Theory for BART

Event Sponsor: 
Mathmatics and Computer Science Division Seminar - LANS
Start Date: 
Oct 24 2018 - 10:30am
Building 240/Room 4301
Argonne National Laboratory
Veronika Rockova
Speaker(s) Title: 
University of Chicago Booth School of Business

The remarkable empirical success of Bayesian additive regression trees (BART) has raised considerable interest in understanding why and when this method produces good results. Since its inception nearly 20 years ago, BART has become widely used in practice and yet, theoretical justifications have been unavailable. To narrow this yawning gap, we study estimation properties of Bayesian trees and tree ensembles in nonparametric regression (such as the speed of posterior concentration, reluctance to overfit, variable selection and adaptation in high-dimensional settings). Our approach rests upon a careful analysis of recursive partitioning schemes and associated sieves of approximating step functions. We develop several useful tools for analyzing additive regression trees, showing their optimal performance in both additive and non-additive regression. Our results constitute a missing piece of the broader theoretical puzzle as to why Bayesian machine learning methods like BART hav e been so successful in practice.

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Upcoming Seminars
October 10, 2018, "Estimation and Inference for Differential Networks" Mladen Kolar, Associate Professor of Econometrics and Statistics, University of Chicago Booth School of Business
October 17, 2018, "From Paraphrase Modeling to Controlled Generation" Kevin Gimpel, Assistant Professor, Toyota Technological Institute at Chicago
October 24, 2018, "Theory for BART" Veronika Rockova, Assistant Professor of Econometrics and Statistics, University of Chicago Booth School of Business
October 31, 2018, "TBA" Prasanna Balaprakash, Computer Scientist, MCS & LCF, ANL
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