Pradeep Ravikumar, PhD
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
Non-parametric multivariate density estimation faces strong statistical and computational bottlenecks, and the more practical approaches impose near-parametric assumptions on the form of the density functions. In this paper, we leverage recent developments in parametric graphical models to propose a class of non-parametric multivariate densities which have very attractive computational and statistical properties. Our approach relies on the simple function space assumption that the conditional distribution of each variable conditioned on the other variables has a non-parametric exponential family form. Joint work with Mladen Kolar, Arun Sai Suggala.
Pradeep Ravikumar is an Associate Professor in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. He received his B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Bombay, and his PhD in Machine Learning from the School of Computer Science at Carnegie Mellon University. He was previously an Associate Professor in the Department of Computer Science, and Associate Director at the Center for Big Data Analytics, at the University of Texas at Austin. His thesis has received honorable mentions in the ACM SIGKDD Dissertation award and the CMU School of Computer Science Distinguished Dissertation award. He is a Sloan Fellow, a Siebel Scholar, a recipient of the NSF CAREER Award, and was Program Chair for the International Conference on Artificial Intelligence and Statistics (AISTATS) in 2013.
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