68 citations · 166 across the 8 of their papers we have counts for
9 papers · 1 filter
Partial Identifiability in Discrete Data With Measurement Error
Noam Finkelstein, Roy Adams, Suchi Saria +1
When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest. These assumptions sho…
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…
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…
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…