41 citations · 48 across the 3 of their papers we have counts for
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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…
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…
stat.ML2018
Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms
Adarsh Subbaswamy, Suchi Saria
Predictive models can fail to generalize from training to deployment environments because of dataset shift, posing a threat to model reliability and the safety of downstream decisi…