Florence d'Alché-Buc, HOMEPAGE


  • Romain Brault*, Alex Lambert*, Zoltan Szabo, Maxime Sangnier, Florence d'Alché-Buc, Infinite-Task Learning with RKHSs, arxiv, accepted to AISTATS 2019.
  • Pierre Laforgue, Stephan Clémençon, Florence d'Alché-Buc, Autoencoding any data through kernel autoencoders, arXiv , accepted to AISTATS 2019.

  • Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d’Alché-Buc. Output Fisher Embedding Regression, Machine Learning Journal 107(8-10): 1229-1256 (2018) and presented at ECML/PKDD track, 2018.
  • Alexandre Garcia, Slim Essid, Chloé Clavel, Florence d'Alché-Buc, Structured Output Learning with Abstention: Application to Accurate Opinion Prediction, arxiv, accepted at ICML 2018.
  • Anna Korba, Alexandre Garcia, Florence d'Alché-Buc, A structured Prediction Approach for Label Ranking, arxiv, accepted at NIPS 2018.
  • Markus Heinonen, Fabien Milliat, Mohamed Amine Benadjaoud, Agnès François, Valérie Buard, Florence d'Alché-Buc, Olivier Guipaud, Temporal clustering analysis of endothelial cell gene expression under a conventional radiotherapy dose fraction using Gaussian process clustering, accepted in Plos One, 2018.
  • 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.

  • Céline Brouard, Florence d’Alché-Buc, Marie Szafranski. Input Output Kernel Regression: supervised and semi-supervised structured output prediction with operator-valued kernels, hal-01216708 pdf, JMLR: 17:176 (1- 48), October, 2016.
  • Céline Brouard, Huibin Shen, Kai Dührkop, Florence d’Alché-Buc, Sebastian Böcker, Juho Rousu. Fast metabolite identification with Input Output Kernel Regression, Bioinformatics, May, 2016.
  • Romain Brault, Markus Heinonen, Florence d’Alché-Buc, Random Fourier Features for Operator-valued kernels, Proc. of  8th Asian Conference in Machine Learning, JMLR Workshop and Conference proc., 2016.
  • Romain Brault, Néhémy Lim, Florence d’Alché-Buc, Scaling up Vector Autoregressive models with operator-valued Random Fourier features, AALTD’16, joint workshop to ECML/PKDD 2016.
  • Adriana Birlutiu, Florence d’Alché-Buc, Tom Heskes, A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions, IEEE Trans. on Computational Biology and Bioinformatics, issue 99, Nov 2014.
  • 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.

  • 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
  • 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.

  • 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.
François Le Fèvre, Serge Smidtas, C. Combe, M. Durot, Florence d'Alché-Buc, Vincent Schächter: CycSim - an online tool for exploring and experimenting with genome-scale metabolic models. Bioinformatics 25(15): 1987-1988 (2009).
Brouard, C., Dubois, J., Vrain, C., Debily, M.-A. and d’Alché-Buc, F. (2009) Statistical relational learning for supervised gene regulatory network inference. Machine Learning in Systems Biology (MLSB) workshop , Ljubljana, Slovenia, (2009).
Book edition: Florence d'Alché-Buc, Actes de la Conférence d’APprentissage francophone, 2008.
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 Bioinformatics 9 (2008).
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)
Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc: Inferring biological networks with output kernel trees. BMC Bioinformatics 8(S-2) (2007).
Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc: Gradient boosting for kernelized output spaces. ICML 2007: 289-296
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.
Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc: Kernelizing the output of tree-based methods. ICML 2006: 345-352.
Joaquin Quiñonero Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc (Eds.): 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 LNCS 3944, Springer 2006, ISBN 3-540-33427-0. Previous publications > 2006 : see
Invited talks:
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.
Florence d’Alché-Buc, Learning structured and multiple outputs with operator-valued kernels,
Centre for Interdisciplinary Mathematics Workshop On Machine Learning, Oct 8-9, Uppsala, Suède, 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)