Student Seminar Series
DATE: Wednesday April 12th, 2017, 11:10am
LOCATION: MSB 1147 (Colloquium Room).
SPEAKERS: Miles Lopes, Assistant Professor, Statistics, UC Davis
TITLE: “On the computation-accuracy tradeoff for majority-voting ensembles”
ABSTRACT: When the methods of bagging or random forests are used for classification, an ensemble of t=1,2,... randomized classifiers is generated, and the predictions of the classifiers are aggregated by voting. Due to the randomization in these methods, there is a natural tradeoff between statistical performance and computational cost. On one hand, as t increases, the (random) prediction error of the ensemble tends to decrease and stabilize. On the other hand, larger ensembles require greater computational cost for training and making new predictions. In this talk, I will discuss some recent methods and theoretical results that quantify this tradeoff in a precise sense.
This seminar series is organized by PhD Students Irene Kim and Ozan Sonmez.