Is Independence all you need? On the Generalization of Representations Learned from Correlated Data
By Frederik Trauble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer. 2020
19 citations recorded by the source corpus.
Subfields: Representation & Self-Supervised Learning, Statistical Learning Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Representation & Self-Supervised Learning
- Bernhard Schölkopf
- Francesco Locatello
- Anirudh Goyal
- Stefan Bauer
- Yoshua Bengio
- Nan Rosemary Ke
- Yash Sharma
- Nal Kalchbrenner
In Statistical Learning Theory
- Yoshua Bengio
- Stefan Bauer
- Francesco Locatello
- Nal Kalchbrenner
- Nan Rosemary Ke
- Anirudh Goyal
- Bernhard Schölkopf
- Matthias Bethge
Citations in the corpus
- Toward Causal Representation Learning (2021, methodology): “Furthermore, the SMS hypothesis can be used as supervision signal, in practice, even if PA i (cid:5) = ∅ [252].”
- Towards Causal Representation Learning (2021, methodology): “Further, the SMS hypothesis can be used as supervision signal in practice even if PAi 6= ∅ [252].”
- Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding (2020, background): “[60] Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, and Stefan Bauer.”
- Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics (2021, background): “In-processing techniques [6, 7, 8, 9, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33] try to remove sensitive information from the learned representation space.”