The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special Topic on Machine Learning and Large Scale Optimization examines this interplay. Machine learning researchers have embraced the advances in mathematical programming allowing new types of models to be pursued. The special...

Data management and machine learning are two important tasks in data science. However, they have been independently studied so far. We argue that they should be complementary to each other. On the one hand, machine learning requires data management techniques to extract, integrate, clean the data, to support scalable and usable machine learning, making it user-friendly and easily deployable. On...

While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learni...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

This paper is about the construction of various specific machine learning paradigms using a primitive-based generic machine learning model. The generic model identifies five functional components involved in a machine learning process, including an input, a transformation, a control, an output, and a knowledge base. It also identifies a set of basic machine learning mechanisms for each componen...