Semi-supervised interpolation in an anticausal learning scenario

By Dominik Janzing, Bernhard Schölkopf. 2015

26 citations recorded by the source corpus.

Subfields: Causal Inference & Discovery, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Causal Inference & Discovery

1. Bernhard Schölkopf
2. Yoshua Bengio
3. Anirudh Goyal
4. Stefan Bauer
5. Nan Rosemary Ke
6. Francesco Locatello
7. Nal Kalchbrenner
8. Dominik Janzing

In Signal Processing

1. Bernhard Schölkopf
2. Stefan Bauer
3. Anirudh Goyal
4. Yoshua Bengio
5. Nal Kalchbrenner
6. Nan Rosemary Ke
7. Francesco Locatello
8. Julius von Kügelgen

Citations in the corpus

• Toward Causal Representation Learning (2021, background): “On the SSL side, subsequent developments include further theoretical analyses [125], [188, Section 5.1.2] and a form of conditional SSL [261].”
• Causality for Machine Learning (2019, background): “…2016; Pfister et al., 2018a; Heinze-Deml et al., 2017; Heinze-Deml and Meinshausen, 2017).16 On the SSL side, subsequent developments include further theoretical analyses (Janzing and Schölkopf, 2015; Peters et al., 2017, Section 5.1.2) and a form of conditional SSL (von Kügelgen et al., 2019).”
• Error asymmetry in causal and anticausal regression (2016, background): “Janzing and Schölkopf (2015) showed that if this assumption holds and / is not the identity function, then / 10 has a positive dependency with p(E)”
• Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features (2018, background): “With effect features ( Y −→ X ), on the other hand, semi-supervised learning is, in principle, possible (Janzing and Schölkopf, 2015).”
• A Survey of Learning Causality with Data: Problems and Methods (2018, background): “Janzing and Schölkopf [67] have proved that SSL indeed outperforms supervised learning in the anti-causal direction whenever P(X) and P(Y |X) are independent.”