Group Distributionally Robust Optimization for Solving Out-Of-Domain Generalization and Finding Causal Invariant Relationships

Out-of-Domain (OOD) generalization is a challenging problem in machine learning about learning a model from one or more domains and making the model perform well on an unseen domain. Empirical Risk Minimization (ERM), the standard machine learning method, suffers from learning spurious correlation in the training domain, therefore may perform...

Evaluating the Performance of the Model Selection with Average ECE and Naive Calibration in Out-of-Domain Generalization Problems for Binary Classifiers

Out-of-domain (OOD) generalization refers to learning a model from one or more different but related domain(s) that can be used in an unknown test domain. It is challenging for existing machine learning models. Several methods have been proposed to solve this problem, and multi-domain calibration is one of these methods. Model selection with the...