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David Kernert

2 accepted papers

2022

Assaying Out-Of-Distribution Generalization in Transfer Learning

NeurIPS 2022accept

Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the sa…

2022

Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies

AISTATS 2022poster

This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel m…