# A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer

arXiv: https://arxiv.org/abs/2202.07735 · HTML: https://mlsanity.com/paper/2202.07735/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Evaluation System for a Bayesian Optimization Service](https://mlsanity.com/paper/1605.06170/) (2016)

## Built on by (most cited first, 1 of 1)

- [Compact Model Parameter Extraction via Derivative-Free Optimization](https://mlsanity.com/paper/2406.16355/) (2024)

## Abstract (arXiv)

We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multiple performance objectives using a target-priority-limit scalarizer. It also supports a trade-off mode, where the goal is to find an appropriate trade-off among objectives by interacting with the user. We focus on the common scenario where there are on the order of tens of hyper-parameters, each with various attributes such as a range of continuous values, or a finite list of values, and whether it should be treated on a linear or logarithmic scale. The system supports multiple asynchronous simulations and is robust to simulation stragglers and failures.