Rxivist logo

A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions

By Eric Schulz, Maarten Speekenbrink, Andreas Krause

Posted 19 Dec 2016
bioRxiv DOI: 10.1101/095190

This tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial aims to provide an accessible introduction to these techniques. We will introduce Gaussian processes which generate distributions over functions used for Bayesian non-parametric regression, and demonstrate their use in applications and didactic examples including simple regression problems, a demonstration of kernel-encoded prior assumptions and compositions, a pure exploration scenario within an optimal design framework, and a bandit-like exploration-exploitation scenario where the goal is to recommend movies. Beyond that, we describe a situation modelling risk-averse exploration in which an additional constraint (not to sample below a certain threshold) needs to be accounted for. Lastly, we summarize recent psychological experiments utilizing Gaussian processes. Software and literature pointers are also provided.

Download data

  • Downloaded 26,316 times
  • Download rankings, all-time:
    • Site-wide: 45 out of 84,043
    • In animal behavior and cognition: 1 out of 1,325
  • Year to date:
    • Site-wide: 183 out of 84,043
  • Since beginning of last month:
    • Site-wide: 267 out of 84,043

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News