150 citations · 554 across the 18 of their papers we have counts for
5 papers · 1 filter
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter, Heinrich Jiang, Serena Wang +4
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study th…
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang +5
Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the gene…
Proxy Fairness
Maya Gupta, Andrew Cotter, Mahdi Milani Fard +1
We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve…
Interpretable Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang +4
We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice net…
Two-Player Games for Efficient Non-Convex Constrained Optimization
Andrew Cotter, Heinrich Jiang, Karthik Sridharan
In recent years, constrained optimization has become increasingly relevant to the machine learning community, with applications including Neyman-Pearson classification, robust opti…