Archambeau, Cedric

34 publications

FnTML 2025 Hyperparameter Optimization in Machine Learning Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi

ICML 2024 Explaining Probabilistic Models with Distributional Values Luca Franceschi, Michele Donini, Cedric Archambeau, Matthias Seeger

MLOSS 2024 Fortuna: A Library for Uncertainty Quantification in Deep Learning Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau

TMLR 2024 On the Choice of Learning Rate for Local SGD Lukas Balles, Prabhu Teja S, Cedric Archambeau

TMLR 2024 Structural Pruning of Pre-Trained Language Models via Neural Architecture Search Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cedric Archambeau

NeurIPSW 2023 A Negative Result on Gradient Matching for Selective Backprop Lukas Balles, Cedric Archambeau, Giovanni Zappella

ICLRW 2023 Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cedric Archambeau, Tamas Madl, Michele Donini, Matthias Seeger

ICML 2023 Optimizing Hyperparameters with Conformal Quantile Regression David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau

ICLR 2023 PASHA: Efficient HPO and NAS with Progressive Resource Allocation Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cedric Archambeau, Giovanni Zappella

AutoML 2022 Automatic Termination for Hyperparameter Optimization Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau

CVPRW 2022 Continual Learning with Transformers for Image Classification Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau

NeurIPSW 2022 Differentially Private Gradient Boosting on Linear Learners for Tabular Data Saeyoung Rho, Cedric Archambeau, Sergul Aydore, Beyza Ermis, Michael Kearns, Aaron Roth, Shuai Tang, Yu-Xiang Wang, Steven Wu

NeurIPS 2022 Memory Efficient Continual Learning with Transformers Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cedric Archambeau

NeurIPS 2022 Private Synthetic Data for Multitask Learning and Marginal Queries Giuseppe Vietri, Cedric Archambeau, Sergul Aydore, William Brown, Michael J. Kearns, Aaron Roth, Ankit Siva, Shuai Tang, Steven Z. Wu

AutoML 2022 Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cedric Archambeau

AISTATS 2021 Hyperparameter Transfer Learning with Adaptive Complexity Samuel Horváth, Aaron Klein, Peter Richtarik, Cedric Archambeau

ICMLW 2021 A Resource-Efficient Method for Repeated HPO and NAS Problems Giovanni Zappella, David Salinas, Cedric Archambeau

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

ICMLW 2021 Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio Julien Niklas Siems, Aaron Klein, Cedric Archambeau, Maren Mahsereci

NeurIPSW 2021 Gradient-Matching Coresets for Continual Learning Lukas Balles, Giovanni Zappella, Cedric Archambeau

UAI 2021 Towards Robust Episodic Meta-Learning Beyza Ermis, Giovanni Zappella, Cédric Archambeau

ICML 2020 LEEP: A New Measure to Evaluate Transferability of Learned Representations Cuong Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau

NeurIPS 2019 Learning Search Spaces for Bayesian Optimization: Another View of Hyperparameter Transfer Learning Valerio Perrone, Huibin Shen, Matthias W Seeger, Cedric Archambeau, Rodolphe Jenatton

NeurIPS 2018 Scalable Hyperparameter Transfer Learning Valerio Perrone, Rodolphe Jenatton, Matthias W Seeger, Cedric Archambeau

ICML 2017 Bayesian Optimization with Tree-Structured Dependencies Rodolphe Jenatton, Cedric Archambeau, Javier González, Matthias Seeger

ICML 2016 Adaptive Algorithms for Online Convex Optimization with Long-Term Constraints Rodolphe Jenatton, Jim Huang, Cedric Archambeau

ECML-PKDD 2013 Error Prediction with Partial Feedback William Darling, Cédric Archambeau, Shachar Mirkin, Guillaume Bouchard

UAI 2012 Plackett-Luce Regression: A New Bayesian Model for Polychotomous Data Cédric Archambeau, Francois Caron

AISTATS 2011 Robust Bayesian Matrix Factorisation Balaji Lakshminarayanan, Guillaume Bouchard, Cedric Archambeau

NeurIPS 2011 Sparse Bayesian Multi-Task Learning Shengbo Guo, Onno Zoeter, Cédric Archambeau

ICML 2009 A Stochastic Memoizer for Sequence Data Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh

NeurIPS 2008 Sparse Probabilistic Projections Cédric Archambeau, Francis R. Bach

NeurIPS 2007 Variational Inference for Diffusion Processes Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John S. Shawe-taylor

ICML 2006 Robust Probabilistic Projections Cédric Archambeau, Nicolas Delannay, Michel Verleysen