28 citations · 40 across the 4 of their papers we have counts for
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cs.LG2018★ 28 cited
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…
cs.LG2018★ 7 cited
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…