Florence d'Alché-Buc
Florence d'Alché-Buc
Télécom Paris, Institut Polytechnique de Paris
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Cited by
Cited by
Gene networks inference using dynamic Bayesian networks
BE Perrin, L Ralaivola, A Mazurie, S Bottani, J Mallet, F d'Alché-Buc
Bioinformatics-Oxford 19 (2), 138-148, 2003
Advances in Neural Information Processing Systems (32), 2019, ed:Curran Associates
H Wallach, H Larochelle, A Beygelzimer, F d’Alché-Buc, E Fox, R Garnett
Inc.: Red Hook, NY, USA 32, 8024-8035, 2019
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
HL Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière ...
CoRR abs, 2020
Support vector machines based on a semantic kernel for text categorization
G Siolas, F d'Alché-Buc
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural …, 2000
Estimating parameters and hidden variables in nonlinear state-space models based on ODEs for biological networks
M. Quach, N Brunel, F d'Alché-Buc
Bioinformatics 23 (23), 3209-3216, 2007
Incremental support vector machine learning: A local approach
L Ralaivola, F d’Alché-Buc
Artificial Neural Networks—ICANN 2001: International Conference Vienna …, 2001
Semi-supervised marginboost
F d'Alché-Buc, Y Grandvalet, C Ambroise
Advances in neural information processing systems, 553-560, 2001
Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues
G Michailidis, F d’Alché-Buc
Mathematical biosciences 246 (2), 326-334, 2013
Fast metabolite identification with input output kernel regression
C Brouard, H Shen, K Dührkop, F d'Alché-Buc, S Böcker, J Rousu
Bioinformatics 32 (12), i28-i36, 2016
RAR/RXR binding dynamics distinguish pluripotency from differentiation associated cis-regulatory elements
B Chatagnon, Veber, Morin, Bedo, Triqueneaux, Sémon, Laudet, d'Alché-Buc
Nucleic Acids Research 43 (10), 4833-4854, 2015
Semi-supervised penalized output kernel regression for link prediction
C Brouard, F d'Alché-Buc, M Szafranski
Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011
Flexible and context-specific AI explainability: a multidisciplinary approach
V Beaudouin, I Bloch, D Bounie, S Clémençon, F d'Alché-Buc, J Eagan, ...
arXiv preprint arXiv:2003.07703, 2020
Time series filtering, smoothing and learning using the kernel Kalman filter
L Ralaivola, F d'Alché-Buc
Proceedings. 2005 IEEE International Joint Conference on Neural Networks …, 2005
Dynamical modeling with kernels for nonlinear time series prediction
L Ralaivola, F d'Alche-Buc
Advances in neural information processing systems 16, 129, 2004
Kernelizing the output of tree-based methods
P Geurts, L Wehenkel, F d'Alché-Buc
Proceedings of the 23rd international conference on Machine learning, 345-352, 2006
Functional isolation forest
G Staerman, P Mozharovskyi, S Clémençon, F d’Alché-Buc
Asian Conference on Machine Learning, 332-347, 2019
Input output kernel regression: Supervised and semi-supervised structured output prediction with operator-valued kernels
C Brouard, M Szafranski, F d’Alché-Buc
Journal of Machine Learning Research 17 (176), 1-48, 2016
Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset
C Auliac, V Frouin, X Gidrol, F d'Alché-Buc
BMC bioinformatics 9, 1-14, 2008
Operator-valued kernel-based vector autoregressive models for network inference
N Lim, F d’Alché-Buc, C Auliac, G Michailidis
Machine learning 99, 489-513, 2015
Joint quantile regression in vector-valued RKHSs
M Sangnier, O Fercoq, F d'Alché-Buc
Advances in Neural Information Processing Systems 29, 2016
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