# PyHopper -- Hyperparameter optimization

arXiv: https://arxiv.org/abs/2210.04728 · HTML: https://mlsanity.com/paper/2210.04728/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Learning Long-Term Dependencies in Irregularly-Sampled Time Series](https://mlsanity.com/paper/2006.04418/) (2020)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://mlsanity.com/paper/1903.06694/) (2019)
- [Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning](https://mlsanity.com/paper/1804.07824/) (2018)

## Built on by (most cited first, 5 of 5)

- [Dataset Distillation with Convexified Implicit Gradients](https://mlsanity.com/paper/2302.06755/) (2023)
- [HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets](https://mlsanity.com/paper/2304.03543/) (2023)
- [LiBOG: Lifelong Learning for Black-Box Optimizer Generation](https://mlsanity.com/paper/2505.13025/) (2025)
- [Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing](https://mlsanity.com/paper/2510.01658/) (2025)
- [Enhancing Robot Learning through Learned Human-Attention Feature Maps](https://mlsanity.com/paper/2308.15327/) (2023)

## Abstract (arXiv)

Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amount of time. Here, we present PyHopper, a black-box optimization platform designed to streamline the hyperparameter tuning workflow of machine learning researchers. PyHopper's goal is to integrate with existing code with minimal effort and run the optimization process with minimal necessary manual oversight. With simplicity as the primary theme, PyHopper is powered by a single robust Markov-chain Monte-Carlo optimization algorithm that scales to millions of dimensions. Compared to existing tuning packages, focusing on a single algorithm frees the user from having to decide between several algorithms and makes PyHopper easily customizable. PyHopper is publicly available under the Apache-2.0 license at https://github.com/PyHopper/PyHopper.