Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning

Publication
International Conference on Learning Representations
Natalie Dullerud
Natalie Dullerud

Natalie Dullerud is a second-year M.S. student at University of Toronto, co-supervised by Dr. Marzyeh Ghassemi and Dr. Nicolas Papernot. Her research broadly focuses on various facets of fair and equitable machine learning; intersections between conceptualizations of privacy and machine learning; and applications in medical and biological contexts. Previously, she completed her B.S. in Mathematics at University of Southern California.

Kimia Hamidieh
Kimia Hamidieh

Kimia is a Ph.D. student in Computer Science at U of T and Vector Institute. Her research interests involve devising strategies for learning fair representations with respect to certain protected attributes from imbalanced and long-tailed distribution data. Currently, she is conducting research focusing on fairness in self-supervised representation learning. Previously, she graduated from Sharif University of Technology with a B.Sc. in Computer Engineering and interned at IST Austria.

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