From kernels to causal inference
published: Jan. 25, 2012, recorded: December 2011, views: 887
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Kernel methods in machine learning have expanded from tricks to construct nonlinear algorithms to general tools to assay higher order statistics and properties of distributions. They find applications also in causal inference, an intriguing field that examines causal structures by testing their probabilistic footprints. However, the links between causal inference and modern machine learning go beyond this and the talk will outline some initial thoughts how problems like covariate shift adaptation and semi-supervised learning can benefit from the causal methodology.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !