| Autoregressive convolutional neural networks for asynchronous time series M Binkowski, G Marti, P Donnat International Conference on Machine Learning, 580-589, 2018 | 195 | 2018 |
| A review of two decades of correlations, hierarchies, networks and clustering in financial markets G Marti, F Nielsen, M Bińkowski, P Donnat Progress in information geometry: Theory and applications, 245-274, 2021 | 165 | 2021 |
| CorrGAN: Sampling realistic financial correlation matrices using generative adversarial networks G Marti ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 57 | 2020 |
| Clustering Financial Time Series: How Long is Enough? G Marti, S Andler, F Nielsen, P Donnat International Joint Conference on Artificial Intelligence, 2016 | 34 | 2016 |
| Toward a generic representation of random variables for machine learning P Donnat, G Marti, P Very Pattern Recognition Letters 70, 24-31, 2016 | 28 | 2016 |
| A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series G Marti, P Very, P Donnat, F Nielsen IEEE ICMLA, 2015 | 25 | 2015 |
| Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series G Marti, S Andler, F Nielsen, P Donnat IEEE Workshop on Statistical Signal Processing, 2016 | 24 | 2016 |
| Optimal Copula Transport for Clustering Multivariate Time Series G Marti, F Nielsen, P Donnat IEEE ICASSP, 2015 | 21 | 2015 |
| On clustering financial time series: a need for distances between dependent random variables G Marti, F Nielsen, P Donnat, S Andler Computational information geometry: for image and signal processing, 149-174, 2017 | 15 | 2017 |
| Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering G Marti, S Andler, F Nielsen, P Donnat NIPS 2016 Time Series Workshop, 59-69, 2017 | 11 | 2017 |
| cCorrGAN: Conditional correlation GAN for learning empirical conditional distributions in the elliptope G Marti, V Goubet, F Nielsen International Conference on Geometric Science of Information, 613-620, 2021 | 7 | 2021 |
| HCMapper: An interactive visualization tool to compare partition-based flat clustering extracted from pairs of dendrograms G Marti, P Donnat, F Nielsen, P Very arXiv preprint arXiv:1507.08137, 2015 | 6 | 2015 |
| Clustering patterns connecting COVID-19 dynamics and Human mobility using optimal transport F Nielsen, G Marti, S Ray, S Pyne Sankhya B 83, 167-184, 2021 | 4 | 2021 |
| Clustering Random Walk Time Series G Marti, F Nielsen, P Very, P Donnat Geometric Science of Information: Second International Conference, GSI 2015 …, 2015 | 3 | 2015 |
| Comment partitionner automatiquement des marches aléatoires? Avec application à la finance quantitative G Marti, F Nielsen, P Very, P Donnat GRETSI, 2015 | 2 | 2015 |
| Enriching Datasets with Demographics through Large Language Models: What's in a Name? K AlNuaimi, G Marti, M Ravaut, A AlKetbi, A Henschel, R Jaradat arXiv preprint arXiv:2409.11491, 2024 | 1 | 2024 |
| Putting self-supervised token embedding on the tables M Szafraniec, G Marti, P Donnat 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 1 | 2017 |
| Some contributions to the clustering of financial time series and applications to credit default swaps G Marti Université Paris Saclay (COmUE), 2017 | 1 | 2017 |
| Mapping Hong Kong's Financial Ecosystem: A Network Analysis of the SFC's Licensed Professionals and Institutions A AlKetbi, G Marti, K AlNuaimi, R Jaradat, A Henschel arXiv preprint arXiv:2410.07970, 2024 | | 2024 |
Philippe DonnatHellebore CapitalVerified email at helleborecapital.com
