Florence d'Alché-Buc

Florence d'Alché-Buc, George Siolas, Florence d'Alché-Buc, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

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

Chloé Clavel, Slim Essid, Florence d'Alché-Buc, 2019, ArXiv.

Kernelizing the output of tree-based methods

Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc, 2006, ICML '06.

Discharge Prediction of Rechargeable Batteries with Neural Networks

Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc, 1999, Integr. Comput. Aided Eng..

Autonomous learning of parameters in differential equations

Michèle Sebag, Florence d'Alché-Buc, Artémis Llamosi, 2015, ICML 2015.

Random Fourier Features For Operator-Valued Kernels pdf

Markus Heinonen, Florence d'Alché-Buc, Romain Brault, 2016, ACML.

Gradient boosting for kernelized output spaces

Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc, 2007, ICML '07.

Evaluating Predictive Uncertainty, Visual Objects Classification and Recognising textual entailment : selected proceedings of the First PASCAL Machine Learning Challenges Workshop

Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc, 2006 .

Inferring biological networks with output kernel trees

Pierre Geurts, Florence d'Alché-Buc, Marie Dutreix, 2007, BMC Bioinformatics.

Evaluation of Topographic Clustering and Its Kernelization

Marie-Jeanne Lesot, Florence d'Alché-Buc, George Siolas, 2003, ECML.

Learning a Markov Logic network for supervised gene regulatory network inference

Florence d'Alché-Buc, Christel Vrain, Céline Brouard, 2012, BMC Bioinformatics.

Gene Regulatory Network Inference using ensembles of Local Multiple Kernel Models

Arnaud Fouchet, Jean-Marc Delosme, Florence d'Alché-Buc, 2013 .

Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching

Vincent Frouin, Florence d'Alché-Buc, Cédric Auliac, 2007, WILF.

On the Dualization of Operator-Valued Kernel Machines pdf

Florence d'Alché-Buc, Pierre Laforgue, Alex Lambert, 2019, ArXiv.

Modeling of biological networks

Vincent Schächter, Florence d'Alché-Buc, Florence d'Alché-Buc, 2005 .

Output Fisher embedding regression

Maxime Sangnier, Florence d'Alché-Buc, Alexandre Garcia, 2018, Machine Learning.

Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment

Ido Dagan, Joaquin Quiñonero-Candela, Bernardo Magnini, 2005 .

Automatic contour extraction in images using a 2-D hidden Markov model

Patrick Gallinari, Sherif Makram-Ebeid, Florence d'Alché-Buc, 1999 .

Infinite Task Learning in RKHSs

Maxime Sangnier, Florence d'Alché-Buc, Zoltán Szabó, 2019, AISTATS.

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

George Michailidis, Florence d'Alché-Buc, Cédric Auliac, 2014, Machine Learning.

Trio Learning: A New Strategy for Building Hybrid Neural Trees

Florence d'Alché-Buc, Jean-Pierre Nadal, Didier Zwierski, 1994, Int. J. Neural Syst..

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

Florence d'Alché-Buc, Marie Szafranski, Céline Brouard, 2016, J. Mach. Learn. Res..

Data sparse nonparametric regression with ε-insensitive losses

Maxime Sangnier, Florence d'Alché-Buc, Olivier Fercoq, 2017, ACML.

Semi-supervised MarginBoost

Christophe Ambroise, Yves Grandvalet, Florence d'Alché-Buc, 2001, NIPS.

Fast metabolite identification with Input Output Kernel Regression

Juho Rousu, Florence d'Alché-Buc, Sebastian Böcker, 2016, Bioinform..

Gene networks inference using dynamic Bayesian networks

Aurélien Mazurie, Liva Ralaivola, Florence d'Alché-Buc, 2003, ECCB.

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

Nicolas Brunel, Florence d'Alché-Buc, Minh Quach, 2007, Bioinform..

Functional Isolation Forest pdf

Stéphan Clémençon, Florence d'Alché-Buc, Pavlo Mozharovskyi, 2019, ACML.

Rule Extraction with Fuzzy Neural Network

Florence d'Alché-Buc, Jean-Pierre Nadal, Vincent Andrés, 1994, Int. J. Neural Syst..

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

Florence d'Alché-Buc, Laurence Rouet, Blandine Romain, 2012, Abdominal Imaging.

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

Florence d'Alché-Buc, Laurence Rouet, Blandine Romain, 2013, MICCAI.

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

Nicolas Brunel, Florence d'Alché-Buc, 2010, PRIB.

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

James R. Eagan, Valérie Beaudouin, Jayneel Parekh, 2020, SSRN Electronic Journal.

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

Michèle Sebag, Florence d'Alché-Buc, Artémis Llamosi, 2014, ECML/PKDD.

Combinaison linéaire optimale de classifieurs

Patrick Gallinari, Yann Guermeur, Florence d'Alché-Buc, 1997 .

Input Output Kernel Regression

Florence d'Alché-Buc, Marie Szafranski, Céline Brouard, 2015 .

Pertinence des mesures de confiance en classification

Philippe Leray, Florence d'Alché-Buc, Hugo Zaragoza, 2000 .