XBBO/examples/BORE

• BORE
• • Introduction
  • Abstract
  • Usage
  • benchmark
  • Results
  • • Branin

BORE

Introduction

BORE: Bayesian Optimization by Density-Ratio Estimation [ICML2021]

Official Repo

Abstract

Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are computed from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI) function. The need to ensure analytical tractability of the predictive often poses limitations that can hinder the efficiency and applicability of BO. In this paper, we cast the computation of EI as a binary classification problem, building on the link between class-probability estimation and density-ratio estimation, and the lesser-known link between density-ratios and EI. By circumventing the tractability constraints, this reformulation provides numerous advantages, not least in terms of expressiveness, versatility, and scalability.

Usage

E.g. PYTHONPATH='./' python examples/BORE/rosenbrock_bore.py

benchmark

Modify the following section of comparison/xbbo_benchmark.py :

test_algs = ["bore"]

And run PYTHONPATH='./' python comparison/xbbo_benchmark.py in the command line.

Results

Branin

Method	Minimum	Best minimum	Mean f_calls to min	Std f_calls to min	Fastest f_calls to min
XBBO(bore)	0.412+/-0.016	0.399	93.9	51.829	38