The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

Despite their renowned in-domain predictive power, convolutional neural networks are
known to rely more on high-frequency patterns that humans deem superficial than on low …

The machine learning community's interest in causality has significantly increased in recent
years. My understanding of causality has been shaped by Judea Pearl and a number of …

Abstract Domain generalization aims to learn from multiple source domains a predictive
model that can generalize to unseen target domains. One essential problem in domain …

Due to the ability of deep neural nets to learn rich representations, recent advances in
unsupervised domain adaptation have focused on learning domain-invariant features that …