Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2011 | CoRR | Covariate shift,Multi-task learning,Computer science,Transfer of learning,Robust learning,Concept drift,Artificial intelligence,Machine learning |
DocType | Volume | ISSN |
Journal | abs/1112.2738 | A version of this paper has been published as "On Causal and
Anticausal Learning" in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012) |
Citations | PageRank | References |
1 | 0.51 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernhard Schölkopf | 1 | 23120 | 3091.82 |
| Dominik Janzing | 2 | 723 | 65.30 |
| Jonas Peters | 3 | 505 | 31.25 |
| Kun Zhang | 4 | 772 | 83.37 |