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.
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