Robust Learning via Cause-Effect Models
By Bernhard Schölkopf, Dominik Janzing, Jan Peters, Kun Zhang. 2011
7 citations recorded by the source corpus.
We consider the problem of function estimation in the case where the data distribution may shift between training and test time, and additional information about it may be available at test time. This relates to popular scenarios such as covariate shift, concept drift, transfer learning and semi-supervised learning. This working paper discusses how these tasks could be tackled depending on the kind of changes of the distributions. It argues that knowledge of an underlying causal direction can facilitate several of these tasks.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Bernhard Schölkopf
- Dominik Janzing
- Arthur Gretton
- Kenji Fukumizu
- Jan Peters
- Kun Zhang
- David Lopez-Paz
- Alex Smola
Citations in the corpus
- Causality for Machine Learning (2019, methodology): “tex, submitted to the arXiv shortly after (Schölkopf et al., 2011).”
- On causal and anticausal learning (2012, neutral): “Finally, we point out that an earlier version of the present work appeared as (Schölkopf et al., 2011).”