41 citations · 48 across the 2 of their papers we have counts for
3 papers
stat.ML2020★ 7 cited
I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models
Adarsh Subbaswamy, Suchi Saria
Shifts in environment between development and deployment cause classical supervised learning to produce models that fail to generalize well to new target distributions. Recently, m…
cs.LG2019★ 41 cited
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…
stat.ML2018
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
Adarsh Subbaswamy, Peter Schulam, Suchi Saria
Classical supervised learning produces unreliable models when training and target distributions differ, with most existing solutions requiring samples from the target domain. We pr…