# Hierarchical Collaborative Hyper-parameter Tuning

arXiv: https://arxiv.org/abs/2205.05272 · HTML: https://mlsanity.com/paper/2205.05272/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 4 of 4)

- [A Literature Survey of Benchmark Functions For Global Optimization Problems](https://mlsanity.com/paper/1308.4008/) (2013)
- [Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits](https://mlsanity.com/paper/2002.02518/) (2020)
- [Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning](https://mlsanity.com/paper/1804.07824/) (2018)
- [HAMLET: A Hierarchical Agent-based Machine Learning Platform](https://mlsanity.com/paper/2010.04894/) (2020)

## Built on by (most cited first, 2 of 2)

- [Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization](https://mlsanity.com/paper/2303.03394/) (2023)
- [Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach](https://mlsanity.com/paper/2309.06604/) (2023)

## Abstract (arXiv)

Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model. The proposed method employs a distributedly formed hierarchical agent-based architecture for the cooperative searching procedure of tuning hyper-parameter values. The presented generic model is used to develop a guided randomized agent-based tuning technique, and its behavior is investigated in both machine learning and global function optimization applications. According the empirical results, the proposed model outperformed both of its underlying randomized tuning strategies in terms of classification error and function evaluations, notably in higher number of dimensions.