Florence d'Alché-Buc

Publication Activity (10 Years)

Years Active: 1994-2024
Publications (10 Years): 63

Top Topics

Matrix Factorization

Kernel Regression

Machine Learning

Structured Prediction

Top Venues

Publications

Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau
End-to-end Supervised Prediction of Arbitrary-size Graphs with Partially-Masked Fused Gromov-Wasserstein Matching. CoRR (2024)
Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. CoRR (2024)
Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance. Trans. Mach. Learn. Res. 2024 (2024)
Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels. AISTATS (2024)
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc
Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization. IEEE ACM Trans. Audio Speech Lang. Process. 32 (2024)
Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc
Deep Sketched Output Kernel Regression for Structured Prediction. CoRR (2024)
Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. Trans. Mach. Learn. Res. 2024 (2024)
Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance. CoRR (2023)
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization. CoRR (2023)
Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels. CoRR (2023)
Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc, Hicham Janati
Fast kernel half-space depth for data with non-convex supports. CoRR (2023)
Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc
Anomaly component analysis. CoRR (2023)
Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
Tailoring Mixup to Data using Kernel Warping functions. CoRR (2023)
Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc
Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches. Trans. Mach. Learn. Res. 2023 (2023)
Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. CoRR (2022)
Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc
Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. ICML (2022)
Dimitri Bouche, Rémi Flamary, Florence d'Alché-Buc, Riwal Plougonven, Marianne Clausel, Jordi Badosa, Philippe Drobinski
Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection. CoRR (2022)
Etienne Desticourt, Véronique Letort, Florence d'Alché-Buc
Interpretable Generative Modeling Using a Hierarchical Topological VAE. CSCI (2022)
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Vector-Valued Least-Squares Regression under Output Regularity Assumptions. CoRR (2022)
Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. ICML (2022)
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Vector-Valued Least-Squares Regression under Output Regularity Assumptions. J. Mach. Learn. Res. 23 (2022)
Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc
p-Sparsified Sketches for Fast Multiple Output Kernel Methods. CoRR (2022)
Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc
Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. CoRR (2022)
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. NeurIPS (2022)
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. CoRR (2022)
Alex Lambert, Sanjeel Parekh, Zoltán Szabó, Florence d'Alché-Buc
Emotion Transfer Using Vector-Valued Infinite Task Learning. CoRR (2021)
Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
When OT meets MoM: Robust estimation of Wasserstein Distance. AISTATS (2021)
Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc
Nonlinear Functional Output Regression: A Dictionary Approach. AISTATS (2021)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
Depth-based pseudo-metrics between probability distributions. CoRR (2021)
Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
A Framework to Learn with Interpretation. NeurIPS (2021)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Hugo Larochelle
Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program). J. Mach. Learn. Res. 22 (2021)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Hugo Larochelle
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). CoRR (2020)
Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
When OT meets MoM: Robust estimation of Wasserstein Distance. CoRR (2020)
Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh
Identifying the "right" level of explanation in a given situation. NeHuAI@ECAI (2020)
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc
Audio-Based Detection of Explicit Content in Music. ICASSP (2020)
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc
Multilingual lyrics-to-audio alignment. ISMIR (2020)
Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alché-Buc
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses. ICML (2020)
Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach. CoRR (2020)
Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc
Nonlinear Functional Output Regression: a Dictionary Approach. CoRR (2020)
Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
A Framework to Learn with Interpretation. CoRR (2020)
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
Learning Output Embeddings in Structured Prediction. CoRR (2020)
Alexandre Garcia, Pierre Colombo, Slim Essid, Florence d'Alché-Buc, Chloé Clavel
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining. CoRR (2019)
Pierre Laforgue, Alex Lambert, Luc Motte, Florence d'Alché-Buc
On the Dualization of Operator-Valued Kernel Machines. CoRR (2019)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
Functional Isolation Forest. ACML (2019)
Alexandre Garcia, Slim Essid, Florence d'Alché-Buc, Chloé Clavel
A multimodal movie review corpus for fine-grained opinion mining. CoRR (2019)
Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc
Autoencoding any Data through Kernel Autoencoders. AISTATS (2019)
Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
Functional Isolation Forest. CoRR (2019)
Alexandre Garcia, Pierre Colombo, Florence d'Alché-Buc, Slim Essid, Chloé Clavel
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining. EMNLP/IJCNLP (1) (2019)
Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc
Infinite Task Learning in RKHSs. AISTATS (2019)
Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d'Alché-Buc
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. ICML (2018)
Alexandre Garcia, Slim Essid, Chloé Clavel, Florence d'Alché-Buc
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. CoRR (2018)
Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc
Infinite-Task Learning with Vector-Valued RKHSs. CoRR (2018)
Anna Korba, Alexandre Garcia, Florence d'Alché-Buc
A Structured Prediction Approach for Label Ranking. CoRR (2018)
Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc
Autoencoding any Data through Kernel Autoencoders. CoRR (2018)
Anna Korba, Alexandre Garcia, Florence d'Alché-Buc
A Structured Prediction Approach for Label Ranking. NeurIPS (2018)
Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d'Alché-Buc
Output Fisher embedding regression. Mach. Learn. 107 (8-10) (2018)
Blandine Romain, Laurence Rouet, Daniel Ohayon, Olivier Lucidarme, Florence d'Alché-Buc, Véronique Letort
Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison. Medical Image Anal. 35 (2017)
Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc
Data sparse nonparametric regression with ε-insensitive losses. ACML (2017)
Céline Brouard, Marie Szafranski, Florence d'Alché-Buc
Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels. J. Mach. Learn. Res. 17 (2016)
Céline Brouard, Huibin Shen, Kai Dührkop, Florence d'Alché-Buc, Sebastian Böcker, Juho Rousu
Fast metabolite identification with Input Output Kernel Regression. Bioinform. 32 (12) (2016)
Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc
Joint quantile regression in vector-valued RKHSs. NIPS (2016)
Romain Brault, Markus Heinonen, Florence d'Alché-Buc
Random Fourier Features For Operator-Valued Kernels. ACML (2016)
Romain Brault, Florence d'Alché-Buc, Markus Heinonen
Random Fourier Features for Operator-Valued Kernels. CoRR (2016)
Adriana Birlutiu, Florence d'Alché-Buc, Tom Heskes
A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions. IEEE ACM Trans. Comput. Biol. Bioinform. 12 (3) (2015)
Néhémy Lim, Florence d'Alché-Buc, Cédric Auliac, George Michailidis
Operator-valued kernel-based vector autoregressive models for network inference. Mach. Learn. 99 (3) (2015)
Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alché-Buc
Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinform. 31 (5) (2015)
Artémis Llamosi, Adel Mezine, Florence d'Alché-Buc, Véronique Letort, Michèle Sebag
Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach. ECML/PKDD (2) (2014)
Markus Heinonen, Florence d'Alché-Buc
Learning nonparametric differential equations with operator-valued kernels and gradient matching. CoRR (2014)
Blandine Romain, Véronique Letort, Olivier Lucidarme, Laurence Rouet, Florence d'Alché-Buc
A Multi-task Learning Approach for Compartmental Model Parameter Estimation in DCE-CT Sequences. MICCAI (2) (2013)
Céline Brouard, Christel Vrain, Julie Dubois, David Castel, Marie-Anne Debily, Florence d'Alché-Buc
Learning a Markov Logic network for supervised gene regulatory network inference. BMC Bioinform. 14 (2013)
Néhémy Lim, Yasin Senbabaoglu, George Michailidis, Florence d'Alché-Buc
OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks. Bioinform. 29 (11) (2013)
Florence d'Alché-Buc
Inférence de réseaux biologiques : un défi pour la fouille de données structurées. EGC (2013)
Blandine Romain, Véronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet
Optimisation of reconstruction for the registration of CT liver perfusion sequences. Medical Imaging: Image Processing (2012)
Blandine Romain, Véronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet
Registration of Free-Breathing Abdominal 3D Contrast-Enhanced CT. Abdominal Imaging (2012)
Céline Brouard, Florence d'Alché-Buc, Marie Szafranski
Semi-supervised Penalized Output Kernel Regression for Link Prediction. ICML (2011)
Nicolas Brunel, Florence d'Alché-Buc
Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks. PRIB (2010)
François Le Fèvre, Serge Smidtas, Cyril Combe, Maxime Durot, Florence d'Alché-Buc, Vincent Schächter
CycSim - an online tool for exploring and experimenting with genome-scale metabolic models. Bioinform. 25 (15) (2009)
Cédric Auliac, Vincent Frouin, Xavier Gidrol, Florence d'Alché-Buc
Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset. BMC Bioinform. 9 (2008)
Minh Quach, Nicolas Brunel, Florence d'Alché-Buc
Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinform. 23 (23) (2007)
Cédric Auliac, Florence d'Alché-Buc, Vincent Frouin
Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching. WILF (2007)
Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
Gradient boosting for kernelized output spaces. ICML (2007)
Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc
Inferring biological networks with output kernel trees. BMC Bioinform. 8 (S-2) (2007)
Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
Kernelizing the output of tree-based methods. ICML (2006)
Bruno-Edouard Perrin, Liva Ralaivola, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, Florence d'Alché-Buc
Gene networks inference using dynamic Bayesian networks. ECCB (2003)
Liva Ralaivola, Florence d'Alché-Buc
Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. NIPS (2003)
Marie-Jeanne Lesot, Florence d'Alché-Buc, George Siolas
Evaluation of Topographic Clustering and Its Kernelization. ECML (2003)
George Siolas, Florence d'Alché-Buc
Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling. ICANN (2002)
Liva Ralaivola, Florence d'Alché-Buc
Incremental Support Vector Machine Learning: A Local Approach. ICANN (2001)
Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise
Boosting Mixture Models for Semi-supervised Learning. ICANN (2001)
Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise
Semi-supervised MarginBoost. NIPS (2001)
George Siolas, Florence d'Alché-Buc
Support Vector Machines Based on a Semantic Kernel for Text Categorization. IJCNN (5) (2000)
Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc
Discharge Prediction of Rechargeable Batteries with Neural Networks. Integr. Comput. Aided Eng. 6 (1) (1999)
Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc
Neural Network Adaptive Modeling of Battery Discharge Behavior. ICANN (1997)
Yann Guermeur, Florence d'Alché-Buc, Patrick Gallinari
Optimal Linear Regression on Classifier Outputs. ICANN (1997)
Florence d'Alché-Buc, Jean-Pierre Nadal
Asymptotic performances of a constructive algorithm. Neural Process. Lett. 2 (2) (1995)
Florence d'Alché-Buc, Jean-Pierre Nadal
Méthodes constructives pour l'apprentissage à partir d'exemples : les arbres neuronaux hybrides et leur comportement asymptotique. Monde des Util. Anal. Données 16 (1995)
Florence d'Alché-Buc, Didier Zwierski, Jean-Pierre Nadal
Trio Learning: A New Strategy for Building Hybrid Neural Trees. Int. J. Neural Syst. 5 (4) (1994)
Florence d'Alché-Buc, Vincent Andrés, Jean-Pierre Nadal
Rule Extraction with Fuzzy Neural Network. Int. J. Neural Syst. 5 (1) (1994)