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Title:

Unpaired Multi-Domain Causal Representation Learning

Document type:
Konferenzbeitrag
Author(s):
Sturma, Nils; Squires, Chandler; Drton, Mathias; Uhler, Caroline
Pages contribution:
34465--34492
Abstract:
The goal of causal representation learning is to find a representation of data that consists of causally related latent variables. We consider a setup where one has access to data from multiple domains that potentially share a causal representation. Crucially, observations in different domains are assumed to be unpaired, that is, we only observe the marginal distribution in each domain but not their joint distribution. In this paper, we give sufficient conditions for identifiability of the joint...     »
Dewey Decimal Classification:
510 Mathematik
Book / Congress title:
Advances in Neural Information Processing Systems 36
Congress (additional information):
37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023)
Date of congress:
December 10 - 16, 2023
Year:
2023
Quarter:
4. Quartal
WWW:
NeurIPS 2023
Semester:
WS 23-24
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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