# Hyperparameter Learning via Distributional Transfer

arXiv: https://arxiv.org/abs/1810.06305 · HTML: https://mlsanity.com/paper/1810.06305/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023)
- [Transfer Learning for Bayesian Optimization: A Survey](https://mlsanity.com/paper/2302.05927/) (2023)
- [Deconditional Downscaling with Gaussian Processes](https://mlsanity.com/paper/2105.12909/) (2021)
- [Meta-Learning Reliable Priors in the Function Space](https://mlsanity.com/paper/2106.03195/) (2021)
- [Hyperparameter Transfer Learning with Adaptive Complexity](https://mlsanity.com/paper/2102.12810/) (2021)
- [Monte Carlo Tree Search based Space Transfer for Black-box Optimization](https://mlsanity.com/paper/2412.07186/) (2024)
- [An Overview of Causal Inference using Kernel Embeddings](https://mlsanity.com/paper/2410.22754/) (2024)
- [FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation](https://mlsanity.com/paper/2307.10052/) (2023)
- [Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendation](https://mlsanity.com/paper/2106.09179/) (2021)
- [Hyperparameter Optimization with Differentiable Metafeatures](https://mlsanity.com/paper/2102.03776/) (2021)
- [None To Optima in Few Shots: Bayesian Optimization with MDP Priors](https://mlsanity.com/paper/2511.01006/) (2025)
- [Provable Accelerated Bayesian Optimization with Knowledge Transfer](https://mlsanity.com/paper/2511.03125/) (2025)
- [AODisaggregation: toward global aerosol vertical profiles](https://mlsanity.com/paper/2205.04296/) (2022)

## Abstract (arXiv)

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. This results in a joint Gaussian process model on hyperparameters and data representations. Representations make use of the framework of distribution embeddings into reproducing kernel Hilbert spaces. The developed method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective.