Linzner, Dominik ; Koeppl, Heinz (2023)
Active Learning of Continuous-time Bayesian Networks through Interventions.
Thirty-eighth International Conference on Machine Learning. virtual Conference (18.07.2021-24.07.2021)
doi: 10.26083/tuprints-00023308
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
We consider the problem of learning structures and parameters of Continuous-time Bayesian Networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences. A popular approach to overcome this is Bayesian optimal experimental design (BOED). However, BOED becomes infeasible in high-dimensional settings, as it involves integration over all possible experimental outcomes. We propose a novel criterion for experimental design based on a variational approximation of the expected information gain. We show that for CTBNs, a semi-analytical expression for this criterion can be calculated for structure and parameter learning. By doing so, we can replace sampling over experimental outcomes by solving the CTBNs master-equation, for which scalable approximations exist. This alleviates the computational burden of sampling possible experimental outcomes in high-dimensions. We employ this framework to recommend interventional sequences. In this context, we extend the CTBN model to conditional CTBNs to incorporate interventions. We demonstrate the performance of our criterion on synthetic and real-world data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Linzner, Dominik ; Koeppl, Heinz |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Active Learning of Continuous-time Bayesian Networks through Interventions |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2021 |
| Verlag: | PMLR |
| Buchtitel: | Proceedings of the 38th International Conference on Machine Learning |
| Reihe: | Proceedings of Machine Learning Research |
| Band einer Reihe: | 139 |
| Veranstaltungstitel: | Thirty-eighth International Conference on Machine Learning |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 18.07.2021-24.07.2021 |
| DOI: | 10.26083/tuprints-00023308 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23308 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | We consider the problem of learning structures and parameters of Continuous-time Bayesian Networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences. A popular approach to overcome this is Bayesian optimal experimental design (BOED). However, BOED becomes infeasible in high-dimensional settings, as it involves integration over all possible experimental outcomes. We propose a novel criterion for experimental design based on a variational approximation of the expected information gain. We show that for CTBNs, a semi-analytical expression for this criterion can be calculated for structure and parameter learning. By doing so, we can replace sampling over experimental outcomes by solving the CTBNs master-equation, for which scalable approximations exist. This alleviates the computational burden of sampling possible experimental outcomes in high-dimensions. We employ this framework to recommend interventional sequences. In this context, we extend the CTBN model to conditional CTBNs to incorporate interventions. We demonstrate the performance of our criterion on synthetic and real-world data. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-233085 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik |
| Hinterlegungsdatum: | 31 Mär 2023 08:26 |
| Letzte Änderung: | 04 Apr 2023 13:04 |
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| Suche nach Titel in: | TUfind oder in Google |
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