| 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 | 618 | 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 | 425 | 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 | 399* | 2020 |
| 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 | 201 | 2007 |
| 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 | 201 | 2000 |
| Incremental support vector machine learning: A local approach L Ralaivola, F d’Alché-Buc Artificial Neural Networks—ICANN 2001: International Conference Vienna …, 2001 | 172 | 2001 |
| Semi-supervised marginboost F d'Alché-Buc, Y Grandvalet, C Ambroise Advances in neural information processing systems, 553-560, 2001 | 123 | 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 | 113 | 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 | 106 | 2016 |
| 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 | 97 | 2011 |
| 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 | 95 | 2015 |
| 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 | 91 | 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 | 83 | 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 | 79 | 2004 |
| Functional isolation forest G Staerman, P Mozharovskyi, S Clémençon, F d’Alché-Buc Asian Conference on Machine Learning, 332-347, 2019 | 72 | 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 | 71 | 2016 |
| 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 | 71 | 2006 |
| 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 | 58 | 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 | 57 | 2015 |
| Random Fourier Features For Operator-Valued Kernels AB Brault, Heinonen 8th ACML 2016 63 (JMLR Workshop and Conference proceedings), 110-125, 2016 | 56* | 2016 |
Liva RalaivolaCriteo AI LabVerified email at criteo.com
