John Langford3:00pm | Remote Access Only - see link below
There are three core orthogonal problems in reinforcement learning: (1) Crediting actions (2) generalizing across rich observations (3) Exploring to discover the information necessary for learning...
Li Deng12:30pm | Remote Access Only - see link below
Jason Eisner3:00pm | Remote Access Only - see link below
Piotr Indyk12:30pm | Remote Access Only - see link below
Inderjit Dhillon3:00pm | Remote Access Only - see link below
Rod Little9:15am | Virtual
Abstract: There is a very extensive literature of statistical methods for the analysis of data with missing values. I'll provide a historical overview, including ad-hoc approaches, statistical...
Julie Josse10:40am | Virtual
Abstract: An abundant literature addresses missing data in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings...
Barbara Englehardt11:05am | Virtual
Kosuke Imai11:55am | Virtual
Abstract: In today’s data-driven society, human beings still make most critical decisions and yet they are increasingly utilizing recommendations produced by statistical and machine...
David Dunson12:00pm | Virtual
Abstract: In many applications, it is of interest to cluster subjects based on very high-dimensional data, often in the presence of missing data. Although discrete mixture models are...