Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Patrick Blöbaum"'
Publikováno v:
PeerJ Computer Science, Vol 5, p e169 (2019)
We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assumption of an independence between the function relating cause and effec
Externí odkaz:
https://doaj.org/article/96a6da906de549c6978a7990c3ff97ae
Autor:
Shohei Shimizu, Patrick Blöbaum
Publikováno v:
Direction Dependence in Statistical Modeling. :111-130
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8bd43d97f7e7e8a510b5effa81c1893
https://doi.org/10.1002/9781119523024.ch5
https://doi.org/10.1002/9781119523024.ch5
Publikováno v:
Behaviormetrika. 44:491-512
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated. Recent work showed that knowledge about the real underlying causal structure of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e2e4fef7467f3d346935881ccf93f2a
https://doi.org/10.1007/s41237-017-0022-z
https://doi.org/10.1007/s41237-017-0022-z
Autor:
Shohei Shimizu, Patrick Blöbaum
Publikováno v:
MLSP
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models with meaningful parameters, the causal relationships between the pred
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::454a74bdc50a3cf056f9127bedd4f950
http://arxiv.org/abs/1709.00776
http://arxiv.org/abs/1709.00776
Publikováno v:
PUB-Publications at Bielefeld University
We investigate the suitability of unsupervised dimensionality reduction (DR) for transfer learning in the context of different representations of the source and target domain. Essentially, unsupervised DR establishes a link of source and target domai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::477699d4d21add52c1b00c4751e2bbf7
https://pub.uni-bielefeld.de/record/2900325
https://pub.uni-bielefeld.de/record/2900325
Publikováno v:
Advanced Methodologies for Bayesian Networks ISBN: 9783319283784
AMBN@JSAI-isAI
AMBN@JSAI-isAI
Having knowledge about the real underlying causal structure of a data generation process has various implications for different machine learning problems. We address the idea of causal and anticausal learning with respect to a comparison of discrimin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9693bd5e2bed430a65914198b3142b56
https://doi.org/10.1007/978-3-319-28379-1_15
https://doi.org/10.1007/978-3-319-28379-1_15