Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization

12/18/2020
by   Mikita Sazanovich, et al.
0

This paper describes our approach to solving the black-box optimization challenge through learning search space partition for local Bayesian optimization. We develop an algorithm for low budget optimization. We further optimize the hyper-parameters of our algorithm using Bayesian optimization. Our approach has been ranked 3rd in the competition.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/18/2019

A Simple Heuristic for Bayesian Optimization with A Low Budget

The aim of black-box optimization is to optimize an objective function w...
research
09/27/2019

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning

Bayesian optimization (BO) is a successful methodology to optimize black...
research
07/01/2020

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

High dimensional black-box optimization has broad applications but remai...
research
10/29/2020

Black-Box Optimization of Object Detector Scales

Object detectors have improved considerably in the last years by using a...
research
10/03/2022

New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization

Bayesian optimization (BO) is one of the most effective methods for clos...
research
07/03/2018

Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization

Bayesian optimization offers the possibility of optimizing black-box ope...
research
10/22/2022

Bayesian Optimization with Conformal Coverage Guarantees

Bayesian optimization is a coherent, ubiquitous approach to decision-mak...

Please sign up or login with your details

Forgot password? Click here to reset