[Statlist] ETH/UZH Research Seminar on Statistics by Xinwei Shen, ETH Zurich, 30.11.2023

[Maurer Letizia]

We are glad to announce the following talk in the ETH/UZH Research Seminar on Statistics: 

"Engression: Extrapolation for Nonlinear Regression?"   

by Xinwei Shen, ETH Zurich

Time: Thursday, 30.11.2023 at 15.15 h
Place: ETH Zurich, HG G 43

Abstract: Extrapolation is crucial in many statistical and machine learning applications, as it is common to encounter test data outside the training support. However, extrapolation is a considerable challenge for nonlinear models. Conventional models typically struggle in this regard: while tree ensembles provide a constant prediction beyond the support, neural network predictions tend to become uncontrollable. This work aims at providing a nonlinear regression methodology whose reliability does not break down immediately at the boundary of the training support. Our primary contribution is a new method called ‘engression’ which, at its core, is a distributional regression technique for pre-​additive noise models, where the noise is added to the covariates before applying a nonlinear transformation. Our experimental results indicate that this model is typically suitable for many real data sets. We show that engression can successfully perform extrapolation under some assumptions such as a strictly monotone function class, whereas traditional regression approaches such as least-​squares regression and quantile regression fall short under the same assumptions. We establish the advantages of engression over existing approaches in terms of extrapolation, showing that engression consistently provides a meaningful improvement. Our empirical results, from both simulated and real data, validate these findings, highlighting the effectiveness of the engression method. The software implementations of engression are available in both R and Python.

Seminar website: https://math.ethz.ch/sfs/news-and-events/research-seminar.html

Research Seminar – Seminar for Statistics | ETH Zurich
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