61 citations · 253 across the 29 of their papers we have counts for
7 papers · 1 filter
Federated Epidemic Surveillance
Ruiqi Lyu, Roni Rosenfeld, Bryan Wilder
Epidemic surveillance is a challenging task, especially when crucial data is fragmented across institutions and data custodians are unable or unwilling to share it. This study aims…
Computationally Assisted Quality Control for Public Health Data Streams
Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld +1
Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the mo…
Statistical Inference Under Constrained Selection Bias
Santiago Cortes-Gomez, Mateo Dulce, Carlos Patino +1
Large-scale datasets are increasingly being used to inform decision making. While this effort aims to ground policy in real-world evidence, challenges have arisen as selection bias…
Auditing Fairness by Betting
Ben Chugg, Santiago Cortes-Gomez, Bryan Wilder +1
We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models. Whereas previous work relies on a fixed-sampl…
Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize
Sanket Shah, Andrew Perrault, Bryan Wilder +1
Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty. The central research question it asks is, "How can the structure of a…
Ideal Abstractions for Decision-Focused Learning
Michael Poli, Stefano Massaroli, Stefano Ermon +2
We present a methodology for formulating simplifying abstractions in machine learning systems by identifying and harnessing the utility structure of decisions. Machine learning tas…