Fetching the paper…

Reading the bibliography…

2020

Robust Gaussian Process Regression with a Bias Model

Park, Chiwoo, Borth, David J., Wilson, Nicholas S. et al.

Original
PDF
Post on X

Understand

This paper presents a new approach to a robust Gaussian process (GP) regression.

Most existing approaches replace an outlier-prone Gaussian likelihood with a non-Gaussian likelihood induced from a heavy tail distribution, such as the Laplace distribution and Student-t distribution.
However, the use of a non-Gaussian likelihood would incur the need for a computationally expensive Bayesian approximate computation in the posterior inferences.
The proposed approach models an outlier as a noisy and biased observation of an unknown regression function, and accordingly, the likelihood contains bias terms to explain the degree of deviations from the regression function.

Robust Gaussian Process Regression with a Bias Model · Around

Built on

Multivariate adaptive regression splines
Jerome H. Friedman · 1991
Earlier work this paper cites.
Regression with input-dependent noise: A Gaussian process treatment
Paul W Goldberg, Christopher KI Williams, and Christopher M Bishop · 1998
Earlier work this paper cites.
Expectation propagation for approximate Bayesian inference
Thomas P Minka · 2001
Earlier work this paper cites.
Gaussian process models for robust regression, classification, and reinforcement learning
Malte Kuss · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning
C.E. Rasmussen and C.K.I. Williams · 2006
Earlier work this paper cites.

Similar

Robust regression with twinned Gaussian processes
Andrew Naish-Guzman and Sean Holden · 2008
Cited alongside, same era.
Robust Statistics
Peter J Huber · 2011
Cited alongside, same era.
Robust Gaussian process regression with a Student-t likelihood
Pasi Jylänki, Jarno Vanhatalo, and Aki Vehtari · 2011
Cited alongside, same era.
Bayesian Data Analysis
Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin · 2013
Cited alongside, same era.

Then

Gpstuff: Bayesian modeling with Gaussian processes
Original
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari · 2013
Later among the works it cites.
Student-t processes as alternatives to Gaussian processes
Amar Shah, Andrew Wilson, and Zoubin Ghahramani · 2014
Later among the works it cites.
Robust Gaussian process modeling using em algorithm
Rishik Ranjan, Biao Huang, and Alireza Fatehi · 2016
Later among the works it cites.
Identification of robust Gaussian process regression with noisy input using EM algorithm
Atefeh Daemi, Yousef Alipouri, and Biao Huang · 2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…