Florence d'Alché-Buc

109 papers A* 14A 5B 2C 9Misc 1Journal 60Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Conformal Graph Prediction with Z-Gromov Wasserstein Distances. CoRR Gabriel Melo, Thibaut de Saivre, Anna Calissano, Florence d'Alché-Buc
2025	A*	`conf`	Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. ICLR Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc
2025	A*	`conf`	Tailoring Mixup to Data for Calibration. ICLR Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
2025	J	`jnl`	The quest for the GRAph Level autoEncoder (GRALE). CoRR Paul Krzakala, Gabriel Melo, Charlotte Laclau, Florence d'Alché-Buc, Rémi Flamary
2024	J	`jnl`	A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. Trans. Mach. Learn. Res. Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc
2024	A*	`conf`	Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss. NeurIPS Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau
2024	—	`conf`	Deep Sketched Output Kernel Regression for Structured Prediction. ECML/PKDD (3) Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc
2024	J	`jnl`	Deep Sketched Output Kernel Regression for Structured Prediction. CoRR Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc
2024	J	`jnl`	End-to-end Supervised Prediction of Arbitrary-size Graphs with Partially-Masked Fused Gromov-Wasserstein Matching. CoRR Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau
2024	J	`jnl`	Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance. Trans. Mach. Learn. Res. Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
2024	J	`jnl`	Learning Differentiable Surrogate Losses for Structured Prediction. CoRR Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
2024	J	`jnl`	Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. CoRR Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc
2024	A	`conf`	Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels. AISTATS Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc
2024	J	`jnl`	Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization. IEEE ACM Trans. Audio Speech Lang. Process. Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc
2023	J	`jnl`	Anomaly component analysis. CoRR Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc
2023	J	`jnl`	Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance. CoRR Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
2023	J	`jnl`	Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches. Trans. Mach. Learn. Res. Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc
2023	J	`jnl`	Fast kernel half-space depth for data with non-convex supports. CoRR Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc, Hicham Janati
2023	J	`jnl`	Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels. CoRR Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc
2023	J	`jnl`	Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization. CoRR Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc
2023	J	`jnl`	Tailoring Mixup to Data using Kernel Warping functions. CoRR Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
2022	A*	`conf`	Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. ICML Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc
2022	J	`jnl`	Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. CoRR Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc
2022	—	`conf`	Interpretable Generative Modeling Using a Hierarchical Topological VAE. CSCI Etienne Desticourt, Véronique Letort, Florence d'Alché-Buc
2022	A*	`conf`	Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. ICML Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
2022	J	`jnl`	Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. CoRR Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
2022	A*	`conf`	Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. NeurIPS Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard
2022	J	`jnl`	Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. CoRR Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard
2022	J	`jnl`	Vector-Valued Least-Squares Regression under Output Regularity Assumptions. CoRR Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
2022	J	`jnl`	Vector-Valued Least-Squares Regression under Output Regularity Assumptions. J. Mach. Learn. Res. Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
2022	J	`jnl`	Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection. CoRR Dimitri Bouche, Rémi Flamary, Florence d'Alché-Buc, Riwal Plougonven, Marianne Clausel, Jordi Badosa, Philippe Drobinski
2022	J	`jnl`	p-Sparsified Sketches for Fast Multiple Output Kernel Methods. CoRR Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc
2021	A*	`conf`	A Framework to Learn with Interpretation. NeurIPS Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
2021	J	`jnl`	Depth-based pseudo-metrics between probability distributions. CoRR Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
2021	J	`jnl`	Emotion Transfer Using Vector-Valued Infinite Task Learning. CoRR Alex Lambert, Sanjeel Parekh, Zoltán Szabó, Florence d'Alché-Buc
2021	J	`jnl`	Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program). J. Mach. Learn. Res. Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Hugo Larochelle
2021	A	`conf`	Nonlinear Functional Output Regression: A Dictionary Approach. AISTATS Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc
2021	A	`conf`	When OT meets MoM: Robust estimation of Wasserstein Distance. AISTATS Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
2020	J	`jnl`	A Framework to Learn with Interpretation. CoRR Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
2020	Misc	`conf`	Audio-Based Detection of Explicit Content in Music. ICASSP Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc
2020	A*	`conf`	Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses. ICML Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alché-Buc
2020	J	`jnl`	Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach. CoRR Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh
2020	—	`conf`	Identifying the "right" level of explanation in a given situation. NeHuAI@ECAI Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh
2020	J	`jnl`	Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). CoRR Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Hugo Larochelle
2020	J	`jnl`	Learning Output Embeddings in Structured Prediction. CoRR Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
2020	—	`conf`	Multilingual lyrics-to-audio alignment. ISMIR Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc
2020	J	`jnl`	Nonlinear Functional Output Regression: a Dictionary Approach. CoRR Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc
2020	J	`jnl`	When OT meets MoM: Robust estimation of Wasserstein Distance. CoRR Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc
2019	J	`jnl`	A multimodal movie review corpus for fine-grained opinion mining. CoRR Alexandre Garcia, Slim Essid, Florence d'Alché-Buc, Chloé Clavel
2019	A*	`ed.`	Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. NeurIPS Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett
2019	A	`conf`	Autoencoding any Data through Kernel Autoencoders. AISTATS Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc
2019	—	`conf`	From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining. EMNLP/IJCNLP (1) Alexandre Garcia, Pierre Colombo, Florence d'Alché-Buc, Slim Essid, Chloé Clavel
2019	J	`jnl`	From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining. CoRR Alexandre Garcia, Pierre Colombo, Slim Essid, Florence d'Alché-Buc, Chloé Clavel
2019	C	`conf`	Functional Isolation Forest. ACML Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
2019	J	`jnl`	Functional Isolation Forest. CoRR Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc
2019	A	`conf`	Infinite Task Learning in RKHSs. AISTATS Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc
2019	J	`jnl`	On the Dualization of Operator-Valued Kernel Machines. CoRR Pierre Laforgue, Alex Lambert, Luc Motte, Florence d'Alché-Buc
2018	A*	`conf`	A Structured Prediction Approach for Label Ranking. NeurIPS Anna Korba, Alexandre Garcia, Florence d'Alché-Buc
2018	J	`jnl`	A Structured Prediction Approach for Label Ranking. CoRR Anna Korba, Alexandre Garcia, Florence d'Alché-Buc
2018	J	`jnl`	Autoencoding any Data through Kernel Autoencoders. CoRR Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc
2018	J	`jnl`	Infinite-Task Learning with Vector-Valued RKHSs. CoRR Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc
2018	J	`jnl`	Output Fisher embedding regression. Mach. Learn. Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d'Alché-Buc
2018	A*	`conf`	Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. ICML Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d'Alché-Buc
2018	J	`jnl`	Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. CoRR Alexandre Garcia, Slim Essid, Chloé Clavel, Florence d'Alché-Buc
2017	C	`conf`	Data sparse nonparametric regression with ε-insensitive losses. ACML Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc
2017	J	`jnl`	Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison. Medical Image Anal. Blandine Romain, Laurence Rouet, Daniel Ohayon, Olivier Lucidarme, Florence d'Alché-Buc, Véronique Letort
2016	J	`jnl`	Fast metabolite identification with Input Output Kernel Regression. Bioinform. Céline Brouard, Huibin Shen, Kai Dührkop, Florence d'Alché-Buc, Sebastian Böcker, Juho Rousu
2016	J	`jnl`	Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels. J. Mach. Learn. Res. Céline Brouard, Marie Szafranski, Florence d'Alché-Buc
2016	—	`conf`	Joint quantile regression in vector-valued RKHSs. NIPS Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc
2016	C	`conf`	Random Fourier Features For Operator-Valued Kernels. ACML Romain Brault, Markus Heinonen, Florence d'Alché-Buc
2016	J	`jnl`	Random Fourier Features for Operator-Valued Kernels. CoRR Romain Brault, Florence d'Alché-Buc, Markus Heinonen
2015	J	`jnl`	A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions. IEEE ACM Trans. Comput. Biol. Bioinform. Adriana Birlutiu, Florence d'Alché-Buc, Tom Heskes
2015	J	`jnl`	Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinform. Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alché-Buc
2015	J	`jnl`	Operator-valued kernel-based vector autoregressive models for network inference. Mach. Learn. Néhémy Lim, Florence d'Alché-Buc, Cédric Auliac, George Michailidis
2014	—	`conf`	Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach. ECML/PKDD (2) Artémis Llamosi, Adel Mezine, Florence d'Alché-Buc, Véronique Letort, Michèle Sebag
2014	J	`jnl`	Learning nonparametric differential equations with operator-valued kernels and gradient matching. CoRR Markus Heinonen, Florence d'Alché-Buc
2013	—	`conf`	A Multi-task Learning Approach for Compartmental Model Parameter Estimation in DCE-CT Sequences. MICCAI (2) Blandine Romain, Véronique Letort, Olivier Lucidarme, Laurence Rouet, Florence d'Alché-Buc
2013	—	`conf`	Inférence de réseaux biologiques : un défi pour la fouille de données structurées. EGC Florence d'Alché-Buc
2013	J	`jnl`	Learning a Markov Logic network for supervised gene regulatory network inference. BMC Bioinform. Céline Brouard, Christel Vrain, Julie Dubois, David Castel, Marie-Anne Debily, Florence d'Alché-Buc
2013	J	`jnl`	OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks. Bioinform. Néhémy Lim, Yasin Senbabaoglu, George Michailidis, Florence d'Alché-Buc
2012	B	`conf`	Optimisation of reconstruction for the registration of CT liver perfusion sequences. Image Processing Blandine Romain, Véronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet
2012	—	`conf`	Registration of Free-Breathing Abdominal 3D Contrast-Enhanced CT. Abdominal Imaging Blandine Romain, Véronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet
2011	A*	`conf`	Semi-supervised Penalized Output Kernel Regression for Link Prediction. ICML Céline Brouard, Florence d'Alché-Buc, Marie Szafranski
2010	—	`ch.`	Estimation of Parametric Nonlinear ODEs for Biological Networks Identification. Learning and Inference in Computational Systems Biology Florence d'Alché-Buc, Nicolas Brunei
2010	—	`conf`	Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks. PRIB Nicolas Brunel, Florence d'Alché-Buc
2009	J	`jnl`	CycSim - an online tool for exploring and experimenting with genome-scale metabolic models. Bioinform. François Le Fèvre, Serge Smidtas, Cyril Combe, Maxime Durot, Florence d'Alché-Buc, Vincent Schächter
2008	J	`jnl`	Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset. BMC Bioinform. Cédric Auliac, Vincent Frouin, Xavier Gidrol, Florence d'Alché-Buc
2007	J	`jnl`	Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinform. Minh Quach, Nicolas Brunel, Florence d'Alché-Buc
2007	A*	`conf`	Gradient boosting for kernelized output spaces. ICML Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
2007	J	`jnl`	Inferring biological networks with output kernel trees. BMC Bioinform. Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc
2007	—	`conf`	Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching. WILF Cédric Auliac, Florence d'Alché-Buc, Vincent Frouin
2006	A*	`conf`	Kernelizing the output of tree-based methods. ICML Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
2006	—	`ed.`	Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers MLCW Joaquin Quiñonero Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc
2005	B	`conf`	Time series filtering, smoothing and learning using the kernel Kalman filter. IJCNN Liva Ralaivola, Florence d'Alché-Buc
2003	—	`conf`	Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. NIPS Liva Ralaivola, Florence d'Alché-Buc
2003	—	`conf`	Evaluation of Topographic Clustering and Its Kernelization. ECML Marie-Jeanne Lesot, Florence d'Alché-Buc, George Siolas
2003	C	`conf`	Gene networks inference using dynamic Bayesian networks. ECCB Bruno-Edouard Perrin, Liva Ralaivola, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, Florence d'Alché-Buc
2002	C	`conf`	Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling. ICANN George Siolas, Florence d'Alché-Buc
2001	C	`conf`	Boosting Mixture Models for Semi-supervised Learning. ICANN Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise
2001	C	`conf`	Incremental Support Vector Machine Learning: A Local Approach. ICANN Liva Ralaivola, Florence d'Alché-Buc
2001	—	`conf`	Semi-supervised MarginBoost. NIPS Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise
2000	—	`conf`	Support Vector Machines Based on a Semantic Kernel for Text Categorization. IJCNN (5) George Siolas, Florence d'Alché-Buc
1999	J	`jnl`	Discharge Prediction of Rechargeable Batteries with Neural Networks. Integr. Comput. Aided Eng. Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc
1997	C	`conf`	Neural Network Adaptive Modeling of Battery Discharge Behavior. ICANN Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc
1997	C	`conf`	Optimal Linear Regression on Classifier Outputs. ICANN Yann Guermeur, Florence d'Alché-Buc, Patrick Gallinari
1995	J	`jnl`	Asymptotic performances of a constructive algorithm. Neural Process. Lett. Florence d'Alché-Buc, Jean-Pierre Nadal
1995	J	`jnl`	Méthodes constructives pour l'apprentissage à partir d'exemples : les arbres neuronaux hybrides et leur comportement asymptotique. Monde des Util. Anal. Données Florence d'Alché-Buc, Jean-Pierre Nadal
1994	J	`jnl`	Rule Extraction with Fuzzy Neural Network. Int. J. Neural Syst. Florence d'Alché-Buc, Vincent Andrés, Jean-Pierre Nadal
1994	J	`jnl`	Trio Learning: A New Strategy for Building Hybrid Neural Trees. Int. J. Neural Syst. Florence d'Alché-Buc, Didier Zwierski, Jean-Pierre Nadal