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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 to appear in AISTATS 2024.
- Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharvoskyi, Gaël Richard, Florence d’Alché-Buc. Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorizationto appear in IEEE Tr. ASLP, 2024.
2023
- 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.
- Tamim El Ahmad, Pierre Laforgue, Florence d’Alché-Buc. Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches, TMLR, 2023.
- 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, Renewable Energy, May, 2023.
2022
- Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d’Alché-Buc, Vector-Valued Least-Squares Regression under Output Regularity Assumptions, Journal of Mach. Learn. Res., 23(344):1−50, 2022.
- Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d’Alché-Buc: Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. ICML 2022.
- Alex Lambert, Dimitri Bouche, Zoltan Szabò, Florence d’Alché-Buc: Functional Output Regression with Infimal Convolution: Exploring the Huber and epsilon-insensitive Losses. ICML 2022.
- 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 . NeurIPS 2022.
- 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 , https://arxiv.org/abs/2204.09362, 2022.
2021
- Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d’Alché-Buc: When OT meets MoM: Robust estimation of Wasserstein Distance. AISTATS 2021: 136-144.
- Dimitri Bouche, Marianne Clausel, François Roueff, Florence d’Alché-Buc: Nonlinear Functional Output Regression: A Dictionary Approach. AISTATS 2021: 235-243.
- Alex Lambert, Sanjeel Parekh, Zoltán Szabó, Florence d’Alché-Buc: Emotion Transfer Using Vector-Valued Infinite Task Learning. CoRR abs/2102.05075 (2021)
- Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d’Alché-Buc:
- Depth-based pseudo-metrics between probability distributions. CoRR abs/2103.12711 (2021)
- 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), JMLR 2021.
- Jayneel Parekh, Pavlo Mozharovksyi, Florence d’Alché-Buc: FLINT: A Framework for learning with Interpretation. NeurIPS 2021.
2020
- Dimitri Bouche, Marianne Clausel, François Roueff, Florence d’Alché-Buc:
Nonlinear Functional Output Regression: a Dictionary Approach. CoRR abs/2003.01432 (2020) - 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)
- 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)
- Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc . Audio-Based Detection of Explicit Content in Music, ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020)
2019
- 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, 8-14 December 2019, Vancouver, BC, Canada. 2019 [contents].
- Romain Brault*, Alex Lambert*, Zoltan Szabo, Maxime Sangnier, Florence d’Alché-Buc, Infinite-Task Learning with RKHSs, AISTATS 2019: 1294-1302.
- Pierre Laforgue, Stephan Clémençon, Florence d’Alché-Buc, Autoencoding any data through kernel autoencoders, AISTATS 2019: 1061-1069.
- 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), EMNLP 2019.
- 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)
- Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d’Alché-Buc: Functional Isolation Forest, ACML 2019.
- Céline Brouard, Antoine Bassé, Florence d’Alché-Buc and Juho Rousu, Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models, Metabolites 2019, 9(8), 160; https://doi.org/10.3390/metabo9080160 – 01 Aug 2019.
- Pierre Laforgue, Alex Lambert, Luc Motte, Florence d’Alché-Buc: On the Dualization of Operator-Valued Kernel Machines. CoRR abs/1910.04621 (2019)
2018
- 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, ICML 2018.
- Anna Korba, Alexandre Garcia, Florence d’Alché-Buc, A structured Prediction Approach for Label Ranking, arxiv, 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, 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.
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.
- 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 Analysis, January, 2017.
- Moussab
Djerrab, Alexandre Garcia, Maxime Sangnier and Florence d’Alché-Buc,
Structured Prediction by minimization of a Fisher Surrogate Loss,
communication à CAP 2017.
- Romain Brault, Florence d’Alché-Buc, Random Fourier Features for operator-valued kernels (long version) https://hal.archives-ouvertes.fr/hal-01313005
2016
- 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.
2015
- 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. Bioinformatics 31(5): 728-735 (2015).
- Chatagnon,
Amandine; Veber, Philippe; Bedo, Justin; Morin, Valerie; Triqueneaux,
Gerard; SEMON, Marie; Laudet, Vincent; d’Alché-Buc, Florence and Benoit,
Gérard , RAR/RXR binding dynamics distinguish pluripotency from
differentiation associated cis-regulatory elements, Nucleic Acids Res. May 26;43(10):4833-54, Apr 20 (2015).
- Néhémy Lim, Florence d’Alché-Buc, Cédric Auliac, George Michailidis:Operator-valued kernel-based vector autoregressive models for network inference. Machine Learning 99(3): 489-513 (2015)
2014
- 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.
2013
- 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 Bioinformatics 14: 273 (2013)
- 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. Bioinformatics 29(11): 1416-1423 (2013)
- 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).
- 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 (2013)
- Markus Heinonen, Olivier Guipaud, Fabien Milliat, Valérie Buard, Béatrice Micheau, Florence d’Alché-Buc, Time-dependent gaussian process regression and significance analysis for sparse time-series, 7th international workshop on Machine Learning in Systems Biology, SIG ISMB/ECCB 2013, Berlin, July 19-20, 2013.
- Arnaud Fouchet, Jean-Marc Delosme, Florence d’Alché-Buc, Gene Regulatory Network Inference using ensembles of Local Multiple Kernel Models, 7th international workshop on Machine Learning in Systems Biology, SIG ISMB/ECCB 2013, Berlin, July 19-20, 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.
2012
- 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.
- 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.
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.
2009
- 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).
2008
- 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).
2007
- 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.
2006
- Pierre Geurts, Louis Wehenkel, Florence d’Alché-Buc: Kernelizing the output of tree-based methods. ICML 2006: 345-352.
- BOOK EDITION: 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
- http://www.informatik.uni-trier.de/~ley/pers/hd/d/d=Alch=eacute==Buc:Florence
- 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)