Cédric Archambeau

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All published works

Action	Title	Year	Authors
+ PDF Chat	Hyperparameter Optimization in Machine Learning	2024	Luca Franceschi Michele Donini Valerio Perrone Aaron Klein Cédric Archambeau Matthias Seeger Massimiliano Pontil Paolo Frasconi
+ PDF Chat	Structural Pruning of Pre-trained Language Models via Neural Architecture Search	2024	Aaron Klein Jacek Gołębiowski Xingchen Ma Valerio Perrone Cédric Archambeau
+ PDF Chat	Explaining Probabilistic Models with Distributional Values	2024	Luca Franceschi Michele Donini Cédric Archambeau Matthias Seeger
+	Fortuna: A Library for Uncertainty Quantification in Deep Learning	2023	Gianluca Detommaso Alberto Gasparin Michele Donini Matthias Seeger Andrew Gordon Wilson Cédric Archambeau
+	Renate: A Library for Real-World Continual Learning	2023	Martin Wistuba Martin Ferianc Lukas Balles Cédric Archambeau Giovanni Zappella
+	Optimizing Hyperparameters with Conformal Quantile Regression	2023	David Salinas Jacek Gołębiowski Aaron Klein Matthias Seeger Cédric Archambeau
+	Geographical Erasure in Language Generation	2023	Pola Schwöbel Jacek Gołębiowski Michele Donini Cédric Archambeau Danish Pruthi
+	Geographical Erasure in Language Generation	2023	Pola Schwöbel Jacek Gołębiowski Michele Donini Cédric Archambeau Danish Pruthi
+	A Negative Result on Gradient Matching for Selective Backprop	2023	Lukas Balles Cédric Archambeau Giovanni Zappella
+ PDF Chat	Continual Learning with Transformers for Image Classification	2022	Beyza Ermiş Giovanni Zappella Martin Wistuba Aditya Rawal Cédric Archambeau
+	Diverse Counterfactual Explanations for Anomaly Detection in Time Series	2022	Déborah Sulem Michele Donini Muhammad Bilal Zafar François-Xavier Aubet Jan Gasthaus Tim Januschowski Sanjiv Ranjan Das Krishnaram Kenthapadi Cédric Archambeau
+	Gradient-Matching Coresets for Rehearsal-Based Continual Learning	2022	Lukas Balles Giovanni Zappella Cédric Archambeau
+	Memory Efficient Continual Learning with Transformers	2022	Beyza Ermiş Giovanni Zappella Martin Wistuba Cédric Archambeau
+	Continual Learning with Transformers for Image Classification	2022	Beyza Ermiş Giovanni Zappella Martin Wistuba Aditya Rawal Cédric Archambeau
+	Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors	2022	Gianluca Detommaso Alberto Gasparin A. S. Wilson Cédric Archambeau
+	Private Synthetic Data for Multitask Learning and Marginal Queries	2022	Giuseppe Vietri Cédric Archambeau Sergül Aydöre William Brown Michael Kearns Aaron Roth Ankit Siva Shuai Tang Zhiwei Steven Wu
+	PASHA: Efficient HPO and NAS with Progressive Resource Allocation	2022	Ondrej Bohdal Lukas Balles Beyza Ermiş Cédric Archambeau Giovanni Zappella
+ PDF Chat	Gradient-matching coresets for continual learning	2021	Lukas Balles Giovanni Zappella Cédric Archambeau
+	Meta-Forecasting by combining Global Deep Representations with Local Adaptation.	2021	Riccardo Grazzi Valentín Flunkert David Salinas Tim Januschowski Matthias Seeger Cédric Archambeau
+ PDF Chat	Meta-Forecasting by combining Global Deep Representations with Local Adaptation	2021	Riccardo Grazzi Valentín Flunkert David Salinas Tim Januschowski Matthias Seeger Cédric Archambeau
+	Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization	2021	Valerio Perrone Huibin Shen Aida Zolic Iaroslav Shcherbatyi Amr Ahmed Tanya Bansal Michele Donini Fela Winkelmolen Rodolphe Jenatton Jean Baptiste Faddoul
+	Fair Bayesian Optimization	2021	Valerio Perrone Michele Donini Muhammad Bilal Zafar Robin Schmucker Krishnaram Kenthapadi Cédric Archambeau
+	A multi-objective perspective on jointly tuning hardware and hyperparameters.	2021	David Salinas Valerio Perrone Olivier Cruchant Cédric Archambeau
+	On the Lack of Robust Interpretability of Neural Text Classifiers	2021	Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das Krishnaram Kenthapadi
+	Overfitting in Bayesian optimization: An empirical study and early-stopping solution	2021	Anastasia Makarova Huibin Shen Valerio Perrone Aaron Klein Jean Baptiste Faddoul Andreas Krause Matthias Seeger Cédric Archambeau
+	Hyperparameter Transfer Learning with Adaptive Complexity	2021	Samuel Horváth Aaron Klein Peter Richtárik Cédric Archambeau
+	BORE: Bayesian Optimization by Density-Ratio Estimation	2021	Louis C. Tiao Aaron Klein Matthias Seeger Edwin V. Bonilla Cédric Archambeau Fábio Ramos
+	A resource-efficient method for repeated HPO and NAS problems	2021	Giovanni Zappella David Salinas Cédric Archambeau
+	BORE: Bayesian Optimization by Density-Ratio Estimation	2021	Chi-Chun Tiao Aaron Klein Matthias Seeger Edwin V. Bonilla Cédric Archambeau Fábio Ramos
+	Multi-objective Asynchronous Successive Halving	2021	Robin Schmucker Michele Donini Muhammad Bilal Zafar David Salinas Cédric Archambeau
+	On the Lack of Robust Interpretability of Neural Text Classifiers	2021	Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das Krishnaram Kenthapadi
+	More Than Words: Towards Better Quality Interpretations of Text Classifiers	2021	Muhammad Bilal Zafar Philipp Schmidt Michele Donini Cédric Archambeau Felix Bießmann Sanjiv Ranjan Das Krishnaram Kenthapadi
+	A multi-objective perspective on jointly tuning hardware and hyperparameters	2021	David Salinas Valerio Perrone Olivier Cruchant Cédric Archambeau
+	On the Lack of Robust Interpretability of Neural Text Classifiers	2021	Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das Krishnaram Kenthapadi
+	Automatic Termination for Hyperparameter Optimization	2021	Anastasia Makarova Huibin Shen Valerio Perrone Aaron Klein Jean Baptiste Faddoul Andreas Krause Matthias Seeger Cédric Archambeau
+	Hyperparameter Transfer Learning with Adaptive Complexity	2021	Samuel Horváth Aaron Klein Peter Richtárik Cédric Archambeau
+	Meta-Forecasting by combining Global Deep Representations with Local Adaptation	2021	Riccardo Grazzi Valentín Flunkert David Salinas Tim Januschowski Matthias Seeger Cédric Archambeau
+	Gradient-matching coresets for continual learning	2021	Lukas Balles Giovanni Zappella Cédric Archambeau
+	Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization	2020	Valerio Perrone Huibin Shen Aida Zolic Iaroslav Shcherbatyi Amr Ahmed Tanya Bansal Michele Donini Fela Winkelmolen Rodolphe Jenatton Jean Baptiste Faddoul
+	Amazon SageMaker Autopilot: a white box AutoML solution at scale	2020	Piali Das Valerio Perrone Nikita Ivkin Tanya Bansal Zohar Karnin Huibin Shen Iaroslav Shcherbatyi Yotam Elor Wilton Wu Aida Zolic
+	Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization	2020	Valerio Perrone Huibin Shen Aida Zolic Iaroslav Shcherbatyi Amr Ahmed Tanya Bansal Michele Donini Fela Winkelmolen Rodolphe Jenatton Jean Baptiste Faddoul
+	Pareto-efficient acquisition functions for cost-aware Bayesian optimization	2020	Gauthier Guinet Valerio Perrone Cédric Archambeau
+	Fair Bayesian Optimization	2020	Valerio Perrone Michele Donini Muhammad Bilal Zafar Robin Schmucker Krishnaram Kenthapadi Cédric Archambeau
+	LEEP: A New Measure to Evaluate Transferability of Learned Representations	2020	Cuong V. Nguyen Tal Hassner Matthias Seeger Cédric Archambeau
+	Cost-aware Bayesian Optimization	2020	Eric Hans Lee Valerio Perrone Cédric Archambeau Matthias Seeger
+	Model-based Asynchronous Hyperparameter and Neural Architecture Search	2020	Aaron Klein Louis C. Tiao Thibaut Lienart Cédric Archambeau Matthias Seeger
+	Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization	2020	Valerio Perrone Huibin Shen Aida Zolic Iaroslav Shcherbatyi Amr Ahmed Tanya Bansal Michele Donini Fela Winkelmolen Rodolphe Jenatton Jean Baptiste Faddoul
+	Amazon SageMaker Autopilot: a white box AutoML solution at scale	2020	Piali Das Valerio Perrone Nikita Ivkin Tanya Bansal Zohar Karnin Huibin Shen Iaroslav Shcherbatyi Yotam Elor Wilton Wu Aida Zolic
+	Fair Bayesian Optimization	2020	Valerio Perrone Michele Donini Muhammad Bilal Zafar Robin Schmucker Krishnaram Kenthapadi Cédric Archambeau
+	Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization	2020	Gauthier Guinet Valerio Perrone Cédric Archambeau
+	Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning	2019	Valerio Perrone Huibin Shen Matthias Seeger Cédric Archambeau Rodolphe Jenatton
+	Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning	2019	Valerio Perrone Huibin Shen Matthias Seeger Cédric Archambeau Rodolphe Jenatton
+	Constrained Bayesian Optimization with Max-Value Entropy Search	2019	Valerio Perrone Iaroslav Shcherbatyi Rodolphe Jenatton Cédric Archambeau Matthias Seeger
+	Constrained Bayesian Optimization with Max-Value Entropy Search	2019	Valerio Perrone Iaroslav Shcherbatyi Rodolphe Jenatton Cédric Archambeau Matthias Seeger
+	Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning	2019	Valerio Perrone Huibin Shen Matthias Seeger Cédric Archambeau Rodolphe Jenatton
+	An interpretable latent variable model for attribute applicability in the Amazon catalogue	2017	Tammo Rukat Dustin Lange Cédric Archambeau
+	Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start	2017	Valerio Perrone Rodolphe Jenatton Matthias Seeger Cédric Archambeau
+	An interpretable latent variable model for attribute applicability in the Amazon catalogue	2017	Tammo Rukat Dustin Lange Cédric Archambeau
+	Online optimization and regret guarantees for non-additive long-term constraints	2016	Rodolphe Jenatton Jim Huang Dominik Csiba Cédric Archambeau
+	Online Inference for Relation Extraction with a Reduced Feature Set	2015	Maxim Rabinovich Cédric Archambeau
+	Adaptive Algorithms for Online Convex Optimization with Long-term Constraints	2015	Rodolphe Jenatton Jim Huang Cédric Archambeau
+	Incremental Variational Inference for Latent Dirichlet Allocation	2015	Cédric Archambeau Beyza Ermiş
+	Overlapping Trace Norms in Multi-View Learning	2014	Behrouz Behmardi Cédric Archambeau Guillaume Bouchard
+	Plackett-Luce regression: A new Bayesian model for polychotomous data	2012	Cédric Archambeau François Caron
+	Approximate inference for continuous-time Markov processes	2011	Cédric Archambeau Manfred Opper
+	Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions	2011	Yuan Shen Dan Cornford Manfred Opper Cédric Archambeau
+	Multiple Gaussian Process Models	2011	Cédric Archambeau Francis Bach
+	Improving the Robustness to Outliers of Mixtures of Probabilistic PCAs	2008	Nicolas Delannay Cédric Archambeau Michel Verleysen
+ PDF Chat	Mixtures of robust probabilistic principal component analyzers	2008	Cédric Archambeau Nicolas Delannay Michel Verleysen
+	Variational Inference for Diffusion Processes	2007	Cédric Archambeau Manfred Opper Yuan Shen Dan Cornford John Shawe‐Taylor
+ PDF Chat	Robust Bayesian clustering	2006	Cédric Archambeau Michel Verleysen
+	Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures	2006	Cédric Archambeau Maurizio Valle Alex Assenza Michel Verleysen
+ PDF Chat	Robust probabilistic projections	2006	Cédric Archambeau Nicolas Delannay Michel Verleysen
+	Flexible and Robust Bayesian Classification by Finite Mixture Models	2004	Cédric Archambeau Frédéric Vrins Michel Verleysen
+	Fully nonparametric probability density function estimation with finite Gaussian mixture models	2003	Cédric Archambeau Michel Verleysen

Common Coauthors

Coauthor	Papers Together
Matthias Seeger	26
Valerio Perrone	24
Michele Donini	18
Rodolphe Jenatton	14
Giovanni Zappella	12
Krishnaram Kenthapadi	12
Huibin Shen	11
Aaron Klein	10
Muhammad Bilal Zafar	9
David Salinas	8
Iaroslav Shcherbatyi	8
Jean Baptiste Faddoul	8
Michel Verleysen	7
Lukas Balles	6
Amr Ahmed	6
Aida Zolic	6
Miroslav Miladinovic	6
Tanya Bansal	6
Fela Winkelmolen	6
Beyza Ermiş	5
Sanjiv Ranjan Das	5
Tim Januschowski	4
Barbara Pogorzelska	4
Martin Wistuba	4
Jacek Gołębiowski	4
Robin Schmucker	4
Dylan Slack	3
Thibaut Lienart	3
Riccardo Grazzi	3
Valentín Flunkert	3
Nicolas Delannay	3
Manfred Opper	3
Samuel Horváth	2
Dan Cornford	2
Edwin V. Bonilla	2
Nikita Ivkin	2
Leo Dirac	2
Yotam Elor	2
Zohar Karnin	2
Gianluca Detommaso	2
Anastasia Makarova	2
Yuan Shen	2
Alberto Gasparin	2
Piali Das	2
Luca Franceschi	2
Olivier Cruchant	2
Laurence Rouesnel	2
Danish Pruthi	2
Louis C. Tiao	2
Andre Perunicic	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Max-value Entropy Search for Efficient Bayesian Optimization	2017	Zi Wang Stefanie Jegelka	9
+	Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm	1977	A. P. Dempster N. M. Laird Donald B. Rubin	8
+	Practical Bayesian Optimization of Machine Learning Algorithms	2012	Jasper Snoek Hugo Larochelle Ryan P. Adams	8
+	Pattern Recognition and Machine Learning	2007	Christopher Bishop	6
+	Scikit-learn: Machine Learning in Python	2012	Fabián Pedregosa Gaël Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron J. Weiss Vincent Dubourg	6
+ PDF Chat	Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting	2012	Niranjan Srinivas Andreas Krause Sham M. Kakade Matthias Seeger	5
+	Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning	2019	Valerio Perrone Huibin Shen Matthias Seeger Cédric Archambeau Rodolphe Jenatton	5
+	Predictive Entropy Search for Efficient Global Optimization of Black-box Functions	2014	José Miguel Hernández-Lobato Matthew W. Hoffman Zoubin Ghahramani	5
+	XGBoost	2016	Tianqi Chen Carlos Guestrin	5
+	Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization	2019	Aaron Klein Frank Hutter	5
+	Scalable Bayesian Optimization Using Deep Neural Networks	2015	Jasper Snoek Oren Rippel Kevin Swersky Ryan Kiros Nadathur Satish Narayanan Sundaram Mostofa Patwary Prabhat Ryan P. Adams	5
+ PDF Chat	OpenML	2014	Joaquin Vanschoren Jan N. van Rijn Bernd Bischl Luı́s Torgo	5
+	None	2000	David Peel Geoffrey J. McLachlan	5
+	Fair Bayesian Optimization	2021	Valerio Perrone Michele Donini Muhammad Bilal Zafar Robin Schmucker Krishnaram Kenthapadi Cédric Archambeau	4
+	Freeze-Thaw Bayesian Optimization	2014	Kevin Swersky Jasper Snoek Ryan P. Adams	4
+	Constrained Bayesian Optimization with Max-Value Entropy Search	2019	Valerio Perrone Iaroslav Shcherbatyi Rodolphe Jenatton Cédric Archambeau Matthias Seeger	4
+	NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search	2020	Xuanyi Dong Yi Yang	4
+	A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning	2010	Eric Brochu Vlad M. Cora Nando de Freitas	4
+	Cost-aware Bayesian Optimization	2020	Eric Hans Lee Valerio Perrone Cédric Archambeau Matthias Seeger	4
+	MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems	2015	Tianqi Chen Mu Li Yutian Li Min Lin Naiyan Wang Minjie Wang Tianjun Xiao Bing Xu Chiyuan Zhang Zheng Zhang	3
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+	Mitigating Unwanted Biases with Adversarial Learning	2018	Brian Hu Zhang Blake Lemoine Margaret Mitchell	3
+	Pareto-efficient acquisition functions for cost-aware Bayesian optimization	2020	Gauthier Guinet Valerio Perrone Cédric Archambeau	3
+	PyTorch: An Imperative Style, High-Performance Deep Learning Library	2019	Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga	3
+ PDF Chat	Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)	2006	Alexandros Beskos Omiros Papaspiliopoulos Gareth O. Roberts Paul Fearnhead	3
+	AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.	2020	Nick Erickson Jonas Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alexander J. Smola	3
+	EM Algorithms for PCA and SPCA	1997	Sam T. Roweis	3
+ PDF Chat	Certifying and Removing Disparate Impact	2015	Michael Feldman Sorelle A. Friedler John Moeller Carlos Scheidegger Suresh Venkatasubramanian	3
+ PDF Chat	Fairness through awareness	2012	Cynthia Dwork Moritz Hardt Toniann Pitassi Omer Reingold Richard S. Zemel	3
+	Input Warping for Bayesian Optimization of Non-stationary Functions	2014	Jasper Snoek Kevin Swersky Richard S. Zemel Ryan P. Adams	3
+	A Tutorial on Bayesian Optimization	2018	Peter I. Frazier	3
+	On inference for partially observed nonlinear diffusion models using the Metropolis-Hastings algorithm	2001	Gareth O. Roberts	3
+	Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start	2017	Valerio Perrone Rodolphe Jenatton Matthias Seeger Cédric Archambeau	3
+	Decoupled Weight Decay Regularization	2017	Ilya Loshchilov Frank Hutter	3
+	A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants	1998	Radford M. Neal Geoffrey E. Hinton	3
+	Robust principal component analysis by self-organizing rules based on statistical physics approach	1995	Lei Xu Alan Yuille	3
+	On the distribution of points in a cube and the approximate evaluation of integrals	1967	I. M. Sobol	3
+	Accelerated Monte Carlo for Optimal Estimation of Time Series	2005	Francis J. Alexander Gregory L. Eyink Juan M. Restrepo	3
+	Equality of Opportunity in Supervised Learning	2016	Moritz Hardt Eric Price Nathan Srebro	3
+	Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions	2002	Shy Shoham	3
+	Multi-fidelity Bayesian Optimisation with Continuous Approximations	2017	Kirthevasan Kandasamy Gautam Dasarathy Jeff Schneider Barnabás Póczos	3
+	Model-based Asynchronous Hyperparameter and Neural Architecture Search	2020	Aaron Klein Louis C. Tiao Thibaut Lienart Cédric Archambeau Matthias Seeger	3
+	Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)	2015	Djork-Arné Clevert Thomas Unterthiner Sepp Hochreiter	2
+	None	1997	Rainer Storn Kenneth V. Price	2
+	Heteroscedastic Treed Bayesian Optimisation	2014	John-Alexander M. Assael Ziyu Wang Nando de Freitas	2
+ PDF Chat	SMOTE: Synthetic Minority Over-sampling Technique	2002	Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Philip Kegelmeyer	2
+ PDF Chat	Likelihood Inference for Discretely Observed Nonlinear Diffusions	2001	Ola Elerian Siddhartha Chib Neil Shephard	2
+	Principal Component Analysis	1988	Colin Goodall Ian T. Jolliffe	2
+ PDF Chat	Bayesian inference for nonlinear multivariate diffusion models observed with error	2007	Andrew Golightly Darren J. Wilkinson	2
+	ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES	1933	W. R THOMPSON	2