Thumb ticker md paul kishan rubenstein  phd student

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 (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