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- affiliation: LTCI, Télécom Paris, Institut Polytechnique de Paris, France
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2020 – today
- 2024
[j24]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: 1392-1405 (2024)- [j23]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) - [j22]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) - [c42]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: 109-117 - [c41]Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc:
Deep Sketched Output Kernel Regression for Structured Prediction. ECML/PKDD (3) 2024: 93-110 - [i33]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 abs/2402.12269 (2024) - [i32]Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc:
Deep Sketched Output Kernel Regression for Structured Prediction. CoRR abs/2406.09253 (2024) - [i31]Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc:
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. CoRR abs/2407.01331 (2024) - 2023
- [j21]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) - [i30]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 abs/2302.10128 (2023) - [i29]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc:
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization. CoRR abs/2305.07132 (2023) - [i28]Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc:
Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance. CoRR abs/2309.16604 (2023) - [i27]Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc:
Tailoring Mixup to Data using Kernel Warping functions. CoRR abs/2311.01434 (2023) - [i26]Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc, Hicham Janati:
Fast kernel half-space depth for data with non-convex supports. CoRR abs/2312.14136 (2023) - [i25]Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc:
Anomaly component analysis. CoRR abs/2312.16139 (2023) - 2022
- [j20]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: 344:1-344:50 (2022) - [c40]Etienne Desticourt, Véronique Letort, Florence d'Alché-Buc:
Interpretable Generative Modeling Using a Hierarchical Topological VAE. CSCI 2022: 1415-1421 - [c39]Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc:
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. ICML 2022: 2321-2335 - [c38]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: 11844-11867 - [c37]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 - [i24]Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc:
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. CoRR abs/2202.03813 (2022) - [i23]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard:
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. CoRR abs/2202.11479 (2022) - [i22]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 abs/2204.09362 (2022) - [i21]Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc:
p-Sparsified Sketches for Fast Multiple Output Kernel Methods. CoRR abs/2206.03827 (2022) - [i20]Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc:
Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. CoRR abs/2206.08220 (2022) - [i19]Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc:
Vector-Valued Least-Squares Regression under Output Regularity Assumptions. CoRR abs/2211.08958 (2022) - 2021
- [j19]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: 164:1-164:20 (2021) - [c36]Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc:
When OT meets MoM: Robust estimation of Wasserstein Distance. AISTATS 2021: 136-144 - [c35]Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc:
Nonlinear Functional Output Regression: A Dictionary Approach. AISTATS 2021: 235-243 - [c34]Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc:
A Framework to Learn with Interpretation. NeurIPS 2021: 24273-24285 - [i18]Alex Lambert, Sanjeel Parekh, Zoltán Szabó, Florence d'Alché-Buc:
Emotion Transfer Using Vector-Valued Infinite Task Learning. CoRR abs/2102.05075 (2021) - [i17]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Depth-based pseudo-metrics between probability distributions. CoRR abs/2103.12711 (2021) - 2020
- [c33]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: 63-66 - [c32]Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc:
Audio-Based Detection of Explicit Content in Music. ICASSP 2020: 526-530 - [c31]Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alché-Buc:
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses. ICML 2020: 5598-5607 - [c30]Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d'Alché-Buc:
Multilingual lyrics-to-audio alignment. ISMIR 2020: 512-519 - [i16]Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc:
Nonlinear Functional Output Regression: a Dictionary Approach. CoRR abs/2003.01432 (2020) - [i15]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 abs/2003.07703 (2020) - [i14]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 abs/2003.12206 (2020) - [i13]Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc:
When OT meets MoM: Robust estimation of Wasserstein Distance. CoRR abs/2006.10325 (2020) - [i12]Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc:
Learning Output Embeddings in Structured Prediction. CoRR abs/2007.14703 (2020) - [i11]Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc:
A Framework to Learn with Interpretation. CoRR abs/2010.09345 (2020)
2010 – 2019
- 2019
- [c29]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Functional Isolation Forest. ACML 2019: 332-347 - [c28]Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc:
Autoencoding any Data through Kernel Autoencoders. AISTATS 2019: 1061-1069 - [c27]Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc:
Infinite Task Learning in RKHSs. AISTATS 2019: 1294-1302 - [c26]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: 5538-5547 - [e2]Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett:
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 2019 [contents] - [i10]Alexandre Garcia, Slim Essid, Florence d'Alché-Buc, Chloé Clavel:
A multimodal movie review corpus for fine-grained opinion mining. CoRR abs/1902.10102 (2019) - [i9]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Functional Isolation Forest. CoRR abs/1904.04573 (2019) - [i8]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 abs/1908.11216 (2019) - [i7]Pierre Laforgue, Alex Lambert, Luc Motte, Florence d'Alché-Buc:
On the Dualization of Operator-Valued Kernel Machines. CoRR abs/1910.04621 (2019) - 2018
- [j18]Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d'Alché-Buc:
Output Fisher embedding regression. Mach. Learn. 107(8-10): 1229-1256 (2018) - [c25]Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d'Alché-Buc:
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. ICML 2018: 1681-1689 - [c24]Anna Korba, Alexandre Garcia, Florence d'Alché-Buc:
A Structured Prediction Approach for Label Ranking. NeurIPS 2018: 9008-9018 - [i6]Alexandre Garcia, Slim Essid, Chloé Clavel, Florence d'Alché-Buc:
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. CoRR abs/1803.08355 (2018) - [i5]Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d'Alché-Buc:
Infinite-Task Learning with Vector-Valued RKHSs. CoRR abs/1805.08809 (2018) - [i4]Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc:
Autoencoding any Data through Kernel Autoencoders. CoRR abs/1805.11028 (2018) - [i3]Anna Korba, Alexandre Garcia, Florence d'Alché-Buc:
A Structured Prediction Approach for Label Ranking. CoRR abs/1807.02374 (2018) - 2017
- [j17]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: 360-374 (2017) - [c23]Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc:
Data sparse nonparametric regression with ε-insensitive losses. ACML 2017: 192-207 - 2016
- [j16]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): 28-36 (2016) - [j15]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: 176:1-176:48 (2016) - [c22]Romain Brault, Markus Heinonen, Florence d'Alché-Buc:
Random Fourier Features For Operator-Valued Kernels. ACML 2016: 110-125 - [c21]Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc:
Joint quantile regression in vector-valued RKHSs. NIPS 2016: 3693-3701 - [i2]Romain Brault, Florence d'Alché-Buc, Markus Heinonen:
Random Fourier Features for Operator-Valued Kernels. CoRR abs/1605.02536 (2016) - 2015
- [j14]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): 728-735 (2015) - [j13]Néhémy Lim, Florence d'Alché-Buc, Cédric Auliac, George Michailidis:
Operator-valued kernel-based vector autoregressive models for network inference. Mach. Learn. 99(3): 489-513 (2015) - [j12]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): 538-550 (2015) - 2014
- [c20]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: 306-321 - [i1]Markus Heinonen, Florence d'Alché-Buc:
Learning nonparametric differential equations with operator-valued kernels and gradient matching. CoRR abs/1411.5172 (2014) - 2013
- [j11]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): 1416-1423 (2013) - [j10]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: 273 (2013) - [c19]Florence d'Alché-Buc:
Inférence de réseaux biologiques : un défi pour la fouille de données structurées. EGC 2013: 5-6 - [c18]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: 271-278 - 2012
- [c17]Blandine Romain, Véronique Letort, Olivier Lucidarme, Florence d'Alché-Buc, Laurence Rouet:
Registration of Free-Breathing Abdominal 3D Contrast-Enhanced CT. Abdominal Imaging 2012: 274-282 - [c16]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: 83143S - 2011
- [c15]Céline Brouard, Florence d'Alché-Buc, Marie Szafranski:
Semi-supervised Penalized Output Kernel Regression for Link Prediction. ICML 2011: 593-600 - 2010
- [c14]Nicolas Brunel, Florence d'Alché-Buc:
Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks. PRIB 2010: 443-454 - [p1]Florence d'Alché-Buc, Nicolas Brunei:
Estimation of Parametric Nonlinear ODEs for Biological Networks Identification. Learning and Inference in Computational Systems Biology 2010: 61-96
2000 – 2009
- 2009
- [j9]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): 1987-1988 (2009) - 2008
- [j8]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) - 2007
- [j7]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): 3209-3216 (2007) - [j6]Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc:
Inferring biological networks with output kernel trees. BMC Bioinform. 8(S-2) (2007) - [c13]Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc:
Gradient boosting for kernelized output spaces. ICML 2007: 289-296 - [c12]Cédric Auliac, Florence d'Alché-Buc, Vincent Frouin:
Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching. WILF 2007: 612-619 - 2006
- [c11]Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc:
Kernelizing the output of tree-based methods. ICML 2006: 345-352 - [e1]Joaquin Quiñonero Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc:
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. Lecture Notes in Computer Science 3944, Springer 2006, ISBN 3-540-33427-0 [contents] - 2003
- [c10]Bruno-Edouard Perrin, Liva Ralaivola, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, Florence d'Alché-Buc:
Gene networks inference using dynamic Bayesian networks. ECCB 2003: 138-148 - [c9]Marie-Jeanne Lesot, Florence d'Alché-Buc, George Siolas:
Evaluation of Topographic Clustering and Its Kernelization. ECML 2003: 265-276 - [c8]Liva Ralaivola, Florence d'Alché-Buc:
Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. NIPS 2003: 129-136 - 2002
- [c7]George Siolas, Florence d'Alché-Buc:
Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling. ICANN 2002: 769-776 - 2001
- [c6]Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise:
Boosting Mixture Models for Semi-supervised Learning. ICANN 2001: 41-48 - [c5]Liva Ralaivola, Florence d'Alché-Buc:
Incremental Support Vector Machine Learning: A Local Approach. ICANN 2001: 322-330 - [c4]Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise:
Semi-supervised MarginBoost. NIPS 2001: 553-560 - 2000
- [c3]George Siolas, Florence d'Alché-Buc:
Support Vector Machines Based on a Semantic Kernel for Text Categorization. IJCNN (5) 2000: 205-209
1990 – 1999
- 1999
- [j5]Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc:
Discharge Prediction of Rechargeable Batteries with Neural Networks. Integr. Comput. Aided Eng. 6(1): 41-52 (1999) - 1997
- [c2]Yann Guermeur, Florence d'Alché-Buc, Patrick Gallinari:
Optimal Linear Regression on Classifier Outputs. ICANN 1997: 481-486 - [c1]Olivier Gérard, Jean-Noël Patillon, Florence d'Alché-Buc:
Neural Network Adaptive Modeling of Battery Discharge Behavior. ICANN 1997: 1095-1100 - 1995
- [j4]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: 1-17 (1995) - [j3]Florence d'Alché-Buc, Jean-Pierre Nadal:
Asymptotic performances of a constructive algorithm. Neural Process. Lett. 2(2): 1-4 (1995) - 1994
- [j2]Florence d'Alché-Buc, Vincent Andrés, Jean-Pierre Nadal:
Rule Extraction with Fuzzy Neural Network. Int. J. Neural Syst. 5(1): 1-11 (1994) - [j1]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): 259-274 (1994)
Coauthor Index
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