Cédric Archambeau

109 papers A* 17A 9B 7C 2Misc 1Journal 55Unranked 17

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Hyperparameter Optimization in Machine Learning. Found. Trends Mach. Learn. Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2025	J	`jnl`	SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling. CoRR Yasin Almalioglu, Andrzej Kucik, Geoffrey French, Dafni Antotsiou, Alexander Adam, Cédric Archambeau
2024	A*	`conf`	Explaining Probabilistic Models with Distributional Values. ICML Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
2024	J	`jnl`	Explaining Probabilistic Models with Distributional Values. CoRR Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
2024	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2024	J	`jnl`	Hyperparameter Optimization in Machine Learning. CoRR Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2024	J	`jnl`	On the Choice of Learning Rate for Local SGD. Trans. Mach. Learn. Res. Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau
2024	J	`jnl`	Structural Pruning of Pre-trained Language Models via Neural Architecture Search. CoRR Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau
2024	J	`jnl`	Structural Pruning of Pre-trained Language Models via Neural Architecture Search. Trans. Mach. Learn. Res. Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cédric Archambeau
2023	J	`jnl`	A Negative Result on Gradient Matching for Selective Backprop. CoRR Lukas Balles, Cédric Archambeau, Giovanni Zappella
2023	—	`conf`	Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger
2023	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2023	—	`conf`	Geographical Erasure in Language Generation. EMNLP (Findings) Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
2023	J	`jnl`	Geographical Erasure in Language Generation. CoRR Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
2023	A*	`conf`	Optimizing Hyperparameters with Conformal Quantile Regression. ICML David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
2023	J	`jnl`	Optimizing Hyperparameters with Conformal Quantile Regression. CoRR David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
2023	A*	`conf`	PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
2023	J	`jnl`	Renate: A Library for Real-World Continual Learning. CoRR Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella
2022	—	`conf`	Automatic Termination for Hyperparameter Optimization. AutoML Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
2022	—	`conf`	Continual Learning with Transformers for Image Classification. CVPR Workshops Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
2022	J	`jnl`	Continual Learning with Transformers for Image Classification. CoRR Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
2022	J	`jnl`	Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau
2022	J	`jnl`	Gradient-Matching Coresets for Rehearsal-Based Continual Learning. CoRR Lukas Balles, Giovanni Zappella, Cédric Archambeau
2022	J	`jnl`	Memory Efficient Continual Learning for Neural Text Classification. CoRR Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau
2022	A*	`conf`	Memory Efficient Continual Learning with Transformers. NeurIPS Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
2022	J	`jnl`	PASHA: Efficient HPO with Progressive Resource Allocation. CoRR Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
2022	A*	`conf`	Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
2022	J	`jnl`	Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
2022	—	`conf`	Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
2022	J	`jnl`	Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors. CoRR Gianluca Detommaso, Alberto Gasparin, Andrew Wilson, Cédric Archambeau
2021	J	`jnl`	A multi-objective perspective on jointly tuning hardware and hyperparameters. CoRR David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau
2021	J	`jnl`	A resource-efficient method for repeated HPO and NAS problems. CoRR Giovanni Zappella, David Salinas, Cédric Archambeau
2021	A*	`conf`	Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD 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
2021	A*	`conf`	BORE: Bayesian Optimization by Density-Ratio Estimation. ICML Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
2021	J	`jnl`	BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
2021	C	`conf`	Fair Bayesian Optimization. AIES Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau
2021	J	`jnl`	Gradient-matching coresets for continual learning. CoRR Lukas Balles, Giovanni Zappella, Cédric Archambeau
2021	A	`conf`	Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
2021	J	`jnl`	Hyperparameter Transfer Learning with Adaptive Complexity. CoRR Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
2021	J	`jnl`	Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau
2021	J	`jnl`	More Than Words: Towards Better Quality Interpretations of Text Classifiers. CoRR Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi
2021	J	`jnl`	Multi-objective Asynchronous Successive Halving. CoRR Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
2021	—	`conf`	On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
2021	J	`jnl`	On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
2021	J	`jnl`	Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. CoRR Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
2021	A	`conf`	Towards robust episodic meta-learning. UAI Beyza Ermis, Giovanni Zappella, Cédric Archambeau
2020	J	`jnl`	Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR 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
2020	—	`conf`	Amazon SageMaker Autopilot: a white box AutoML solution at scale. DEEM@SIGMOD 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
2020	J	`jnl`	Amazon SageMaker Autopilot: a white box AutoML solution at scale. CoRR 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
2020	J	`jnl`	Cost-aware Bayesian Optimization. CoRR Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger
2020	J	`jnl`	Fair Bayesian Optimization. CoRR Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau
2020	A*	`conf`	LEEP: A New Measure to Evaluate Transferability of Learned Representations. ICML Cuong V. Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau
2020	J	`jnl`	LEEP: A New Measure to Evaluate Transferability of Learned Representations. CoRR Cuong V. Nguyen, Tal Hassner, Cédric Archambeau, Matthias W. Seeger
2020	J	`jnl`	Model-based Asynchronous Hyperparameter Optimization. CoRR Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger
2020	J	`jnl`	Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. CoRR Gauthier Guinet, Valerio Perrone, Cédric Archambeau
2019	J	`jnl`	Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger
2019	J	`jnl`	Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton
2018	A*	`conf`	Scalable Hyperparameter Transfer Learning. NeurIPS Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau
2017	J	`jnl`	An interpretable latent variable model for attribute applicability in the Amazon catalogue. CoRR Tammo Rukat, Dustin Lange, Cédric Archambeau
2017	A*	`conf`	Bayesian Optimization with Tree-structured Dependencies. ICML Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger
2016	A*	`conf`	Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. ICML Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
2016	A*	`conf`	Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation. KDD Jim C. Huang, Rodolphe Jenatton, Cédric Archambeau
2016	J	`jnl`	Online optimization and regret guarantees for non-additive long-term constraints. CoRR Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
2015	J	`jnl`	Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. CoRR Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
2015	J	`jnl`	Latent IBP Compound Dirichlet Allocation. IEEE Trans. Pattern Anal. Mach. Intell. Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard
2015	A*	`conf`	One-Pass Ranking Models for Low-Latency Product Recommendations. KDD Antonino Freno, Martin Saveski, Rodolphe Jenatton, Cédric Archambeau
2015	J	`jnl`	Online Inference for Relation Extraction with a Reduced Feature Set. CoRR Maxim Rabinovich, Cédric Archambeau
2014	J	`jnl`	Overlapping Trace Norms in Multi-View Learning. CoRR Behrouz Behmardi, Cédric Archambeau, Guillaume Bouchard
2014	A*	`conf`	Towards crowd-based customer service: a mixed-initiative tool for managing Q&A sites. CHI Tiziano Piccardi, Gregorio Convertino, Massimo Zancanaro, Ji Wang, Cédric Archambeau
2013	—	`conf`	Bringing Representativeness into Social Media Monitoring and Analysis. HICSS Michael Kaschesky, Pawel Sobkowicz, José Miguel Hernández-Lobato, Guillaume Bouchard, Cédric Archambeau, Nicolas Scharioth, Robert Manchin, Adrian Gschwend, Reinhard Riedl
2013	A	`conf`	Connecting comments and tags: improved modeling of social tagging systems. WSDM Dawei Yin, Shengbo Guo, Boris Chidlovskii, Brian D. Davison, Cédric Archambeau, Guillaume Bouchard
2013	—	`conf`	Error Prediction with Partial Feedback. ECML/PKDD (2) William Darling, Cédric Archambeau, Shachar Mirkin, Guillaume Bouchard
2013	A*	`conf`	Structured Penalties for Log-Linear Language Models. EMNLP Anil Kumar Nelakanti, Cédric Archambeau, Julien Mairal, Francis R. Bach, Guillaume Bouchard
2012	A	`conf`	Plackett-Luce regression: A new Bayesian model for polychotomous data. UAI Cédric Archambeau, Francois Caron
2012	J	`jnl`	Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions. Comput. Stat. Yuan Shen, Dan Cornford, Manfred Opper, Cédric Archambeau
2011	—	`conf`	Mail2Wiki: low-cost sharing and early curation from email to wikis. C&T Benjamin V. Hanrahan, Guillaume Bouchard, Gregorio Convertino, Thiébaud Weksteen, Nicholas Kong, Cédric Archambeau, Ed H. Chi
2011	A	`conf`	Mail2Wiki: posting and curating Wiki content from email. IUI Benjamin V. Hanrahan, Thiébaud Weksteen, Nicholas Kong, Gregorio Convertino, Guillaume Bouchard, Cédric Archambeau, Ed H. Chi
2011	A	`conf`	Robust Bayesian Matrix Factorisation. AISTATS Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau
2011	—	`conf`	Sparse Bayesian Multi-Task Learning. NIPS Cédric Archambeau, Shengbo Guo, Onno Zoeter
2011	J	`jnl`	The sequence memoizer. Commun. ACM Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh
2010	J	`jnl`	A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. J. Signal Process. Syst. Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Louis Barillec
2009	A*	`conf`	A stochastic memoizer for sequence data. ICML Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh
2009	J	`jnl`	Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinform. Stefano Lise, Cédric Archambeau, Massimiliano Pontil, David T. Jones
2009	J	`jnl`	Switching regulatory models of cellular stress response. Bioinform. Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau
2009	J	`jnl`	The Variational Gaussian Approximation Revisited. Neural Comput. Manfred Opper, Cédric Archambeau
2008	B	`conf`	Improving the Robustness to Outliers of Mixtures of Probabilistic PCAs. PAKDD Nicolas Delannay, Cédric Archambeau, Michel Verleysen
2008	J	`jnl`	Mixtures of robust probabilistic principal component analyzers. Neurocomputing Cédric Archambeau, Nicolas Delannay, Michel Verleysen
2008	—	`conf`	Sparse probabilistic projections. NIPS Cédric Archambeau, Francis R. Bach
2008	A	`conf`	Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages. CHES François-Xavier Standaert, Cédric Archambeau
2007	—	`conf`	Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor
2007	B	`conf`	Mixtures of robust probabilistic principal component analyzers. ESANN Cédric Archambeau, Nicolas Delannay, Michel Verleysen
2007	J	`jnl`	Robust Bayesian clustering. Neural Networks Cédric Archambeau, Michel Verleysen
2007	J	`jnl`	Towards Security Limits in Side-Channel Attacks. IACR Cryptol. ePrint Arch. François-Xavier Standaert, Eric Peeters, Cédric Archambeau, Jean-Jacques Quisquater
2007	—	`conf`	Variational Inference for Diffusion Processes. NIPS Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor
2006	C	`conf`	Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures. ISCAS Cédric Archambeau, Maurizio Valle, Alex Assenza, Michel Verleysen
2006	—	`conf`	Automatic Adjustment of Discriminant Adaptive Nearest Neighbor. ICPR (2) Nicolas Delannay, Cédric Archambeau, Michel Verleysen
2006	A*	`conf`	Robust probabilistic projections. ICML Cédric Archambeau, Nicolas Delannay, Michel Verleysen
2006	A	`conf`	Template Attacks in Principal Subspaces. CHES Cédric Archambeau, Eric Peeters, François-Xavier Standaert, Jean-Jacques Quisquater
2006	A	`conf`	Towards Security Limits in Side-Channel Attacks. CHES François-Xavier Standaert, Eric Peeters, Cédric Archambeau, Jean-Jacques Quisquater
2005	Misc	`conf`	Manifold Constrained Finite Gaussian Mixtures. IWANN Cédric Archambeau, Michel Verleysen
2005	—	`conf`	Manifold Constrained Variational Mixtures. ICANN (2) Cédric Archambeau, Michel Verleysen
2005	—	`—`	Probabilistic models in noisy environments : and their application to a visual prosthesis for the blind/. Cédric Archambeau
2004	B	`conf`	Flexible and Robust Bayesian Classification by Finite Mixture Models. ESANN Cédric Archambeau, Frédéric Vrins, Michel Verleysen
2004	J	`jnl`	Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind. Artif. Intell. Medicine Cédric Archambeau, Jean Delbeke, Claude Veraart, Michel Verleysen
2004	—	`conf`	Supervised Nonparametric Information Theoretic Classification. ICPR (3) Cédric Archambeau, Torsten Butz, Vlad Popovici, Michel Verleysen, Jean-Philippe Thiran
2004	B	`conf`	Towards a Local Separation Performances Estimator Using Common ICA Contrast Functions? ESANN Frédéric Vrins, Cédric Archambeau, Michel Verleysen
2003	B	`conf`	Locally Linear Embedding versus Isotop. ESANN John Aldo Lee, Cédric Archambeau, Michel Verleysen
2003	B	`conf`	On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures. ESANN Cédric Archambeau, John Aldo Lee, Michel Verleysen
2002	B	`conf`	Width optimization of the Gaussian kernels in Radial Basis Function Networks. ESANN Nabil Benoudjit, Cédric Archambeau, Amaury Lendasse, John Aldo Lee, Michel Verleysen