# Graduated Optimization of Black-Box Functions

## TL;DR

This paper introduces a graduated optimization method for adaptively tuning hyperparameters in machine learning by estimating gradients, demonstrating improved performance on both low and high dimensional problems.

## Contribution

The paper proposes a novel graduated optimization approach for black-box functions, specifically targeting hyperparameter tuning in machine learning.

## Key findings

- Effective in high-dimensional hyperparameter tuning
- Outperforms existing methods in empirical tests
- Applicable to both low and high dimensional problems

## Abstract

Motivated by the problem of tuning hyperparameters in machine learning, we present a new approach for gradually and adaptively optimizing an unknown function using estimated gradients. We validate the empirical performance of the proposed idea on both low and high dimensional problems. The experimental results demonstrate the advantages of our approach for tuning high dimensional hyperparameters in machine learning.