86 citations · 572 across the 39 of their papers we have counts for
6 papers · 1 filter
A Unifying Causal Framework for Analyzing Dataset Shift-stable Learning Algorithms
Adarsh Subbaswamy, Bryant Chen, Suchi Saria
Recent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known as dataset shift) has produced many methods for fi…
Tutorial: Safe and Reliable Machine Learning
Suchi Saria, Adarsh Subbaswamy
This document serves as a brief overview of the "Safe and Reliable Machine Learning" tutorial given at the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* 2…
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin, Peter Schulam, Eero Siivola +3
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based deci…
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning
Peter Schulam, Suchi Saria
To use machine learning in high stakes applications (e.g. medicine), we need tools for building confidence in the system and evaluating whether it is reliable. Methods to improve m…
Learning Models from Data with Measurement Error: Tackling Underreporting
Roy Adams, Yuelong Ji, Xiaobin Wang +1
Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to infor…
Artificial Intelligence for Social Good
Gregory D. Hager, Ann Drobnis, Fei Fang +8
The Computing Community Consortium (CCC), along with the White House Office of Science and Technology Policy (OSTP), and the Association for the Advancement of Artificial Intellige…