# Mathematics Colloquium - Yan Wang

This event is in the past.

**Date:**January 11, 2023

**Time:**2:45 p.m. - 3:45 p.m.

**Category:**Lecture

**Speaker:**Yan Wang (Iowa State University)

**Title:**Active Learning for Regression Guided by Out-of-Bag Error Prediction

**Abstract:**Active learning aims to do machine learning in an economical way by actively picking the “most informative” data points in a pool of data. The feature (or covariate) vector of each data point in the pool is known “for free”, but the corresponding response (or label) will only be revealed for picked data points at some cost. Given a budget, how to select the informative data points stands at the core of active learning. We tackle this problem by first analyzing the constituents of the prediction error for a (stable) learning algorithm, and then propose a method to actively sample data points to reduce the prediction error in a bias-correcting way. In particular, by taking advantage of the out-of-bag (OOB) error of random forests, the implementation of our method is computationally efficient. We might also discuss how the OOB error can be helpful in constructing prediction intervals for active learning.

## Contact

Department of Mathematics

3135772479

math@wayne.edu