Missing data are an unavoidable complication in many machine learning tasks. When data
are 'missing at random'there exist a range of tools and techniques to deal with the issue …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Recent studies have shown that deep neural net-works (DNNs) are vulnerable to
adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense …

Artificial intelligence (AI) systems are trained to solve complex problems and learn to
perform specific tasks by using large volumes of data, such as prediction, classification …

Developing machine learning models can be seen as a process similar to the one
established for traditional software development. A key difference between the two lies in the …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

Data quality affects machine learning (ML) model performances, and data scientists spend
considerable amount of time on data cleaning before model training. However, to date, there …

Abstract Vertical Federated Learning (VFL), that trains federated models over vertically
partitioned data, has emerged as an important learning paradigm. However, existing VFL …

Given a dataset with incomplete data (eg, missing values), training a machine learning
model over the incomplete data requires two steps. First, it requires a data-effective step that …

Data cleaning is widely regarded as a critical piece of machine learning (ML) applications,
as data errors can corrupt models in ways that cause the application to operate incorrectly …