A collaborative framework for tweaking properties in a synthetic dataset

Recent research has made strides toward fair machine learning. Relatively few datasets, however, are commonly examined to evaluate these fairness-aware algorithms, and even fewer in education domains, which can lead to a narrow focus on particular types ...

Unrestricted availability of the datasets is important for the researchers to evaluate their strategies to solve the research problems. While publicly releasing the datasets, it is equally important to protect the privacy of the respective data ...

A controlled environment based on known properties of the dataset used by a learning algorithm is useful to empirically evaluate machine learning algorithms. Synthetic (artificial) datasets are used for this purpose. Although there are publicly ...