Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

We study the problem of transfer learning via Multi-Task Representation Learning (MTRL),
wherein multiple source tasks are used to learn a good common representation, and a …

Existing generalization analysis of clustering mainly focuses on specific instantiations, such
as (kernel) $ k $-means, and a unified framework for studying clustering performance is still …

In this paper, we study the generalization performance of multi-class classification and
obtain a shaper data-dependent generalization error bound with fast convergence rate …

Multi-task learning leverages structural similarities between multiple tasks to learn despite
very few samples. Motivated by the recent success of neural networks applied to data-scarce …

In federated learning (FL), the not independently or identically distributed (non-IID) data
partitioning impairs the performance of the global model, which is a severe problem to be …

We consider a weakly supervised learning problem called Learning from Label Proportions
(LLP), where examples are grouped into``bags''and only the average label within each bag …

The Area Under the ROC curve (AUC) is a crucial metric for machine learning, which is often
a reasonable choice for applications like disease prediction and fraud detection where the …

This paper provides some first steps in developing empirical process theory for functions
taking values in a vector space. Our main results provide bounds on the entropy of classes …

Data integration is the process of extracting information from multiple sources and jointly
analyzing different data sets. In this paper, we propose to use the mixed ℓ 2, 1 regularized …