# Hyperparameter Transfer Across Developer Adjustments

arXiv: https://arxiv.org/abs/2010.13117 · HTML: https://mlsanity.com/paper/2010.13117/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer](https://mlsanity.com/paper/2203.03466/) (2022)
- [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://mlsanity.com/paper/2204.11051/) (2022)
- [PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning](https://mlsanity.com/paper/2306.12370/) (2023)
- [Super Consistency of Neural Network Landscapes and Learning Rate Transfer](https://mlsanity.com/paper/2402.17457/) (2024)
- [nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales](https://mlsanity.com/paper/2304.06875/) (2023)
- [A resource-efficient method for repeated HPO and NAS problems](https://mlsanity.com/paper/2103.16111/) (2021)
- [Principled Architecture-aware Scaling of Hyperparameters](https://mlsanity.com/paper/2402.17440/) (2024)
- [Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach](https://mlsanity.com/paper/2112.08250/) (2021)
- [Adaptive Hyperparameter Optimization for Continual Learning Scenarios](https://mlsanity.com/paper/2403.07015/) (2024)
- [Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation](https://mlsanity.com/paper/2306.16916/) (2023)
- [Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces](https://mlsanity.com/paper/2409.05325/) (2024)

## Abstract (arXiv)

After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can change which hyperparameter settings perform well, or even the hyperparameter search space itself. While many approaches exist that leverage knowledge obtained on previous tasks, so far, knowledge from previous development steps remains entirely untapped. In this work, we remedy this situation and propose a new research framework: hyperparameter transfer across adjustments (HT-AA). To lay a solid foundation for this research framework, we provide four simple HT-AA baseline algorithms and eight benchmarks changing various aspects of ML algorithms, their hyperparameter search spaces, and the neural architectures used. The best baseline, on average and depending on the budgets for the old and new HPO, reaches a given performance 1.2--2.6x faster than a prominent HPO algorithm without transfer. As HPO is a crucial step in ML development but requires extensive computational resources, this speedup would lead to faster development cycles, lower costs, and reduced environmental impacts. To make these benefits available to ML developers off-the-shelf and to facilitate future research on HT-AA, we provide python packages for our baselines and benchmarks.