Full Professor, Télécom Paris, Institut Polytechnique de Paris
2026
Gabriel Silva de Melo, Thibaut de Saivre, Anna Calissano, Florence d’Alché-Buc, Conformal Graph Prediction, UAI 2026 [https://arxiv.org/abs/2603.02460].
Valla, R., Mozharovskyi, P., and d’Alché-Buc, F. (2026): Abnormal component analysis. Technometrics, in press. [arXiv:2312.16139]
Fine-tuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation, Auguste de Lambilly, Vladimir Baturin, David Portehault, Guillaume Lambard, Nataliya Sokolovska, Florence d’Alché-Buc, Jean-Claude Crivello (2026). Preprint. [https://arxiv.org/pdf/2604.13354]
2025
The quest for the GRAph Level autoEncoder (GRALE), Paul Krzakala, Gabriel Silva de Melo, Charlotte Laclau, Florence d’Alché-Buc, Remi Flamary, arxiv, accepted at NeurIPS 2025.
Quentin Bouniot, Pavlo Mozharovskyi, Florence d’Alché-Buc. Tayloring Mixup for Calibration, accepted in ICLR 2025, arxiv here.
Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d’Alché-Buc, Charlotte laclau, Mathieu Labeau. Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss, NeurIPS 2024, arxiv here.
Staerman, G., Mozharovskyi, P., Colombo, P., Clémençon, S., & d’Alché-Buc, F. (2024). A pseudo-metric between probability distributions based on depth-trimmed regions. TMLR, 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 Mozharvoskyi, Gaël Richard, Florence d’Alché-Buc. Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization in IEEE Tr. ASLP, 2024.
Valla, R., Mozharovskyi, P., & d’Alché-Buc, F. (2023). Anomaly component analysis. arXiv preprint arXiv:2312.16139.
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, Arxiv, 2023.
M. Djerrab, A. Garcia, Florence d’Alché-Buc. Learning with a surrogate Fisher loss in small data regime, in Proc. of ESANN’18.
Pierre Laforgue, Stephan Clémençon, Florence d’Alché-Buc, Autoencoding any data through kernel autoencoders, arXiv and presented at Journées françaises de Statistique 2018, 2018.
2017
Maxime
Sangnier, Olivier Fercoq, Florence d’Alché-Buc. Data sparse
nonparametric regression with epsilon-sensititive losses, in 9th
ACML, in JMLR proceedings, 2017, pdf.
Moussab
Djerrab, Alexandre Garcia, Maxime Sangnier and Florence d’Alché-Buc,
Structured Prediction by minimization of a Fisher Surrogate Loss,
communication à CAP 2017.
Nicolas Brunel, Quentin Clairon & Florence d’Alché-Buc,
Parametric Estimation of Ordinary Differential Equations with
Orthogonality Conditions, Journal of American Statistical Association
(JASA), 10 Oct 2013, DOI:10.1080/01621459.2013.841583 (version HAL).
Published in 2014.
Pablo Meyer1*, Thomas Cokelaer2, Deepak Chandran3, Kyung Hyuk Kim4, Po-Ru Loh5, George Tucker5, Mark Lipson5, Bonnie Berger5, Clemens Kreutz8, Andreas Raue78, Bernhard Steiert8, Jens Timmer68, Erhan Bilal1, DREAM 6&7 Parameter Estimation consortium, Herbert M Sauro4, Gustavo Stolovitzky1 and Julio Saez-Rodriguez2*,
Network topology and parameter estimation: from experimental design
methods to gene regulatory network kinetics using a community based
approach, BMC Systems Biology 2014, 8:13.
Artemis
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.
George Michailidis and Florence d’Alché-Buc, Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues, in Special issue on : Parameter estimation in differential equations, Mathematical Biosciences, Springer, Available online 28 October (2013).
Lise Pomies, Mélanie Courteix, Justin Bedo, Nathalie Leblanc-Fournier, Bruno Moulia and Florence d’Alché-Buc, Deciphering gene regulatory network from gene expression kinetics with an unfavorable data-to-variables ratio, MLSB’16, joint workshop to ECML 2016, Den Haag.
Brouard, C., Guerrera, C., Brouillard, F., Ollero, M., Edelman, A. and d’Alché-Buc, F. (2012) Search for new CFTR-protein interactions using statistical learning. 6th European Cystic Fibrosis Young Investigator Meeting, Paris, France.
Brouard, C., Vrain, C., Dubois, J., Castel, D., Debily, M.-A. and d’Alché-Buc, F. (2012) Learning a Markov Logic Network for supervised gene regulation inference: application to the ID2 regulatory network in human keratinocytes. Machine Learning in Systems Biology (MLSB) workshop, Basel, Switzerland.
Brouard, C., d’Alché-Buc, F. and Szafranski, M. (2012) Link prediction as a structured output prediction problem using operator-valued kernels. Object, functional and structured data: towards next generation kernel-based methods – ICML Workshop, Edinburgh, Scotland.
2011
Brouard,
C., d’Alché-Buc, F. and Szafranski, M. (2011) Semi-supervised Penalized
Output Kernel Regression for Link Prediction. In Proceedings of the
28th International Conference on Machine Leaning (ICML), Bellevue,
Washington, USA. [PDF][Supplementary materials][Slides]
Brouard,
C., d’Alché-Buc, F. and Szafranski, M. (2012) Link prediction as a
structured output prediction problem using operator-valued kernels.
Object, functional and structured data: towards next generation
kernel-based methods – ICML Workshop, Edinburgh, Scotland.
Brouard,
C., d’Alché-Buc, F. and Szafranski, M. (2011) A new theoretical angle
to semi-supervised output kernel regression for protein-protein
interaction network inference. Machine Learning in Systems Biology
(MLSB) workshop, Vienna, Austria.
2010
Nicolas Brunel, Florence d’Alché-Buc: Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks, in Tjeerd Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes
(Eds.): Pattern Recognition in Bioinformatics – 5th IAPR International
Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010.
Proceedings. Springer 2010 Lecture Notes in Computer Science ISBN 978-3-642-16000-4, : 443-454.
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. Bioinformatics 23(23): 3209-3216 (2007)
Cédric Auliac, Florence d’Alché-Buc, Vincent Frouin: Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching. WILF 2007: 612-619
Florence d’Alché-Buc and Louis Wehenkel, edition of Selected Proceedings of Machine Learning in Systems Biology: MLSB 2007, Machine Learning in Systems Biology: MLSB 2007, Evry, France, 24-25 September 2007, BMC Proceedings, Volume 2 Supplement 4.
Florence d’Alché-Buc, Learning operator-valued kernels in multiple output regression, FEAST 2015 (workshop joint to ICML 2015), Lille, Jul 10, 2015.
Florence d’Alché-Buc, Experimental design as a one-player game: a promising tool in systems and synthetic biology, Workshop on Living factories, Aalto University, Helsinki (Finland), Sept 3, 2015.
A new angle to causal network discovery using operator-valued kernel-based autoregressive models at ENBIS-Spring Meeting. Causal Graphical models and Bayesian networks. April 9-11 2014, Institut Poincarré, Paris, France.
De la mesure d’expression de gènes à l’inférence de réseaux de régulation, DIM ANALYTICS, ESPCI, Paris, France, April 8 2014.
Florence d’Alché-Buc: Inférence de réseaux biologiques : un défi pour la fouille de données structurées. EGC 2013: 5-6
Talk at PEDS II, Eurandom, June 2012 (special issue MBS)
2005
d’Alché-Buc, F.; Lahaye, P.-J.; Perrin, B.-E.; Ralaivola, L.; Vujasinovic, T.; Mazurie, A.; Bottani, S. A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle. In Bioinformatics Using Computational Intelligence Paradigms, Studies in Fuzziness and Soft Computing 176, pp. 93–117, Springer, 2005.
Ralaivola, L.; d’Alché-Buc, F. Time Series Filtering, Smoothing and Learning using the Kernel Kalman Filter. IJCNN 2005, pp. 1449–1454. DOI: 10.1109/IJCNN.2005.1556088.
Combe, C.; d’Alché-Buc, F. Apprentissage relationnel du concept de régulation génétique, 2005 (unpublished).
d’Alché-Buc, F.; Schächter, V. Modeling of biological networks, 2005.
2004
Lesot, M.-J.; Dard, D.; d’Alché-Buc, F. A methodology for topographic clustering of structured text documents. PASCAL Workshop on Learning Methods for Text Understanding and Mining, Grenoble, 2004.
Lesot, M.-J.; d’Alché-Buc, F.; Siolas, G. Evaluation of Topographic Clustering and Its Kernelization. ECML 2003, LNCS 2837, pp. 265–276.
Ralaivola, L.; d’Alché-Buc, F. Filtrage de Kalman non linéaire à l’aide de noyaux. GRETSI 2003, Paris.
d’Alché-Buc, F. Association de systèmes d’inférence floue avec les méthodes connexionnistes et évolutionnistes. Chapter in Traitement de données complexes, Hermès-Lavoisier, 2003.
2002
Siolas, G.; d’Alché-Buc, F.Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling.ICANN 2002, LNCS 2415, pp. 769–774, Springer.
Liva Ralaivola, Florence d’Alché-Buc, Incremental Support Vector Machine Learning: A Local Approach. ICANN 2001: 322-330
d’Alché-Buc, F.; Ralaivola, L.Incremental Learning Algorithms for Classification and Regression: local strategies. CASYS 2001 (best paper award), AIP Conference Proceedings 627, pp. 320–329.
