Meta-Learning for Hyperparameter Optimization
A tutorial summarizing the progresses in Meta-Learning for Hyperparameter Optimization
presented at the AutoML Conference 2023 (AutoMLConf 2023).
Hyperparameter Optimization (HPO) has been recognized as a crucial element in achieving state-of-the-art performance across a wide range of machine learning tasks. However, the complexity and compute-intensive nature of HPO makes it a challenging feat, often inhibiting its adoption and the successful deployment of ML systems to novel tasks. This tutorial proposal delves into meta-learning for HPO. Our focus will be on presenting a comprehensive guide on meta-learning strategies which facilitates the transfer of design choices across tasks, thereby reducing the computational burden. This tutorial aims to equip participants with the knowledge to implement meta-learning strategies for HPO, ultimately enhancing the efficiency and effectiveness of their Machine Learning systems. Ideal attendees would be machine learning practitioners, researchers, and enthusiasts interested in leveraging meta-learning for hyperparameter optimization.
Recording
Tutorial Material
Presenters
Martin Wistuba
Martin Wistuba is a researcher at Amazon Web Services where he works on automation of hyperparameter optimization and Neural Architecture Search. Earlier, he was at IBM Research, where he developed tools to automate deep learning. He received his Ph.D. in Machine Learning from the University of Hildesheim. His research interest includes AutoML, in particular the idea of meta-knowledge transfer to speed up Bayesian optimization and Neural Architecture Search.
Josif Grabocka
Josif Grabocka is an Assistant Professor of Representation Learning at the University of Freiburg, where he heads the RELEA research group. His primary interest lies in hyperparameter optimization, time-series mining and tabular data.
Citation
author = {Martin Wistuba and Josif Grabocka},
title = {Meta-Learning for Hyperparameter Optimization},
howpublished = {Tutorial at AutoMLConf 2023},
year = {2023},
url = {https://neural-architecture-search.github.io/tutorial-automlconf-2023},
}