Search Results for author:
Found 5 papers, 2 papers with code
Anticipating Performativity by Predicting from Predictions
Predictions about people, such as their expected educational achievement or their credit risk, can be performative and shape the outcome that they aim to predict.
Grounding Representation Similarity Through Statistical Testing
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
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.
Grounding Representation Similarity with Statistical Testing
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
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.
