Janke, Tim ; Ghanmi, Mohamed ; Steinke, Florian (2021)
Implicit Generative Copulas.
35th International Conference on Neural Information Processing Systems (NeurIPS 2021). virtual Conference (07.12.2021-10.12.2021)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Copulas are a powerful tool for modeling multivariate distributions as they allow to separately estimate the univariate marginal distributions and the joint dependency structure. However, known parametric copulas offer limited flexibility especially in high dimensions, while commonly used non-parametric methods suffer from the curse of dimensionality. A popular remedy is to construct a tree-based hierarchy of conditional bivariate copulas.In this paper, we propose a flexible, yet conceptually simple alternative based on implicit generative neural networks.The key challenge is to ensure marginal uniformity of the estimated copula distribution.We achieve this by learning a multivariate latent distribution with unspecified marginals but the desired dependency structure.By applying the probability integral transform, we can then obtain samples from the high-dimensional copula distribution without relying on parametric assumptions or the need to find a suitable tree structure.Experiments on synthetic and real data from finance, physics, and image generation demonstrate the performance of this approach.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Janke, Tim ; Ghanmi, Mohamed ; Steinke, Florian |
| Art des Eintrags: | Bibliographie |
| Titel: | Implicit Generative Copulas |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Kollation: | 16 Seiten |
| Veranstaltungstitel: | 35th International Conference on Neural Information Processing Systems (NeurIPS 2021) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 07.12.2021-10.12.2021 |
| URL / URN: | http://arxiv.org/abs/2109.14567 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Copulas are a powerful tool for modeling multivariate distributions as they allow to separately estimate the univariate marginal distributions and the joint dependency structure. However, known parametric copulas offer limited flexibility especially in high dimensions, while commonly used non-parametric methods suffer from the curse of dimensionality. A popular remedy is to construct a tree-based hierarchy of conditional bivariate copulas.In this paper, we propose a flexible, yet conceptually simple alternative based on implicit generative neural networks.The key challenge is to ensure marginal uniformity of the estimated copula distribution.We achieve this by learning a multivariate latent distribution with unspecified marginals but the desired dependency structure.By applying the probability integral transform, we can then obtain samples from the high-dimensional copula distribution without relying on parametric assumptions or the need to find a suitable tree structure.Experiments on synthetic and real data from finance, physics, and image generation demonstrate the performance of this approach. |
| Freie Schlagworte: | emergenCITY_CPS |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Energieinformationsnetze und Systeme (EINS) 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY Forschungsfelder Forschungsfelder > Energy and Environment Forschungsfelder > Energy and Environment > Integrated Energy Systems |
| TU-Projekte: | HMWK|III L6-519/03/05.001-(0016)|emergenCity TP Bock |
| Hinterlegungsdatum: | 04 Okt 2021 10:35 |
| Letzte Änderung: | 19 Dez 2024 10:42 |
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