Parallel Architecture And Hyperparameter Search Via Successive Halving And Classification

# Parallel Architecture and Hyperparameter Search via Successive Halving and Classification

**arXiv ID:** 1805.10255
**Authors:** Manoj Kumar, George E. Dahl, Vijay Vasudevan, Mohammad Norouzi
**Published:** 2018-05-25T17:12:38Z
**Abstract:**
We present a simple and powerful algorithm for parallel black box optimization called Successive Halving and Classification (SHAC). The algorithm operates in $K$ stages of parallel function evaluations and trains a cascade of binary classifiers to iteratively cull the undesirable regions of the search space. SHAC is easy to implement, requires no tuning of its own configuration parameters, is invariant to the scale of the objective function and can be built using any choice of binary classifier. We adopt tree-based classifiers within SHAC and achieve competitive performance against several strong baselines for optimizing synthetic functions, hyperparameters and architectures.

## Skill Description

This skill is generated from the arXiv paper: Parallel Architecture and Hyperparameter Search via Successive Halving and Classification (1805.10255).

## How to Use

[To be filled in by the user or by future automation]

## References

- [arXiv:1805.10255](http://arxiv.org/abs/1805.10255v1)