Speaker: Antonio Linero, Assistant Professor, Dept of Statistics and Data Sciences, University of Texas, Austin
Title: "Bayesian Decision Tree Ensembling Strategies for Nonparametric Problems"
Abstract: In this talk we will make the case for using Bayesian decision tree ensembles, such as Bayesian additive regression trees (BART), for addressing some fully-nonparametric problems. We present models for density regression and survival analysis, and argue that our approaches are both easier to use and more effective than more standard Bayesian nonparametric solutions (such as those based on mixture models). On the applied side, we show how to use our models to extract interesting features across several datasets. On the theoretical side, we also show that our models attain minimax-optimal rates of convergence of the posterior in high-dimensional settings. Throughout the talk, we will emphasize the flexibility and ease-of-use of our approach: all simulation and real data analyses attain excellent results using heuristically chosen "default" priors, and it is quite straight-forward for researchers (both in-principle and in-practice) to embed our ensembles in larger models.
About the speaker: Antonio Linero is an assistant professor in the department of statistics and data sciences at the University of Texas, Austin. His research areas are focused on developing flexible Bayesian non-parametrics methods for missing and causal inference problems.
Seminar Date/Time: Thursday April 8, 4:10pm
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact the instructor Professor Jairo Fùquene Patiño or Pete Scully (firstname.lastname@example.org) for the meeting ID and password, stating your affiliation.