Search Results for author: Frances Ding

Found 4 papers, 2 papers with code

Grounding Representation Similarity Through Statistical Testing

no code implementations NeurIPS 2021 Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt

To understand neural network behavior, recent works quantitatively compare different networks' learned representations using canonical correlation analysis (CCA), centered kernel alignment (CKA), and other dissimilarity measures.

Retiring Adult: New Datasets for Fair Machine Learning

1 code implementation NeurIPS 2021 Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt

Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning.

Fairness

Grounding Representation Similarity with Statistical Testing

3 code implementations3 Aug 2021 Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt

To understand neural network behavior, recent works quantitatively compare different networks' learned representations using canonical correlation analysis (CCA), centered kernel alignment (CKA), and other dissimilarity measures.

Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

no code implementations20 Jun 2020 Zhun Deng, Frances Ding, Cynthia Dwork, Rachel Hong, Giovanni Parmigiani, Prasad Patil, Pragya Sur

We study an adversarial loss function for $k$ domains and precisely characterize its limiting behavior as $k$ grows, formalizing and proving the intuition, backed by experiments, that observing data from a larger number of domains helps.

Domain Generalization Fairness

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