Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

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 …

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 …

While domain adaptation has been actively researched, most algorithms focus on the single-
source-single-target adaptation setting. In this paper we propose new generalization bounds …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

All famous machine learning algorithms that comprise both supervised and semi-supervised
learning work well only under a common assumption: the training and test data follow the …

Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer
learning, with a particular focus placed on domain adaptation from a theoretical point-of …

Modern machine learning methods often require more data for training than a single expert
can provide. Therefore, it has become a standard procedure to collect data from multiple …

This paper advocates a new paradigm Personalized Empirical Risk Minimization (PERM) to
facilitate learning from heterogeneous data sources without imposing stringent constraints …

Robustness is of central importance in machine learning and has given rise to the fields of
domain generalization and invariant learning, which are concerned with improving …