One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

Entity resolution (ER) is a significant task in data integration, which aims to detect all entity
profiles that correspond to the same real-world entity. Due to its inherently quadratic …

Many data management and analytics tasks, such as entity resolution, cannot be solely
addressed by automated processes. Crowdsourcing is an effective way to harness the …

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

The lack of sufficient labeled data is a key bottleneck for practitioners in many real-world
supervised machine learning (ML) tasks. In this paper, we study a new problem, namely …

Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA)
results on ER are achieved by deep learning (DL) based methods, trained with a lot of …

Outlier detection is critical to a large number of applications from finance fraud detection to
health care. Although numerous approaches have been proposed to automatically detect …

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 …

One of the most important tasks for improving data quality and the reliability of data analytics
results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the …

Human-in-the-loop techniques are playing more and more significant roles in the machine
learning pipeline, which consists of data preprocessing, data labeling, model training and …