Rule Extraction with Fuzzy Neural Network. - Florence d'Alché-Buc; Vincent Andrés; Jean-Pierre Nadal (Int. J. Neural Syst., 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)

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)

Asymptotic performances of a constructive algorithm. - Florence d'Alché-Buc; Jean-Pierre Nadal (Neural Process. Lett., 1995)

Neural Network Adaptive Modeling of Battery Discharge Behavior. - Olivier Gérard; Jean-Noël Patillon; Florence d'Alché-Buc (ICANN, 1997)

Optimal Linear Regression on Classifier Outputs. - Yann Guermeur; Florence d'Alché-Buc; Patrick Gallinari (ICANN, 1997)

Discharge Prediction of Rechargeable Batteries with Neural Networks. - Olivier Gérard; Jean-Noël Patillon; Florence d'Alché-Buc (Integr. Comput. Aided Eng., 1999)

Support Vector Machines Based on a Semantic Kernel for Text Categorization. - George Siolas; Florence d'Alché-Buc (IJCNN (5), 2000)

Boosting Mixture Models for Semi-supervised Learning. - Yves Grandvalet; Florence d'Alché-Buc; Christophe Ambroise (ICANN, 2001)

Incremental Support Vector Machine Learning: A Local Approach. - Liva Ralaivola; Florence d'Alché-Buc (ICANN, 2001)

Semi-supervised MarginBoost. - Florence d'Alché-Buc; Yves Grandvalet; Christophe Ambroise (NIPS, 2001)

Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling. - George Siolas; Florence d'Alché-Buc (ICANN, 2002)

Gene networks inference using dynamic Bayesian networks. - Bruno-Edouard Perrin; Liva Ralaivola; Aurélien Mazurie; Samuele Bottani; Jacques Mallet; Florence d'Alché-Buc (ECCB, 2003)

Evaluation of Topographic Clustering and Its Kernelization. - Marie-Jeanne Lesot; Florence d'Alché-Buc; George Siolas (ECML, 2003)

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. - Liva Ralaivola; Florence d'Alché-Buc (NIPS, 2003)

Time series filtering, smoothing and learning using the kernel Kalman filter. - Liva Ralaivola; Florence d'Alché-Buc (IJCNN, 2005)

Kernelizing the output of tree-based methods. - Pierre Geurts; Louis Wehenkel; Florence d'Alché-Buc (ICML, 2006)

Gradient boosting for kernelized output spaces. - Pierre Geurts; Louis Wehenkel; Florence d'Alché-Buc (ICML, 2007)

Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching. - Cédric Auliac; Florence d'Alché-Buc; Vincent Frouin (WILF, 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)

Inferring biological networks with output kernel trees. - Pierre Geurts; Nizar Touleimat; Marie Dutreix; Florence d'Alché-Buc (BMC Bioinform., 2007)

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)

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)

Estimation of Parametric Nonlinear ODEs for Biological Networks Identification. - Florence d'Alché-Buc; Nicolas Brunei (Learning and Inference in Computational Systems Biology, 2010)

Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks. - Nicolas Brunel; Florence d'Alché-Buc (PRIB, 2010)

Semi-supervised Penalized Output Kernel Regression for Link Prediction. - Céline Brouard; Florence d'Alché-Buc; Marie Szafranski (ICML, 2011)

Registration of Free-Breathing Abdominal 3D Contrast-Enhanced CT. - Blandine Romain; Véronique Letort; Olivier Lucidarme; Florence d'Alché-Buc; Laurence Rouet (Abdominal Imaging, 2012)

Optimisation of reconstruction for the registration of CT liver perfusion sequences. - Blandine Romain; Véronique Letort; Olivier Lucidarme; Florence d'Alché-Buc; Laurence Rouet (Image Processing, 2012)

Inférence de réseaux biologiques : un défi pour la fouille de données structurées. - Florence d'Alché-Buc (EGC, 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 (MICCAI (2), 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)

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)

Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach. - Artémis Llamosi; Adel Mezine; Florence d'Alché-Buc; Véronique Letort; Michèle Sebag (ECML/PKDD (2), 2014)

Learning nonparametric differential equations with operator-valued kernels and gradient matching. - Markus Heinonen; Florence d'Alché-Buc (CoRR, 2014)

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)

Operator-valued kernel-based vector autoregressive models for network inference. - Néhémy Lim; Florence d'Alché-Buc; Cédric Auliac; George Michailidis (Mach. Learn., 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)

Random Fourier Features For Operator-Valued Kernels. - Romain Brault; Markus Heinonen; Florence d'Alché-Buc (ACML, 2016)

Joint quantile regression in vector-valued RKHSs. - Maxime Sangnier; Olivier Fercoq; Florence d'Alché-Buc (NIPS, 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)

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)

Data sparse nonparametric regression with ε-insensitive losses. - Maxime Sangnier; Olivier Fercoq; Florence d'Alché-Buc (ACML, 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)

Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. - Alexandre Garcia 001; Chloé Clavel; Slim Essid; Florence d'Alché-Buc (ICML, 2018)

A Structured Prediction Approach for Label Ranking. - Anna Korba; Alexandre Garcia 001; Florence d'Alché-Buc (NeurIPS, 2018)

Infinite-Task Learning with Vector-Valued RKHSs. - Romain Brault; Alex Lambert; Zoltán Szabó; Maxime Sangnier; Florence d'Alché-Buc (CoRR, 2018)

Output Fisher embedding regression. - Moussab Djerrab; Alexandre Garcia 001; Maxime Sangnier; Florence d'Alché-Buc (Mach. Learn., 2018)

Functional Isolation Forest. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon; Florence d'Alché-Buc (ACML, 2019)

Infinite Task Learning in RKHSs. - Romain Brault; Alex Lambert; Zoltán Szabó; Maxime Sangnier; Florence d'Alché-Buc (AISTATS, 2019)

Autoencoding any Data through Kernel Autoencoders. - Pierre Laforgue; Stéphan Clémençon; Florence d'Alché-Buc (AISTATS, 2019)

From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining. - Alexandre Garcia 001; Pierre Colombo; Florence d'Alché-Buc; Slim Essid; Chloé Clavel (EMNLP/IJCNLP (1), 2019)

A multimodal movie review corpus for fine-grained opinion mining. - Alexandre Garcia 001; Slim Essid; Florence d'Alché-Buc; Chloé Clavel (CoRR, 2019)

On the Dualization of Operator-Valued Kernel Machines. - Pierre Laforgue; Alex Lambert; Luc Motte; Florence d'Alché-Buc (CoRR, 2019)

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 (NeHuAI@ECAI, 2020)

Audio-Based Detection of Explicit Content in Music. - Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gaël Richard; Florence d'Alché-Buc (ICASSP, 2020)

Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses. - Pierre Laforgue; Alex Lambert; Luc Brogat-Motte; Florence d'Alché-Buc (ICML, 2020)

Multilingual lyrics-to-audio alignment. - Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gaël Richard; Florence d'Alché-Buc (ISMIR, 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)

Learning Output Embeddings in Structured Prediction. - Luc Brogat-Motte; Alessandro Rudi; Céline Brouard; Juho Rousu; Florence d'Alché-Buc (CoRR, 2020)

Nonlinear Functional Output Regression: A Dictionary Approach. - Dimitri Bouche; Marianne Clausel; François Roueff; Florence d'Alché-Buc (AISTATS, 2021)

When OT meets MoM: Robust estimation of Wasserstein Distance. - Guillaume Staerman; Pierre Laforgue; Pavlo Mozharovskyi; Florence d'Alché-Buc (AISTATS, 2021)

A Framework to Learn with Interpretation. - Jayneel Parekh; Pavlo Mozharovskyi; Florence d'Alché-Buc (NeurIPS, 2021)

Emotion Transfer Using Vector-Valued Infinite Task Learning. - Alex Lambert; Sanjeel Parekh; Zoltán Szabó; Florence d'Alché-Buc (CoRR, 2021)

Depth-based pseudo-metrics between probability distributions. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon; Florence d'Alché-Buc (CoRR, 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)

Interpretable Generative Modeling Using a Hierarchical Topological VAE. - Etienne Desticourt; Véronique Letort; Florence d'Alché-Buc (CSCI, 2022)

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. - Luc Brogat-Motte; Rémi Flamary; Céline Brouard; Juho Rousu; Florence d'Alché-Buc (ICML, 2022)

Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. - Alex Lambert; Dimitri Bouche; Zoltán Szabó; Florence d'Alché-Buc (ICML, 2022)

Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. - Jayneel Parekh; Sanjeel Parekh; Pavlo Mozharovskyi; Florence d'Alché-Buc; Gaël Richard (NeurIPS, 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)

p-Sparsified Sketches for Fast Multiple Output Kernel Methods. - Tamim El Ahmad; Pierre Laforgue; Florence d'Alché-Buc (CoRR, 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)

Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance. - Junjie Yang; Matthieu Labeau; Florence d'Alché-Buc (CoRR, 2023)

Tailoring Mixup to Data using Kernel Warping functions. - Quentin Bouniot; Pavlo Mozharovskyi; Florence d'Alché-Buc (CoRR, 2023)

Fast kernel half-space depth for data with non-convex supports. - Arturo Castellanos 002; Pavlo Mozharovskyi; Florence d'Alché-Buc; Hicham Janati (CoRR, 2023)

Anomaly component analysis. - Romain Valla; Pavlo Mozharovskyi; Florence d'Alché-Buc (CoRR, 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)

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 (AISTATS, 2024)

Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss. - Paul Krzakala; Junjie Yang; Rémi Flamary; Florence d'Alché-Buc; Charlotte Laclau; Matthieu Labeau (NeurIPS, 2024)

Deep Sketched Output Kernel Regression for Structured Prediction. - Tamim El Ahmad; Junjie Yang; Pierre Laforgue; Florence d'Alché-Buc (ECML/PKDD (3), 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)

Learning Differentiable Surrogate Losses for Structured Prediction. - Junjie Yang; Matthieu Labeau; Florence d'Alché-Buc (CoRR, 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)

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)

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)

Tailoring Mixup to Data for Calibration. - Quentin Bouniot; Pavlo Mozharovskyi; Florence d'Alché-Buc (ICLR, 2025)

Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. - Jayneel Parekh; Quentin Bouniot; Pavlo Mozharovskyi; Alasdair Newson; Florence d'Alché-Buc (ICLR, 2025)

The quest for the GRAph Level autoEncoder (GRALE). - Paul Krzakala; Gabriel Melo; Charlotte Laclau; Florence d'Alché-Buc; Rémi Flamary (CoRR, 2025)