[IPOL announce] new article: An Active Learning Method Based on Bagging and Applied to Surrogate Modelling

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2026/514/


Nomena Andrianarisoa, Matthieu Ancellin, and Vincent Laurent,
An Active Learning Method Based on Bagging and Applied to Surrogate 
Modelling,
Image Processing On Line, 16 (2026), pp. 190–198.
https://doi.org/10.5201/ipol.2026.514

Abstract
In this work, we study active learning techniques to highlight their 
strengths and limitations in the context of surrogate modeling. We 
implemented an iterative algorithm that uses active sampling methods to 
approximate known functions. Through a series of experiments, we 
demonstrate the effectiveness of active learning in improving surrogate 
models by wisely selecting informative data points. Furthermore, we 
examine cases where active learning may face challenges, particularly 
when balancing exploration and exploitation. This study provides 
information on the improvement of the efficiency and accuracy of 
surrogate models in practical applications.