57 citations · 137 across the 12 of their papers we have counts for
4 papers · 1 filter
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
Doyup Lee, Suehun Jung, Yeongjae Cheon +2
Short-term demand forecasting models commonly combine convolutional and recurrent layers to extract complex spatiotemporal patterns in data. Long-term histories are also used to co…
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…
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…