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Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
Michael Oberst · Nikolaj Thams · David Sontag

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in Spurious correlations, Invariance, and Stability (SCIS) »
Event URL: https://openreview.net/forum?id=Kj7XyIpSCkX »

We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance. To ensure that these shifts are plausible, we parameterize them in terms of interpretable changes in causal mechanisms of observed variables. This defines a parametric robustness set of plausible distributions and a corresponding worst-case loss. We construct a local approximation to the loss under shift, and show that problem of finding worst-case shifts can be efficiently solved.

Keywords: Causality · robustness · distribution shift · Dataset Shift

Author Information

Michael Oberst (MIT)
Nikolaj Thams (University of Copenhagen)
David Sontag (Massachusetts Institute of Technology)

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