Assaying Out-Of-Distribution Generalization in Transfer Learning

1 code implementation • 19 Jul 2022 • Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello

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.

Adversarial Robustness Out-of-Distribution Generalization +1

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

no code implementations • 23 Feb 2022 • Lenon Minorics, Caner Turkmen, David Kernert, Patrick Bloebaum, Laurent Callot, Dominik Janzing

This paper proposes a new approach for testing Granger non-causality on panel data.