Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

RSD.(triangle inequality) We first introduce the concept of weak majorization: Majorization is
a preorder on vectors of real numbers. For a vector a∈ Rd, we denote by a↓∈ Rd the …

Materials property prediction models are usually evaluated using random splitting of
datasets into training and test datasets, which not only leads to over-estimated performance …

Deep transfer learning (DTL) has received increasing attention in smart manufacturing,
whereas most current studies focus on the situation of marginal distribution shift in …

In this paper, we study the problem of domain adaptation regression, which learns a
regressor for a target domain by leveraging the knowledge from a relevant source domain …

Abstract Domain adaptation (DA) methods facilitate cross-domain learning by minimizing the
marginal or conditional distribution shift between domains. However, the conditional …

Importance weighting is a fundamental procedure in statistics and machine learning that
weights the objective function or probability distribution based on the importance of the …

Least squares estimators, when trained on few target domain samples, may predict poorly.
Supervised domain adaptation aims to improve the predictive accuracy by exploiting …

The graph neural network (GNN) has shown outstanding performance in processing
unstructured data. However, the downstream task performance of GNN strongly depends on …

Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud
platforms. There are massive amounts of short-workload sequences in the cloud platform …