42 citations · 136 across the 14 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…
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization
Serena Wang, Maya Gupta, Seungil You
Given a classifier ensemble and a set of examples to be classified, many examples may be confidently and accurately classified after only a subset of the base models in the ensembl…
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…