AAAI 2025technical0 citations
Representation Learning: A Causal Perspective
Abstract
Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing various desiderata associated with learned representations; e.g., that they be non-spurious, efficient, or disentangled. It can be challenging, however, to turn these intuitive desiderata into formal criteria that can be measured and enhanced based on observed data. In this paper, we take a causal perspective on representation learning, formalizing desiderata like non-spuriousness and demonstrating their practical utility.
BibTeX
@article{Wang_2025, title={Representation Learning: A Causal Perspective}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35124}, DOI={10.1609/aaai.v39i27.35124}, abstractNote={Representation learning constructs low-dimensional representations to
summarize essential features of high-dimensional data. This learning
problem is often approached by describing various desiderata
associated with learned representations; e.g., that they be
non-spurious, efficient, or disentangled. It can be challenging,
however, to turn these intuitive desiderata into formal criteria that
can be measured and enhanced based on observed data. In this paper, we
take a causal perspective on representation learning, formalizing
desiderata like non-spuriousness and demonstrating their practical utility.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wang, Yixin}, year={2025}, month={Apr.}, pages={28731-28731} }