Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

In this paper, we study the problem of constrained min-max optimization in a black-box
setting, where the desired optimizer cannot access the gradients of the objective function but …

Data science and machine learning (DS/ML) are at the heart of the recent advancements of
many Artificial Intelligence (AI) applications. There is an active research thread in AI,\autoai …

Despite the ubiquity of data science, we are far from rigorously understanding how coding in
data science is performed. Even though the scientific literature has hinted at the iterative and …

This study proposes and evaluates the OCEL. AI (Open Collaborative Experiential Learning.
AI) paradigm that aims at broadening participation in data science education and enhancing …

People commonly utilize visualizations not only to examine a given dataset, but also to draw
generalizable conclusions about the underlying models or phenomena. Prior research has …

A machine learning task can be viewed as a sequential pipeline of different algorithmic
choices, including data preprocessing, model selection, and hyper-parameter tuning …

Feature engineering is a crucial but tedious task that requires up to 80% of the total time in
data science projects. A significant challenge is when data consists of tables from different …

As AI technology continues to advance, the importance of human-AI collaboration becomes
increasingly evident, with numerous studies exploring its potential in various fields. One vital …

From a bird's eye point of view, large-scale data analytics workflows, eg, those executed in
popular tools, such as Apache Spark and Flink, are typically represented by directed acyclic …