Corpus ID: 232097550

On Disentangled Representations Learned from Correlated Data

@inproceedings{Trauble2021OnDR,
  title={On Disentangled Representations Learned from Correlated Data},
  author={Frederik Trauble and Elliot Creager and Niki Kilbertus and Francesco Locatello and Andrea Dittadi and Anirudh Goyal and B. Scholkopf and Stefan Bauer},
  booktitle={ICML},
  year={2021}
}
The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset… Expand
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning

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