# Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior

arXiv: https://arxiv.org/abs/2410.05660 · HTML: https://mlsanity.com/paper/2410.05660/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences](https://mlsanity.com/paper/1807.02582/) (2018)
- [Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning](https://mlsanity.com/paper/1909.12552/) (2019)
- [Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation](https://mlsanity.com/paper/1610.07379/) (2016)
- [Warm Starting Bayesian Optimization](https://mlsanity.com/paper/1608.03585/) (2016)
- [Nearly Optimal Algorithms for Level Set Estimation](https://mlsanity.com/paper/2111.01768/) (2021)
- [Robust Super-Level Set Estimation using Gaussian Processes](https://mlsanity.com/paper/1811.09977/) (2018)
- [An Information-Theoretic Framework for Unifying Active Learning Problems](https://mlsanity.com/paper/2012.10695/) (2020)
- [Multiscale Gaussian Process Level Set Estimation](https://mlsanity.com/paper/1902.09682/) (2019)
- [Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation](https://mlsanity.com/paper/2304.01404/) (2023)
- [Active Level Set Estimation for Continuous Search Space with Theoretical Guarantee](https://mlsanity.com/paper/2402.16237/) (2024)

## Built on by (most cited first, 1 of 1)

- [High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions](https://mlsanity.com/paper/2602.12391/) (2026)

## Abstract (arXiv)

The objective of active level set estimation for a black-box function is to precisely identify regions where the function values exceed or fall below a specified threshold by iteratively performing function evaluations to gather more information about the function. This becomes particularly important when function evaluations are costly, drastically limiting our ability to acquire large datasets. A promising way to sample-efficiently model the black-box function is by incorporating prior knowledge from a related function. However, this approach risks slowing down the estimation task if the prior knowledge is irrelevant or misleading. In this paper, we present a novel transfer learning method for active level set estimation that safely integrates a given prior knowledge while constantly adjusting it to guarantee a robust performance of a level set estimation algorithm even when the prior knowledge is irrelevant. We theoretically analyze this algorithm to show that it has a better level set convergence compared to standard transfer learning approaches that do not make any adjustment to the prior. Additionally, extensive experiments across multiple datasets confirm the effectiveness of our method when applied to various different level set estimation algorithms as well as different transfer learning scenarios.