Representation Learning: A Causal Perspective

Yixin Wang, University of Michigan

18.07.2024

5:00 pm - 6:30 pm

Online via Zoom

Representation learning aims to create low-dimensional representations that capture essential features of high-dimensional data, such as images and texts. Ideally, these representations should efficiently capture meaningful, non-spurious features and be disentangled for interpretability. However, defining and enforcing these qualities is challenging.

In this talk, a causal perspective on representation learning is presented. The desiderata for effective representation learning are formalized using counterfactual concepts, which lead to metrics and algorithms designed to achieve efficient, non-spurious, and disentangled representations. The talk covers the theoretical foundations of the proposed algorithm and demonstrates its performance in both supervised and unsupervised settings.

Organized by:

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Representation Learning: A Causal Perspective

Yixin Wang, University of Michigan

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