In the last few years, machine learning (ML) and artificial intelligence have seen a new wave
of publicity fueled by the huge and ever‐increasing amount of data and computational …

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 …

The increasing size of data generated by smartphones and IoT devices motivated the
development of Federated Learning (FL), a framework for on-device collaborative training of …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
However, most existing methods do not give sufficient consideration to weights of different …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

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 …

Object frequency in the real world often follows a power law, leading to a mismatch between
datasets with long-tailed class distributions seen by a machine learning model and our …

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 …