Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly
from data. This approach has achieved impressive results and has contributed significantly …

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 …

Modern artificial intelligence (AI) applications require large quantities of training and test
data. This need creates critical challenges not only concerning the availability of such data …

We identify label errors in the test sets of 10 of the most commonly-used computer vision,
natural language, and audio datasets, and subsequently study the potential for these label …

Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

Anomaly detection is often used to identify and remove outliers in datasets. However,
detecting and analyzing the pattern of outliers can contribute to future business decisions or …

Designing effective data manipulation methods is a long standing problem in data lakes.
Traditional methods, which rely on rules or machine learning models, require extensive …

Machine learning (ML) is playing an increasingly important role in data management tasks,
particularly in Data Integration and Preparation (DI&P). The success of ML-based …

Data-centric artificial intelligence (AI) is an emerging paradigm that emphasizes the critical
role of data in real-world machine learning (ML) systems—as a complement to model …

Abstract Machine learning (ML) methods provide a new opportunity to build quantitative
structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on …