Cédric Archambeau

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 57

Top Venues

CoRR

ICML

KDD

NeurIPS

Catholic University of Louvain, Louvain-la-Neuve, Belgium (PHD)

Education History

Catholic University of Louvain, Louvain-la-Neuve, Belgium

PHD

Publications

Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau
Structural Pruning of Pre-trained Language Models via Neural Architecture Search. CoRR (2024)
Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
Explaining Probabilistic Models with Distributional Values. CoRR (2024)
Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau
On the Choice of Learning Rate for Local SGD. Trans. Mach. Learn. Res. 2024 (2024)
Lukas Balles, Cédric Archambeau, Giovanni Zappella
A Negative Result on Gradient Matching for Selective Backprop. CoRR (2023)
Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella
Renate: A Library for Real-World Continual Learning. CoRR (2023)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. CoRR (2023)
Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H (2023)
Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
Geographical Erasure in Language Generation. EMNLP (Findings) (2023)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. ICML (2023)
Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR (2023)
Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
Geographical Erasure in Language Generation. CoRR (2023)
Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR (2023)
Giuseppe Vietri, Cédric Archambeau, Sergül Aydore, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
Continual Learning with Transformers for Image Classification. CoRR (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau
Memory Efficient Continual Learning for Neural Text Classification. CoRR (2022)
Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau
Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR (2022)
David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML (2022)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Automatic Termination for Hyperparameter Optimization. AutoML (2022)
Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
PASHA: Efficient HPO with Progressive Resource Allocation. CoRR (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
Continual Learning with Transformers for Image Classification. CVPR Workshops (2022)
Lukas Balles, Giovanni Zappella, Cédric Archambeau
Gradient-Matching Coresets for Rehearsal-Based Continual Learning. CoRR (2022)
Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS (2022)
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
Memory Efficient Continual Learning with Transformers. NeurIPS (2022)
Gianluca Detommaso, Alberto Gasparin, Andrew Wilson, Cédric Archambeau
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors. CoRR (2022)
Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR (2021)
Lukas Balles, Giovanni Zappella, Cédric Archambeau
Gradient-matching coresets for continual learning. CoRR (2021)
Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
Hyperparameter Transfer Learning with Adaptive Complexity. CoRR (2021)
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
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD (2021)
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
Multi-objective Asynchronous Successive Halving. CoRR (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML (2021)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. CoRR (2021)
Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS (2021)
Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau
Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR (2021)
Beyza Ermis, Giovanni Zappella, Cédric Archambeau
Towards robust episodic meta-learning. UAI (2021)
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau
Fair Bayesian Optimization. AIES (2021)
Giovanni Zappella, David Salinas, Cédric Archambeau
A resource-efficient method for repeated HPO and NAS problems. CoRR (2021)
David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau
A multi-objective perspective on jointly tuning hardware and hyperparameters. CoRR (2021)
Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) (2021)
Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi
More Than Words: Towards Better Quality Interpretations of Text Classifiers. CoRR (2021)
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
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR (2020)
Gauthier Guinet, Valerio Perrone, Cédric Archambeau
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. CoRR (2020)
Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger
Cost-aware Bayesian Optimization. CoRR (2020)
Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger
Model-based Asynchronous Hyperparameter Optimization. CoRR (2020)
Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau
Fair Bayesian Optimization. CoRR (2020)
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
Amazon SageMaker Autopilot: a white box AutoML solution at scale. DEEM@SIGMOD (2020)
Cuong Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau
LEEP: A New Measure to Evaluate Transferability of Learned Representations. ICML (2020)
Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, 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
Amazon SageMaker Autopilot: a white box AutoML solution at scale. CoRR (2020)
Cuong V. Nguyen, Tal Hassner, Cédric Archambeau, Matthias W. Seeger
LEEP: A New Measure to Evaluate Transferability of Learned Representations. CoRR (2020)
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR (2019)
Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger
Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR (2019)
Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau
Scalable Hyperparameter Transfer Learning. NeurIPS (2018)
Tammo Rukat, Dustin Lange, Cédric Archambeau
An interpretable latent variable model for attribute applicability in the Amazon catalogue. CoRR (2017)
Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger
Bayesian Optimization with Tree-structured Dependencies. ICML (2017)
Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. ICML (2016)
Jim C. Huang, Rodolphe Jenatton, Cédric Archambeau
Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation. KDD (2016)
Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
Online optimization and regret guarantees for non-additive long-term constraints. CoRR (2016)
Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard
Latent IBP Compound Dirichlet Allocation. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Maxim Rabinovich, Cédric Archambeau
Online Inference for Relation Extraction with a Reduced Feature Set. CoRR (2015)
Antonino Freno, Martin Saveski, Rodolphe Jenatton, Cédric Archambeau
One-Pass Ranking Models for Low-Latency Product Recommendations. KDD (2015)
Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. CoRR (2015)
Behrouz Behmardi, Cédric Archambeau, Guillaume Bouchard
Overlapping Trace Norms in Multi-View Learning. CoRR (2014)
Tiziano Piccardi, Gregorio Convertino, Massimo Zancanaro, Ji Wang, Cédric Archambeau
Towards crowd-based customer service: a mixed-initiative tool for managing Q&A sites. CHI (2014)
Dawei Yin, Shengbo Guo, Boris Chidlovskii, Brian D. Davison, Cédric Archambeau, Guillaume Bouchard
Connecting comments and tags: improved modeling of social tagging systems. WSDM (2013)
Anil Kumar Nelakanti, Cédric Archambeau, Julien Mairal, Francis R. Bach, Guillaume Bouchard
Structured Penalties for Log-Linear Language Models. EMNLP (2013)
Michael Kaschesky, Pawel Sobkowicz, José Miguel Hernández-Lobato, Guillaume Bouchard, Cédric Archambeau, Nicolas Scharioth, Robert Manchin, Adrian Gschwend, Reinhard Riedl
Bringing Representativeness into Social Media Monitoring and Analysis. HICSS (2013)
William Darling, Cédric Archambeau, Shachar Mirkin, Guillaume Bouchard
Error Prediction with Partial Feedback. ECML/PKDD (2) (2013)
Yuan Shen, Dan Cornford, Manfred Opper, Cédric Archambeau
Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. Comput. Stat. 27 (1) (2012)
Cédric Archambeau, Francois Caron
Plackett-Luce regression: A new Bayesian model for polychotomous data. UAI (2012)
Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh
The sequence memoizer. Commun. ACM 54 (2) (2011)
Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau
Robust Bayesian Matrix Factorisation. AISTATS (2011)
Benjamin V. Hanrahan, Guillaume Bouchard, Gregorio Convertino, Thiébaud Weksteen, Nicholas Kong, Cédric Archambeau, Ed H. Chi
Mail2Wiki: low-cost sharing and early curation from email to wikis. C&T (2011)
Cédric Archambeau, Shengbo Guo, Onno Zoeter
Sparse Bayesian Multi-Task Learning. NIPS (2011)
Benjamin V. Hanrahan, Thiébaud Weksteen, Nicholas Kong, Gregorio Convertino, Guillaume Bouchard, Cédric Archambeau, Ed H. Chi
Mail2Wiki: posting and curating Wiki content from email. IUI (2011)
Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Louis Barillec
A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. J. Signal Process. Syst. 61 (1) (2010)
Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau
Switching regulatory models of cellular stress response. Bioinform. 25 (10) (2009)
Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh
A stochastic memoizer for sequence data. ICML (2009)
Manfred Opper, Cédric Archambeau
The Variational Gaussian Approximation Revisited. Neural Comput. 21 (3) (2009)
Stefano Lise, Cédric Archambeau, Massimiliano Pontil, David T. Jones
Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinform. 10 (2009)
Nicolas Delannay, Cédric Archambeau, Michel Verleysen
Improving the Robustness to Outliers of Mixtures of Probabilistic PCAs. PAKDD (2008)
Cédric Archambeau, Nicolas Delannay, Michel Verleysen
Mixtures of robust probabilistic principal component analyzers. Neurocomputing 71 (7-9) (2008)
François-Xavier Standaert, Cédric Archambeau
Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages. CHES (2008)
Cédric Archambeau, Francis R. Bach
Sparse probabilistic projections. NIPS (2008)
Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor
Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice (2007)
Cédric Archambeau, Michel Verleysen
Robust Bayesian clustering. Neural Networks 20 (1) (2007)
François-Xavier Standaert, Eric Peeters, Cédric Archambeau, Jean-Jacques Quisquater
Towards Security Limits in Side-Channel Attacks. IACR Cryptol. ePrint Arch. 2007 (2007)
Cédric Archambeau, Nicolas Delannay, Michel Verleysen
Mixtures of robust probabilistic principal component analyzers. ESANN (2007)
Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor
Variational Inference for Diffusion Processes. NIPS (2007)
Cédric Archambeau, Nicolas Delannay, Michel Verleysen
Robust probabilistic projections. ICML (2006)
Nicolas Delannay, Cédric Archambeau, Michel Verleysen
Automatic Adjustment of Discriminant Adaptive Nearest Neighbor. ICPR (2) (2006)
Cédric Archambeau, Maurizio Valle, Alex Assenza, Michel Verleysen
Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures. ISCAS (2006)
Cédric Archambeau, Eric Peeters, François-Xavier Standaert, Jean-Jacques Quisquater
Template Attacks in Principal Subspaces. CHES (2006)
François-Xavier Standaert, Eric Peeters, Cédric Archambeau, Jean-Jacques Quisquater
Towards Security Limits in Side-Channel Attacks. CHES (2006)
Cédric Archambeau, Michel Verleysen
Manifold Constrained Finite Gaussian Mixtures. IWANN (2005)
Cédric Archambeau, Michel Verleysen
Manifold Constrained Variational Mixtures. ICANN (2) (2005)
Cédric Archambeau, Frédéric Vrins, Michel Verleysen
Flexible and Robust Bayesian Classification by Finite Mixture Models. ESANN (2004)
Cédric Archambeau, Jean Delbeke, Claude Veraart, Michel Verleysen
Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind. Artif. Intell. Medicine 32 (3) (2004)
Frédéric Vrins, Cédric Archambeau, Michel Verleysen
Towards a Local Separation Performances Estimator Using Common ICA Contrast Functions? ESANN (2004)
Cédric Archambeau, Torsten Butz, Vlad Popovici, Michel Verleysen, Jean-Philippe Thiran
Supervised Nonparametric Information Theoretic Classification. ICPR (3) (2004)
John Aldo Lee, Cédric Archambeau, Michel Verleysen
Locally Linear Embedding versus Isotop. ESANN (2003)

Cédric Archambeau.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Cédric Archambeau