[gov3009-l] Applied Statistics 3/8/2017 - Paramveer Dhillon

Hi all, This week at the Applied Statistics workshop we will be welcoming Paramveer Dhillon, a Postdoctoral Fellow at the MIT Sloan School of Management and the Initiative on Digital Economy at MIT. He will be presenting work entitled "Linear Methods for Big Data." Please find the abstract below and on the website. We will meet in CGIS Knafel Room 354 at noon and lunch will be provided. Best, Pam Title: Linear Methods for Big Data Abstract: Statistical machine learning has seen great advances in the last decade owing to the availability of large-scale annotated datasets and significant improvements in computation hardware. Amidst this measurement revolution, it has become increasingly important to come up with statistical methods that are not only statistically efficient but that are also computationally efficient i.e. they run fast. Drawing on these developments and recent advances in random matrix theory, I will present my work on building fast and theoretically sound methods for linear regression (OLS) and canonical correlation analysis (CCA). I will also describe how these methods can be used to generate linear features that give a state-of-the-art performance on several natural language processing tasks.