A review of domain adaptation without target labels
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 …
related fields. This review asks the question: How can a classifier learn from a source …
Causal machine learning for healthcare and precision medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
Towards out-of-distribution generalization: A survey
Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …
test data follow the same statistical pattern, which is mathematically referred to as …
Toward causal representation learning
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 …
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
Causality inspired representation learning for domain generalization
Abstract Domain generalization (DG) is essentially an out-of-distribution problem, aiming to
generalize the knowledge learned from multiple source domains to an unseen target …
generalize the knowledge learned from multiple source domains to an unseen target …
Self-supervised learning with data augmentations provably isolates content from style
Self-supervised representation learning has shown remarkable success in a number of
domains. A common practice is to perform data augmentation via hand-crafted …
domains. A common practice is to perform data augmentation via hand-crafted …
In search of lost domain generalization
I Gulrajani, D Lopez-Paz - arXiv preprint arXiv:2007.01434, 2020 - arxiv.org
The goal of domain generalization algorithms is to predict well on distributions different from
those seen during training. While a myriad of domain generalization algorithms exist …
those seen during training. While a myriad of domain generalization algorithms exist …
Fishr: Invariant gradient variances for out-of-distribution generalization
Learning robust models that generalize well under changes in the data distribution is critical
for real-world applications. To this end, there has been a growing surge of interest to learn …
for real-world applications. To this end, there has been a growing surge of interest to learn …
Learning invariant graph representations for out-of-distribution generalization
Graph representation learning has shown effectiveness when testing and training graph
data come from the same distribution, but most existing approaches fail to generalize under …
data come from the same distribution, but most existing approaches fail to generalize under …
Learning substructure invariance for out-of-distribution molecular representations
Molecule representation learning (MRL) has been extensively studied and current methods
have shown promising power for various tasks, eg, molecular property prediction and target …
have shown promising power for various tasks, eg, molecular property prediction and target …