Optimization Theory for Machine Learning
At the foundations of machine learning lie a number of interconnected discrete optimization problems, such as optimal point labeling and subset selection. As time passes, the need to address ever more complex variations on these problems arises. We address this need with a study of the discrete optimization foundations of…... Read More
Privacy & Security in Learning Systems
The ever-growing deployment of machine learning models in industrial and health contexts raises critical privacy and security concerns. These models are built on personal data (e.g. clinical records, images, and user profiles). Our work on this subject focuses on extending ideas in differential privacy to deep neural networks to secure…... Read More