PyHopper -- Hyperparameter optimization
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 10 Oct 2022.
Builds on
6 of 15 references.
References · 6
- 2017Attention Is All You Needcited by 59,932
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
- 2020Learning Long-Term Dependencies in Irregularly-Sampled Time Seriescited by 82
- 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 66
- 2018Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuningcited by 20
Built on by
Citing papers per year: 2023: 3; 2024: 0; 2025: 2.
5 papers.
- 2023Dataset Distillation with Convexified Implicit Gradientscited by 46
- 2023HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasetscited by 10
- 2025LiBOG: Lifelong Learning for Black-Box Optimizer Generationcited by 3
- 2025Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancingcited by 1
- 2023Enhancing Robot Learning through Learned Human-Attention Feature Mapscited by 0
- 2025Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing2 Oct 2025
- 2025LiBOG: Lifelong Learning for Black-Box Optimizer Generation19 May 2025
- 2023Enhancing Robot Learning through Learned Human-Attention Feature Maps29 Aug 2023
- 2023HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets7 Apr 2023
- 2023Dataset Distillation with Convexified Implicit Gradients13 Feb 2023