| MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets PA Mattei, J Frellsen International Conference on Machine Learning, 4413-4423, 2019 | 289 | 2019 |
| Leveraging the exact likelihood of deep latent variable models PA Mattei, J Frellsen Advances in Neural Information Processing Signals 31, 3859-3870, 2018 | 66 | 2018 |
| not-MIWAE: Deep generative modelling with missing not at random data NB Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2021 | 61 | 2021 |
| Asteroid taxonomy from cluster analysis of spectrometry and albedo M Mahlke, B Carry, PA Mattei Astronomy & Astrophysics 665, A26, 2022 | 44 | 2022 |
| How to deal with missing data in supervised deep learning? N Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2022 | 40 | 2022 |
| Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation S Wiqvist, PA Mattei, U Picchini, J Frellsen International Conference on Machine Learning, 6798-6807, 2019 | 38 | 2019 |
| Deep adversarial Gaussian mixture auto-encoder for clustering W Harchaoui, PA Mattei, C Bouveyron OpenReview preprint, 2017 | 35 | 2017 |
| Bayesian variable selection for globally sparse probabilistic PCA C Bouveyron, P Latouche, PA Mattei Electronic Journal of Statistics 12 (2), 3036-3070, 2018 | 31 | 2018 |
| Exact dimensionality selection for Bayesian PCA C Bouveyron, P Latouche, PA Mattei Scandinavian Journal of Statistics 47 (1), 196-211, 2020 | 23 | 2020 |
| Globally sparse probabilistic PCA PA Mattei, C Bouveyron, P Latouche International Conference on Artificial Intelligence and Statistics, 976-984, 2016 | 18 | 2016 |
| Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression P Latouche, PA Mattei, C Bouveyron, J Chiquet Journal of Multivariate Analysis 146, 177-190, 2016 | 16 | 2016 |
| Model-agnostic out-of-distribution detection using combined statistical tests F Bergamin, PA Mattei, JD Havtorn, H Senetaire, H Schmutz, L Maaløe, ... International Conference on Artificial Intelligence and Statistics, 10753-10776, 2022 | 15 | 2022 |
| A parsimonious tour of bayesian model uncertainty PA Mattei arXiv preprint arXiv:1902.05539, 2019 | 15 | 2019 |
| Don’t fear the unlabelled: safe semi-supervised learning via debiasing H Schmutz, O Humbert, PA Mattei International Conference on Learning Representations, 2023 | 14* | 2023 |
| Refit your encoder when new data comes by PA Mattei, J Frellsen 3rd NeurIPS workshop on Bayesian Deep Learning, 2018 | 11 | 2018 |
| Multiplying a Gaussian matrix by a Gaussian vector PA Mattei Statistics & Probability Letters 128, 67-70, 2017 | 9 | 2017 |
| Tensor decomposition for learning Gaussian mixtures from moments R Khouja, PA Mattei, B Mourrain Journal of Symbolic Computation 113, 193-210, 2022 | 8 | 2022 |
| Class‐specific variable selection in high‐dimensional discriminant analysis through Bayesian Sparsity F Orlhac, PA Mattei, C Bouveyron, N Ayache Journal of Chemometrics 33 (2), e3097, 2019 | 8 | 2019 |
| Generalised mutual information for discriminative clustering L Ohl, PA Mattei, C Bouveyron, W Harchaoui, M Leclercq, A Droit, ... Advances in Neural Information Processing Systems 35, 3377-3390, 2022 | 5 | 2022 |
| Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. A Sportisse, H Schmutz, O Humbert, C Bouveyron, PA Mattei International Conference on Machine Learning, 32521-32539, 2023 | 4 | 2023 |
Jes FrellsenAssociate Professor, Technical University of Denmark (DTU)在 dtu.dk 的电子邮件经过验证
