253 citations · 474 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones +15
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held…
Discretizing Logged Interaction Data Biases Learning for Decision-Making
Peter Schulam, Suchi Saria
Time series data that are not measured at regular intervals are commonly discretized as a preprocessing step. For example, data about customer arrival times might be simplified by…
A Review of Challenges and Opportunities in Machine Learning for Health
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam +3
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. Howe…