Florence d'Alché-Buc

According to our database, Florence d'Alché-Buc authored at least 85 papers between 1994 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance.

Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc

Trans. Mach. Learn. Res., 2024

A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions.

Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc

Trans. Mach. Learn. Res., 2024

Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization.

Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc

IEEE ACM Trans. Audio Speech Lang. Process., 2024

Learning Differentiable Surrogate Losses for Structured Prediction.

Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc

CoRR, 2024

Restyling Unsupervised Concept Based Interpretable Networks with Generative Models.

Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc

CoRR, 2024

End-to-end Supervised Prediction of Arbitrary-size Graphs with Partially-Masked Fused Gromov-Wasserstein Matching.

Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau

CoRR, 2024

Deep Sketched Output Kernel Regression for Structured Prediction.

Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels.

Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches.

Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc

Trans. Mach. Learn. Res., 2023

Anomaly component analysis.

Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc

CoRR, 2023

Fast kernel half-space depth for data with non-convex supports.

Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc, Hicham Janati

CoRR, 2023

Tailoring Mixup to Data using Kernel Warping functions.

Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc

CoRR, 2023

Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance.

Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc

CoRR, 2023

2022

Vector-Valued Least-Squares Regression under Output Regularity Assumptions.

Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc

J. Mach. Learn. Res., 2022

p-Sparsified Sketches for Fast Multiple Output Kernel Methods.

Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc

CoRR, 2022

Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection.

Dimitri Bouche, Rémi Flamary, Florence d'Alché-Buc, Riwal Plougonven, Marianne Clausel, Jordi Badosa, Philippe Drobinski

CoRR, 2022

Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF.

Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard

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

Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses.

Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc

Proceedings of the International Conference on Machine Learning, 2022

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters.

Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc

Proceedings of the International Conference on Machine Learning, 2022

Interpretable Generative Modeling Using a Hierarchical Topological VAE.

Etienne Desticourt, Véronique Letort, Florence d'Alché-Buc

Proceedings of the International Conference on Computational Science and Computational Intelligence, 2022

2021

Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program).

Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Hugo Larochelle

J. Mach. Learn. Res., 2021

Depth-based pseudo-metrics between probability distributions.

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc

CoRR, 2021

Emotion Transfer Using Vector-Valued Infinite Task Learning.

Alex Lambert, Sanjeel Parekh, Zoltán Szabó, Florence d'Alché-Buc

CoRR, 2021

A Framework to Learn with Interpretation.

Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

When OT meets MoM: Robust estimation of Wasserstein Distance.

Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc

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

Nonlinear Functional Output Regression: A Dictionary Approach.

Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc

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

2020

Learning Output Embeddings in Structured Prediction.

Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc

CoRR, 2020

Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach.

Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh

CoRR, 2020

Multilingual lyrics-to-audio alignment.

Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc

Proceedings of the 21th International Society for Music Information Retrieval Conference, 2020

Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses.

Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alché-Buc

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

Audio-Based Detection of Explicit Content in Music.

Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Identifying the "right" level of explanation in a given situation.

Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh

Proceedings of the First International Workshop on New Foundations for Human-Centered AI (NeHuAI) co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), 2020

2019

On the Dualization of Operator-Valued Kernel Machines.

Pierre Laforgue, Alex Lambert, Luc Motte, Florence d'Alché-Buc

CoRR, 2019

A multimodal movie review corpus for fine-grained opinion mining.

Alexandre Garcia, Slim Essid, Florence d'Alché-Buc, Chloé Clavel

CoRR, 2019

From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining.

Alexandre Garcia, Pierre Colombo, Florence d'Alché-Buc, Slim Essid, Chloé Clavel

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Autoencoding any Data through Kernel Autoencoders.

Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Infinite Task Learning in RKHSs.

Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Functional Isolation Forest.

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc

Proceedings of The 11th Asian Conference on Machine Learning, 2019

2018

Output Fisher embedding regression.

Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d'Alché-Buc

Mach. Learn., 2018

Infinite-Task Learning with Vector-Valued RKHSs.

Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc

CoRR, 2018

A Structured Prediction Approach for Label Ranking.

Anna Korba, Alexandre Garcia, Florence d'Alché-Buc

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

Structured Output Learning with Abstention: Application to Accurate Opinion Prediction.

Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d'Alché-Buc

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison.

Blandine Romain, Laurence Rouet, Daniel Ohayon, Olivier Lucidarme, Florence d'Alché-Buc, Véronique Letort

Medical Image Anal., 2017

Data sparse nonparametric regression with ε-insensitive losses.

Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc

Proceedings of The 9th Asian Conference on Machine Learning, 2017

2016

Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels.

Céline Brouard, Marie Szafranski, Florence d'Alché-Buc

J. Mach. Learn. Res., 2016

Fast metabolite identification with Input Output Kernel Regression.

Céline Brouard, Huibin Shen, Kai Dührkop, Florence d'Alché-Buc, Sebastian Böcker, Juho Rousu

Bioinform., 2016

Joint quantile regression in vector-valued RKHSs.

Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Random Fourier Features For Operator-Valued Kernels.

Romain Brault, Markus Heinonen, Florence d'Alché-Buc

Proceedings of The 8th Asian Conference on Machine Learning, 2016

2015

A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

Adriana Birlutiu, Florence d'Alché-Buc, Tom Heskes

IEEE ACM Trans. Comput. Biol. Bioinform., 2015

Operator-valued kernel-based vector autoregressive models for network inference.

Néhémy Lim, Florence d'Alché-Buc, Cédric Auliac, George Michailidis

Mach. Learn., 2015

Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction.

Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alché-Buc

Bioinform., 2015

2014

Learning nonparametric differential equations with operator-valued kernels and gradient matching.

Markus Heinonen, Florence d'Alché-Buc

CoRR, 2014

Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach.

Artémis Llamosi, Adel Mezine, Florence d'Alché-Buc, Véronique Letort, Michèle Sebag

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

2013

Learning a Markov Logic network for supervised gene regulatory network inference.

Céline Brouard, Christel Vrain, Julie Dubois, David Castel, Marie-Anne Debily, Florence d'Alché-Buc

BMC Bioinform., 2013

OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.

Néhémy Lim, Yasin Senbabaoglu, George Michailidis, Florence d'Alché-Buc

Bioinform., 2013

A Multi-task Learning Approach for Compartmental Model Parameter Estimation in DCE-CT Sequences.

Blandine Romain, Véronique Letort, Olivier Lucidarme, Laurence Rouet, Florence d'Alché-Buc

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 2013

Inférence de réseaux biologiques : un défi pour la fouille de données structurées.

Florence d'Alché-Buc

Proceedings of the Extraction et gestion des connaissances (EGC'2013), Actes, 29 janvier, 2013

2012

Optimisation of reconstruction for the registration of CT liver perfusion sequences.

Blandine Romain, Véronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet

Proceedings of the Medical Imaging 2012: Image Processing, 2012

Registration of Free-Breathing Abdominal 3D Contrast-Enhanced CT.

Blandine Romain, Véronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet

Proceedings of the Abdominal Imaging. Computational and Clinical Applications, 2012

2011

Semi-supervised Penalized Output Kernel Regression for Link Prediction.

Céline Brouard, Florence d'Alché-Buc, Marie Szafranski

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks.

Nicolas Brunel, Florence d'Alché-Buc

Proceedings of the Pattern Recognition in Bioinformatics, 2010

Estimation of Parametric Nonlinear ODEs for Biological Networks Identification.

Florence d'Alché-Buc, Nicolas Brunei

Proceedings of the Learning and Inference in Computational Systems Biology., 2010

2009

CycSim - an online tool for exploring and experimenting with genome-scale metabolic models.

François Le Fèvre, Serge Smidtas, Cyril Combe, Maxime Durot, Florence d'Alché-Buc, Vincent Schächter

Bioinform., 2009

2008

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset.

Cédric Auliac, Vincent Frouin, Xavier Gidrol, Florence d'Alché-Buc

BMC Bioinform., 2008

2007

Inferring biological networks with output kernel trees.

Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc

BMC Bioinform., 2007

Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference.

Minh Quach, Nicolas Brunel, Florence d'Alché-Buc

Bioinform., 2007

Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching.

Cédric Auliac, Florence d'Alché-Buc, Vincent Frouin

Proceedings of the Applications of Fuzzy Sets Theory, 2007

Gradient boosting for kernelized output spaces.

Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc

Proceedings of the Machine Learning, 2007

2006

Kernelizing the output of tree-based methods.

Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc

Proceedings of the Machine Learning, 2006

2005

Time series filtering, smoothing and learning using the kernel Kalman filter.

Liva Ralaivola, Florence d'Alché-Buc

Proceedings of the IEEE International Joint Conference on Neural Networks, 2005

2003

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction.

Liva Ralaivola, Florence d'Alché-Buc

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Evaluation of Topographic Clustering and Its Kernelization.

Marie-Jeanne Lesot, Florence d'Alché-Buc, George Siolas

Proceedings of the Machine Learning: ECML 2003, 2003

Gene networks inference using dynamic Bayesian networks.

Bruno-Edouard Perrin, Liva Ralaivola, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, Florence d'Alché-Buc

Proceedings of the European Conference on Computational Biology (ECCB 2003), 2003

2002

Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling.

George Siolas, Florence d'Alché-Buc

Proceedings of the Artificial Neural Networks, 2002

2001

Semi-supervised MarginBoost.

Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Incremental Support Vector Machine Learning: A Local Approach.

Liva Ralaivola, Florence d'Alché-Buc

Proceedings of the Artificial Neural Networks, 2001

Boosting Mixture Models for Semi-supervised Learning.

Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise

Proceedings of the Artificial Neural Networks, 2001

2000

Support Vector Machines Based on a Semantic Kernel for Text Categorization.

George Siolas, Florence d'Alché-Buc

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

1999

Discharge Prediction of Rechargeable Batteries with Neural Networks.

Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc

Integr. Comput. Aided Eng., 1999

1997

Optimal Linear Regression on Classifier Outputs.

Yann Guermeur, Florence d'Alché-Buc, Patrick Gallinari

Proceedings of the Artificial Neural Networks, 1997

Neural Network Adaptive Modeling of Battery Discharge Behavior.

Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc

Proceedings of the Artificial Neural Networks, 1997

1995

Asymptotic performances of a constructive algorithm.

Florence d'Alché-Buc, Jean-Pierre Nadal

Neural Process. Lett., 1995

Méthodes constructives pour l'apprentissage à partir d'exemples : les arbres neuronaux hybrides et leur comportement asymptotique.

Florence d'Alché-Buc, Jean-Pierre Nadal

Monde des Util. Anal. Données, 1995

1994

Trio Learning: A New Strategy for Building Hybrid Neural Trees.

Florence d'Alché-Buc, Didier Zwierski, Jean-Pierre Nadal

Int. J. Neural Syst., 1994

Rule Extraction with Fuzzy Neural Network.

Florence d'Alché-Buc, Vincent Andrés, Jean-Pierre Nadal

Int. J. Neural Syst., 1994