Cédric Archambeau

According to our database, Cédric Archambeau authored at least 90 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Explaining Probabilistic Models with Distributional Values.

Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger

CoRR, 2024

2023

A Negative Result on Gradient Matching for Selective Backprop.

Lukas Balles, Cédric Archambeau, Giovanni Zappella

CoRR, 2023

Renate: A Library for Real-World Continual Learning.

Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella

CoRR, 2023

Fortuna: A Library for Uncertainty Quantification in Deep Learning.

Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau

CoRR, 2023

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography.

Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger

Proceedings of the Trustworthy Machine Learning for Healthcare, 2023

Optimizing Hyperparameters with Conformal Quantile Regression.

David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau

Proceedings of the International Conference on Machine Learning, 2023

PASHA: Efficient HPO and NAS with Progressive Resource Allocation.

Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Geographical Erasure in Language Generation.

Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022

Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors.

Gianluca Detommaso, Alberto Gasparin, Andrew Wilson, Cédric Archambeau

CoRR, 2022

PASHA: Efficient HPO with Progressive Resource Allocation.

Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella

CoRR, 2022

Gradient-Matching Coresets for Rehearsal-Based Continual Learning.

Lukas Balles, Giovanni Zappella, 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

Memory Efficient Continual Learning for Neural Text Classification.

Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau

CoRR, 2022

Private Synthetic Data for Multitask Learning and Marginal Queries.

Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Memory Efficient Continual Learning with Transformers.

Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Continual Learning with Transformers for Image Classification.

Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 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

Proceedings of the International Conference on Automated Machine Learning, 2022

Automatic Termination for Hyperparameter Optimization.

Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau

Proceedings of the International Conference on Automated Machine Learning, 2022

2021

More Than Words: Towards Better Quality Interpretations of Text Classifiers.

Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi

CoRR, 2021

Gradient-matching coresets for continual learning.

Lukas Balles, Giovanni Zappella, 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

Multi-objective Asynchronous Successive Halving.

Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau

CoRR, 2021

A multi-objective perspective on jointly tuning hardware and hyperparameters.

David Salinas, Valerio Perrone, Olivier Cruchant, 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, Matthias W. Seeger, Cédric Archambeau

CoRR, 2021

A resource-efficient method for repeated HPO and NAS problems.

Giovanni Zappella, David Salinas, Cédric Archambeau

CoRR, 2021

Towards robust episodic meta-learning.

Beyza Ermis, Giovanni Zappella, Cédric Archambeau

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization.

Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

BORE: Bayesian Optimization by Density-Ratio Estimation.

Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos

Proceedings of the 38th International Conference on Machine Learning, 2021

Hyperparameter Transfer Learning with Adaptive Complexity.

Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Fair Bayesian Optimization.

Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

On the Lack of Robust Interpretability of Neural Text Classifiers.

Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi

Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization.

Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, 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, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias W. Seeger, Bhaskar Dutt, Laurence Rouesnel

CoRR, 2020

Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization.

Gauthier Guinet, Valerio Perrone, Cédric Archambeau

CoRR, 2020

Fair Bayesian Optimization.

Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau

CoRR, 2020

Cost-aware Bayesian Optimization.

Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger

CoRR, 2020

Model-based Asynchronous Hyperparameter Optimization.

Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger

CoRR, 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, Huibin Shen, Amr Ahmed, Fela Winkelmolen, Miroslav Miladinovic, Cédric Archambeau, Alex Tang, Bhaskar Dutt, Patricia Grao, Kumar Venkateswar

Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, 2020

LEEP: A New Measure to Evaluate Transferability of Learned Representations.

Cuong V. Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Constrained Bayesian Optimization with Max-Value Entropy Search.

Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger

CoRR, 2019

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

2018

Scalable Hyperparameter Transfer Learning.

Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

An interpretable latent variable model for attribute applicability in the Amazon catalogue.

Tammo Rukat, Dustin Lange, Cédric Archambeau

CoRR, 2017

Bayesian Optimization with Tree-structured Dependencies.

Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Online optimization and regret guarantees for non-additive long-term constraints.

Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau

CoRR, 2016

Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation.

Jim C. Huang, Rodolphe Jenatton, Cédric Archambeau

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints.

Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Latent IBP Compound Dirichlet Allocation.

Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Online Inference for Relation Extraction with a Reduced Feature Set.

Maxim Rabinovich, Cédric Archambeau

CoRR, 2015

One-Pass Ranking Models for Low-Latency Product Recommendations.

Antonino Freno, Martin Saveski, Rodolphe Jenatton, Cédric Archambeau

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014

Overlapping Trace Norms in Multi-View Learning.

Behrouz Behmardi, Cédric Archambeau, Guillaume Bouchard

CoRR, 2014

Towards crowd-based customer service: a mixed-initiative tool for managing Q&A sites.

Tiziano Piccardi, Gregorio Convertino, Massimo Zancanaro, Ji Wang, Cédric Archambeau

Proceedings of the CHI Conference on Human Factors in Computing Systems, 2014

2013

Connecting comments and tags: improved modeling of social tagging systems.

Dawei Yin, Shengbo Guo, Boris Chidlovskii, Brian D. Davison, Cédric Archambeau, Guillaume Bouchard

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, 2013

Error Prediction with Partial Feedback.

William Darling, Cédric Archambeau, Shachar Mirkin, Guillaume Bouchard

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 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

Proceedings of the 46th Hawaii International Conference on System Sciences, 2013

Structured Penalties for Log-Linear Language Models.

Anil Kumar Nelakanti, Cédric Archambeau, Julien Mairal, Francis R. Bach, Guillaume Bouchard

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013

2012

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions.

Yuan Shen, Dan Cornford, Manfred Opper, Cédric Archambeau

Comput. Stat., 2012

Plackett-Luce regression: A new Bayesian model for polychotomous data.

Cédric Archambeau, Francois Caron

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011

Robust Bayesian Matrix Factorisation.

Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

The sequence memoizer.

Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh

Commun. ACM, 2011

Sparse Bayesian Multi-Task Learning.

Cédric Archambeau, Shengbo Guo, Onno Zoeter

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 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

Proceedings of the 16th International Conference on Intelligent User Interfaces, 2011

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

Proceedings of the Fifth International Conference on Communities and Technologies, 2011

2010

A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems.

Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Louis Barillec

J. Signal Process. Syst., 2010

2009

The Variational Gaussian Approximation Revisited.

Manfred Opper, Cédric Archambeau

Neural Comput., 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

Switching regulatory models of cellular stress response.

Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau

Bioinform., 2009

A stochastic memoizer for sequence data.

Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Mixtures of robust probabilistic principal component analyzers.

Cédric Archambeau, Nicolas Delannay, Michel Verleysen

Neurocomputing, 2008

Improving the Robustness to Outliers of Mixtures of Probabilistic PCAs.

Nicolas Delannay, Cédric Archambeau, Michel Verleysen

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2008

Sparse probabilistic projections.

Cédric Archambeau, Francis R. Bach

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages.

François-Xavier Standaert, Cédric Archambeau

Proceedings of the Cryptographic Hardware and Embedded Systems, 2008

2007

Robust Bayesian clustering.

Cédric Archambeau, Michel Verleysen

Neural Networks, 2007

Gaussian Process Approximations of Stochastic Differential Equations.

Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor

Proceedings of the Gaussian Processes in Practice, 2007

Towards Security Limits in Side-Channel Attacks.

François-Xavier Standaert, Eric Peeters, Cédric Archambeau, Jean-Jacques Quisquater

IACR Cryptol. ePrint Arch., 2007

Variational Inference for Diffusion Processes.

Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures.

Cédric Archambeau, Maurizio Valle, Alex Assenza, Michel Verleysen

Proceedings of the International Symposium on Circuits and Systems (ISCAS 2006), 2006

Automatic Adjustment of Discriminant Adaptive Nearest Neighbor.

Nicolas Delannay, Cédric Archambeau, Michel Verleysen

Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), 2006

Robust probabilistic projections.

Cédric Archambeau, Nicolas Delannay, Michel Verleysen

Proceedings of the Machine Learning, 2006

Template Attacks in Principal Subspaces.

Cédric Archambeau, Eric Peeters, François-Xavier Standaert, Jean-Jacques Quisquater

Proceedings of the Cryptographic Hardware and Embedded Systems, 2006

2005

Probabilistic models in noisy environments : and their application to a visual prosthesis for the blind/.

Cédric Archambeau

PhD thesis, 2005

Manifold Constrained Finite Gaussian Mixtures.

Cédric Archambeau, Michel Verleysen

Proceedings of the Computational Intelligence and Bioinspired Systems, 2005

Manifold Constrained Variational Mixtures.

Cédric Archambeau, Michel Verleysen

Proceedings of the Artificial Neural Networks: Formal Models and Their Applications, 2005

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

Supervised Nonparametric Information Theoretic Classification.

Cédric Archambeau, Torsten Butz, Vlad Popovici, Michel Verleysen, Jean-Philippe Thiran

Proceedings of the 17th International Conference on Pattern Recognition, 2004

Towards a Local Separation Performances Estimator Using Common ICA Contrast Functions?

Frédéric Vrins, Cédric Archambeau, Michel Verleysen

Proceedings of the 12th European Symposium on Artificial Neural Networks, 2004

Flexible and Robust Bayesian Classification by Finite Mixture Models.

Cédric Archambeau, Frédéric Vrins, Michel Verleysen

Proceedings of the 12th European Symposium on Artificial Neural Networks, 2004

2003

Locally Linear Embedding versus Isotop.

John Aldo Lee, Cédric Archambeau, Michel Verleysen

Proceedings of the 11th European Symposium on Artificial Neural Networks, 2003

On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures.

Cédric Archambeau, John Aldo Lee, Michel Verleysen

Proceedings of the 11th European Symposium on Artificial Neural Networks, 2003

2002

Width optimization of the Gaussian kernels in Radial Basis Function Networks.

Nabil Benoudjit, Cédric Archambeau, Amaury Lendasse, John Aldo Lee, Michel Verleysen

Proceedings of the 10th Eurorean Symposium on Artificial Neural Networks, 2002