This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"

Prior-RObust Bayesian Optimization (PROBO)

Introduction, TOC

This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization" (Julian Rodemann, Thomas Augustin). More precisely,

• PROBO contains implementation of PROBO
• benchmarking provides files for experiments (section 4), in order to reproduce results, see setup below
• files in data allow recreating visualizations of data and functions used in the benchmark experiments, see below
• files in univariate-benchmark-functions allow visualization of synthetic test functions mentioned in section 4

Tested with

• R 4.1.6
• R 4.0.3

on

• Linux Ubuntu 20.04
• Linux Debian 10
• Windows 10 Build 20H2
• MacOS (only visualizations)

Setup

First and foremost, please clone this repo (and install required packages as indicated by your IDE)

In order to reproduce figure 2 showing the papers' key results (and visualizations of further results not included but only mentioned in the paper on page 10)

• source this file

Please find optional (currently commented out) visualizations in lines 118-159 of this very file. In order to rerun all simulations described in section 4 (PROBO on graphene data), please

• source this file to kick off the simulation study (estimated time on 64-bit-core (linux gnu): 11h)
• results are saved automatically
• source this file to visualize the retrieved results

Data

Find files to read in data and create target functions in folder data. E.g. source data/make-kapton-rf.R to read in graphene data (source is here) and reproduce figure 1 of the paper