# Sherpa: Robust Hyperparameter Optimization for Machine Learning

arXiv: https://arxiv.org/abs/2005.04048 · HTML: https://mlsanity.com/paper/2005.04048/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 14 of 14)

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- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers](https://mlsanity.com/paper/2303.06201/) (2023)
- [Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications](https://mlsanity.com/paper/2408.02161/) (2024)
- [A Review and Comparison of AI Enhanced Side Channel Analysis](https://mlsanity.com/paper/2402.02299/) (2024)
- [Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2102.00280/) (2021)
- [Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission](https://mlsanity.com/paper/2206.02819/) (2022)
- [Quantitative Imaging Principles Improves Medical Image Learning](https://mlsanity.com/paper/2206.06663/) (2022)
- [Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps in Reacting PIV Data](https://mlsanity.com/paper/2312.06461/) (2023)
- [Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks](https://mlsanity.com/paper/2504.17065/) (2025)
- [Sparse Phased Array Optimization Using Deep Learning](https://mlsanity.com/paper/2504.17073/) (2025)

## Abstract (arXiv)

Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of powerful and interchangeable algorithms. Sherpa can be run on either a single machine or in parallel on a cluster. Finally, an interactive dashboard enables users to view the progress of models as they are trained, cancel trials, and explore which hyperparameter combinations are working best. Sherpa empowers machine learning practitioners by automating the more tedious aspects of model tuning. Its source code and documentation are available at https://github.com/sherpa-ai/sherpa.