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 …

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 …

Performance diagnosis systems are defined as detecting abnormal performance
phenomena and play a crucial role in cloud applications. An effective performance …

Governments of the world have invested a lot of manpower and material resources to
combat COVID-19 this year. At this moment, the most efficient way that could stop the …

In this paper, we study the problem of interactive cleaning for progressive visualization
(ICPV): Given a bad visualization V, it is to obtain a" cleaned" visualization V whose distance …

Data exploration—the problem of extracting knowledge from database even if we do not
know exactly what we are looking for—is important for data discovery and analysis …

Deep learning (DL) has widespread applications and has revolutionized many industries.
Although automated machine learning (AutoML) can help us away from coding for DL …

The resourcefulness of crowdsourcing can be used to handle a wide range of complex
macro-tasks, such as travel planning, translation, and software development. Multi-phase …

Electric vehicle (EV) charging infrastructure is present all over the United States, but
charging prices vary greatly, both in amount and in the methods by which they are assessed …