Causal relationships in nonlinear representation learning
Patrik Reizinger (Ph.D. Student)
The robustness of underlying representations is a key for deploying machine learning systems in real-world applications. This requires both the possibly unique identification of the latent factors themselves and their connections as well. Although the topic is an active research area, there are no satisfactory algorithms beyond the linear case that are able to address the complex relationships of latent factors in a scalable manner. As part of my Ph.D. research, I will be targeting these issues by applying the paradigms of Nonlinear Independent Component Analysis and causal inference.
|Primary Host:||Matthias Bethge (University of Tübingen)|
|Exchange Host:||Ferenc Huszár (University of Cambridge)|
|PhD Duration:||01 April 2021 - 31 March 2024|
|Exchange Duration:||01 September 2022 - 28 February 2023 - Ongoing|