Multi-Task Learning. - Cédric Archambeau; Shengbo Guo; Onno Zoeter (NIPS, 2011)

The sequence memoizer. - Frank D. Wood; Jan Gasthaus; Cédric Archambeau; Lancelot James; Yee Whye Teh (Commun. ACM, 2011)

Robust Bayesian Matrix Factorisation. - Balaji Lakshminarayanan; Guillaume Bouchard; Cédric Archambeau (AISTATS, 2011)

Plackett-Luce regression: A new Bayesian model for polychotomous data. - Cédric Archambeau; Francois Caron (UAI, 2012)

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. - Yuan Shen 001; Dan Cornford; Manfred Opper; Cédric Archambeau (Comput. Stat., 2012)

Structured Penalties for Log-Linear Language Models. - Anil Kumar Nelakanti; Cédric Archambeau; Julien Mairal; Francis R. Bach; Guillaume Bouchard (EMNLP, 2013)

Bringing Representativeness into Social Media Monitoring and Analysis. - Michael Kaschesky; Pawel Sobkowicz; José Miguel Hernández-Lobato; Guillaume Bouchard; Cédric Archambeau; Nicolas Scharioth; Robert Manchin; Adrian Gschwend; Reinhard Riedl (HICSS, 2013)

Error Prediction with Partial Feedback. - William Darling; Cédric Archambeau; Shachar Mirkin; Guillaume Bouchard (ECML/PKDD (2), 2013)

Connecting comments and tags: improved modeling of social tagging systems. - Dawei Yin; Shengbo Guo; Boris Chidlovskii; Brian D. Davison 001; Cédric Archambeau; Guillaume Bouchard (WSDM, 2013)

Towards crowd-based customer service: a mixed-initiative tool for managing Q&A sites. - Tiziano Piccardi; Gregorio Convertino; Massimo Zancanaro; Ji Wang; Cédric Archambeau (CHI, 2014)

Overlapping Trace Norms in Multi-View Learning. - Behrouz Behmardi; Cédric Archambeau; Guillaume Bouchard (CoRR, 2014)

One-Pass Ranking Models for Low-Latency Product Recommendations. - Antonino Freno; Martin Saveski; Rodolphe Jenatton; Cédric Archambeau (KDD, 2015)

Online Inference for Relation Extraction with a Reduced Feature Set. - Maxim Rabinovich; Cédric Archambeau (CoRR, 2015)

Latent IBP Compound Dirichlet Allocation. - Cédric Archambeau; Balaji Lakshminarayanan; Guillaume Bouchard (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. - Rodolphe Jenatton; Jim C. Huang; Cédric Archambeau (ICML, 2016)

Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation. - Jim C. Huang; Rodolphe Jenatton; Cédric Archambeau (KDD, 2016)

Online optimization and regret guarantees for non-additive long-term constraints. - Rodolphe Jenatton; Jim C. Huang; Cédric Archambeau (CoRR, 2016)

Bayesian Optimization with Tree-structured Dependencies. - Rodolphe Jenatton; Cédric Archambeau; Javier González 002; Matthias W. Seeger (ICML, 2017)

An interpretable latent variable model for attribute applicability in the Amazon catalogue. - Tammo Rukat; Dustin Lange; Cédric Archambeau (CoRR, 2017)

Scalable Hyperparameter Transfer Learning. - Valerio Perrone; Rodolphe Jenatton; Matthias W. Seeger; Cédric Archambeau (NeurIPS, 2018)

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. - Valerio Perrone; Huibin Shen; Matthias W. Seeger; Cédric Archambeau; Rodolphe Jenatton (CoRR, 2019)

Constrained Bayesian Optimization with Max-Value Entropy Search. - Valerio Perrone; Iaroslav Shcherbatyi; Rodolphe Jenatton; Cédric Archambeau; Matthias W. Seeger (CoRR, 2019)

LEEP: A New Measure to Evaluate Transferability of Learned Representations. - Cuong V. Nguyen; Tal Hassner; Matthias W. Seeger; Cédric Archambeau (ICML, 2020)

Amazon SageMaker Autopilot: a white box AutoML solution at scale. - Piali Das; Nikita Ivkin; Tanya Bansal; Laurence Rouesnel; Philip Gautier; Zohar S. Karnin; Leo Dirac; Lakshmi Ramakrishnan; Andre Perunicic; Iaroslav Shcherbatyi; Wilton Wu; Aida Zolic; et al. (DEEM@SIGMOD, 2020)

Model-based Asynchronous Hyperparameter Optimization. - Louis C. Tiao; Aaron Klein; Cédric Archambeau; Matthias W. Seeger (CoRR, 2020)

Cost-aware Bayesian Optimization. - Eric Hans Lee; Valerio Perrone; Cédric Archambeau; Matthias W. Seeger (CoRR, 2020)

Fair Bayesian Optimization. - Valerio Perrone; Michele Donini; Krishnaram Kenthapadi; Cédric Archambeau (CoRR, 2020)

Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. - Gauthier Guinet; Valerio Perrone; Cédric Archambeau (CoRR, 2020)

Amazon SageMaker Autopilot: a white box AutoML solution at scale. - Piali Das; Valerio Perrone; Nikita Ivkin; Tanya Bansal; Zohar S. Karnin; Huibin Shen; Iaroslav Shcherbatyi; Yotam Elor; Wilton Wu; Aida Zolic; Thibaut Liénart; Alex Tang; et al. (CoRR, 2020)

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (CoRR, 2020)

On the Lack of Robust Interpretability of Neural Text Classifiers. - Muhammad Bilal Zafar; Michele Donini; Dylan Slack; Cédric Archambeau; Sanjiv Das; Krishnaram Kenthapadi (ACL/IJCNLP (Findings), 2021)

Fair Bayesian Optimization. - Valerio Perrone; Michele Donini; Muhammad Bilal Zafar; Robin Schmucker; Krishnaram Kenthapadi; Cédric Archambeau (AIES, 2021)

Hyperparameter Transfer Learning with Adaptive Complexity. - Samuel Horváth; Aaron Klein; Peter Richtárik; Cédric Archambeau (AISTATS, 2021)

BORE: Bayesian Optimization by Density-Ratio Estimation. - Louis C. Tiao; Aaron Klein; Matthias W. Seeger; Edwin V. Bonilla; Cédric Archambeau; Fabio Ramos 001 (ICML, 2021)

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (KDD, 2021)

Towards robust episodic meta-learning. - Beyza Ermis; Giovanni Zappella; Cédric Archambeau (UAI, 2021)

A resource-efficient method for repeated HPO and NAS problems. - Giovanni Zappella; David Salinas; Cédric Archambeau (CoRR, 2021)

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. - Anastasia Makarova; Huibin Shen; Valerio Perrone; Aaron Klein; Jean Baptiste Faddoul; Andreas Krause 001; Matthias W. Seeger; Cédric Archambeau (CoRR, 2021)

A multi-objective perspective on jointly tuning hardware and hyperparameters. - David Salinas; Valerio Perrone; Olivier Cruchant; Cédric Archambeau (CoRR, 2021)

Multi-objective Asynchronous Successive Halving. - Robin Schmucker; Michele Donini; Muhammad Bilal Zafar; David Salinas; Cédric Archambeau (CoRR, 2021)

Meta-Forecasting by combining Global Deep Representations with Local Adaptation. - Riccardo Grazzi; Valentin Flunkert; David Salinas; Tim Januschowski; Matthias W. Seeger; Cédric Archambeau (CoRR, 2021)

Gradient-matching coresets for continual learning. - Lukas Balles; Giovanni Zappella; Cédric Archambeau (CoRR, 2021)

More Than Words: Towards Better Quality Interpretations of Text Classifiers. - Muhammad Bilal Zafar; Philipp Schmidt 002; Michele Donini; Cédric Archambeau; Felix Biessmann; Sanjiv Ranjan Das; Krishnaram Kenthapadi (CoRR, 2021)

Automatic Termination for Hyperparameter Optimization. - Anastasia Makarova; Huibin Shen; Valerio Perrone; Aaron Klein; Jean Baptiste Faddoul; Andreas Krause 001; Matthias W. Seeger; Cédric Archambeau (AutoML, 2022)

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. - David Salinas; Matthias W. Seeger; Aaron Klein; Valerio Perrone; Martin Wistuba; Cédric Archambeau (AutoML, 2022)

Continual Learning with Transformers for Image Classification. - Beyza Ermis; Giovanni Zappella; Martin Wistuba; Aditya Rawal; Cédric Archambeau (CVPR Workshops, 2022)

Memory Efficient Continual Learning with Transformers. - Beyza Ermis; Giovanni Zappella; Martin Wistuba; Aditya Rawal; Cédric Archambeau (NeurIPS, 2022)

Private Synthetic Data for Multitask Learning and Marginal Queries. - Giuseppe Vietri; Cédric Archambeau; Sergül Aydöre; William Brown; Michael Kearns; Aaron Roth 001; Amaresh Ankit Siva; Shuai Tang; Zhiwei Steven Wu (NeurIPS, 2022)

Memory Efficient Continual Learning for Neural Text Classification. - Beyza Ermis; Giovanni Zappella; Martin Wistuba; Cédric Archambeau (CoRR, 2022)

Diverse Counterfactual Explanations for Anomaly Detection in Time Series. - Déborah Sulem; Michele Donini; Muhammad Bilal Zafar; Francois-Xavier Aubet; Jan Gasthaus; Tim Januschowski; Sanjiv Das; Krishnaram Kenthapadi; Cédric Archambeau (CoRR, 2022)

Gradient-Matching Coresets for Rehearsal-Based Continual Learning. - Lukas Balles; Giovanni Zappella; Cédric Archambeau (CoRR, 2022)

PASHA: Efficient HPO with Progressive Resource Allocation. - Ondrej Bohdal; Lukas Balles; Beyza Ermis; Cédric Archambeau; Giovanni Zappella (CoRR, 2022)

Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors. - Gianluca Detommaso; Alberto Gasparin; Andrew Wilson; Cédric Archambeau (CoRR, 2022)

Geographical Erasure in Language Generation. - Pola Schwöbel; Jacek Golebiowski; Michele Donini; Cédric Archambeau; Danish Pruthi (EMNLP (Findings), 2023)

PASHA: Efficient HPO and NAS with Progressive Resource Allocation. - Ondrej Bohdal; Lukas Balles; Martin Wistuba; Beyza Ermis; Cédric Archambeau; Giovanni Zappella (ICLR, 2023)

Optimizing Hyperparameters with Conformal Quantile Regression. - David Salinas; Jacek Golebiowski; Aaron Klein; Matthias W. Seeger; Cédric Archambeau (ICML, 2023)

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. - Luca Franceschi 001; Cemre Zor; Muhammad Bilal Zafar; Gianluca Detommaso; Cédric Archambeau; Tamas Madl; Michele Donini; Matthias W. Seeger (TML4H, 2023)

Renate: A Library for Real-World Continual Learning. - Martin Wistuba; Martin Ferianc; Lukas Balles; Cédric Archambeau; Giovanni Zappella (CoRR, 2023)

A Negative Result on Gradient Matching for Selective Backprop. - Lukas Balles; Cédric Archambeau; Giovanni Zappella (CoRR, 2023)

Explaining Probabilistic Models with Distributional Values. - Luca Franceschi 001; Michele Donini; Cédric Archambeau; Matthias W. Seeger (ICML, 2024)

Hyperparameter Optimization in Machine Learning. - Luca Franceschi 001; Michele Donini; Valerio Perrone; Aaron Klein; Cédric Archambeau; Matthias W. Seeger; Massimiliano Pontil; Paolo Frasconi (CoRR, 2024)

Fortuna: A Library for Uncertainty Quantification in Deep Learning. - Gianluca Detommaso; Alberto Gasparin; Michele Donini; Matthias W. Seeger; Andrew Gordon Wilson; Cédric Archambeau (J. Mach. Learn. Res., 2024)

On the Choice of Learning Rate for Local SGD. - Lukas Balles; Prabhu Teja Sivaprasad; Cédric Archambeau (Trans. Mach. Learn. Res., 2024)

Structural Pruning of Pre-trained Language Models via Neural Architecture Search. - Aaron Klein; Jacek Golebiowski; Xingchen Ma; Valerio Perrone; Cédric Archambeau (Trans. Mach. Learn. Res., 2024)

SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling. - Yasin Almalioglu; Andrzej Kucik; Geoffrey French; Dafni Antotsiou; Alexander Adam; Cédric Archambeau (CoRR, 2025)