Statistics Seminar Speaker: Garvesh Raskutti, 11/18/2015

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Wednesday Nov 18 2015

Statistics Seminar Speaker: Garvesh Raskutti, 11/18/2015

4:15pm @ G01 Biotechnology

The Statistics Seminar Speaker for November 18 is Garvesh Raskutti, assistant professor of Statistics and Optimization at the Wisconsin Institute for Discovery. His research interests include Optimization Theory, Information theory and Theoretical statistics to study computational and statistical aspects of large-scale and inference problems. 

TItle: Recent Developments in Learning Large-scale DAG Models

Abstract: Learning causal or directional relationships for large-scale multivariate data is an extremely challenging problem. The directed acyclic graphical (DAG) model methodology provides one framework for addressing this problem. However there are a number of statistical and computational challenges associated with learning DAG models from observational data. In this talk, I present three of my recent pieces of work that investigates and addresses these challenges. Firstly, I discuss one of the fundamental assumptions in many algorithms for learning DAG models, the so-called faithfulness assumption. I show that this faithfulness assumption is extremely restrictive and unlikely to be satisfied in most practical scenarios. Secondly, I present a strictly weaker assumption than the faithfulness assumption that is guaranteed to recover the DAG model by considering a new approach based on searching over causal orderings. Finally, I present a computationally tractable algorithm for learning count-data DAG models based on a computationally feasible approach to learning the causal orderings.

This is based on joint work with Caroline Uhler (MIT EECS) and Gunwoong Park (UW Madison) 

Refreshments will be served after the seminar in 1181 Comstock Hall. 

PDF iconGarvesh_Raskutti[1].pdf