Theory of latent feature learning
Paul Kishan Rubenstein (Ph.D. Student)
Low dimensional, abstract or otherwise 'simple' structure occurs widely across machine learning. This project advances theoretical understanding in a variety of areas. These are: causality, in which a theory of micro-macro abstractions is developed; independent component analysis, in which new identifiability results are derived in a multi-view setting; and generative modelling, where the estimation of divergences in latent spaces is analysed in a learning theoretic framework.
|Primary Host:||Bernhard Schölkopf (ELLIS Institute Tübingen & Max Planck Institute for Intelligent Systems)|
|Exchange Host:||Carl Edward Rasmussen (University of Cambridge)|
|PhD Duration:||01 October 2015 - 30 June 2020|
|Exchange Duration:||01 October 2015 - 30 September 2016 - Ongoing|