Center for Computational and Statistical Learning Group - overview

The Center for Computational and Statistical Learning aims to advance the frontiers of learning theory and machine learning.


The centre covers all aspects of machine learning from learning theory, development of novel learning algorithms to applications of learning. Learning often plays a key role in other research areas such as automated reasoning, computational biology, perception, etc.


The goals of learning are understanding and prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood. Supervised learning involves learning from a training set of data. Every point in the training is an input-output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict output from future input.

Depending on the type of output, supervised learning problems are either problems of regression or problems of classification. If the output takes a continuous range of values, it is a regression problem.

Center for Computational and Statistical Learning

Naoki Abe,Yasuo Am emiya, Upendra Chitnis, Shuyu Chu, Andrew Conn, Stefa Etchegaray Garcia, Caio Giuliani, Chen Jiang, Huijing Jiang, Alan King, Georgios Kollias, Anastasios Kyrillidis, Kimberly Lang, Ta-Hsin Li, Aurelie Lozano, Jiri Navratil, Rodrigue Ngueyep Tzoumpe, Julie Novak, Min-Hwan Oh, Peder Olsen, Srikanth Peddi, Hui Qi, Ruben Rodriguez,Karthikeyan Shanmugam, Stuart Siegel, Lily Weng, Brian White and Emmanuel Yashchin