Machine Learning
Our work in machine learning spans theory, algorithms and applications. We have been studying different learning settings, including online learning, learning with partial feedback, learning in games, unsupervised learning, meta-learning, and statistical learning theory. Researchers in the unit have made significant contributions to the foundations of machine learning, leveraging tools from statistics and probability, optimization, and theoretical computer science. Underpinning the theory of machine learning is important in order to understand the success and failure of learning algorithms, and plays a key role in order to make machine learning a practical tool and reliable.