Beschrijving
Hyperparameter Optimization (HPO) plays a pivotal role in modern machine learning pipelines, determining model performance, generalization, and stability. However, HPO is inherently computationally intensive, resulting in significant energy consumption and environmental cost. As models and datasets grow, this computational burden scales dramatically, making traditional HPO practices unsustainable.This tutorial provides an overview of state-of-the-art HPO methods, with a particular emphasis on Bayesian Optimization (BO) as a data-efficient and principled framework for black-box optimization. We will begin by revisiting the theoretical underpinnings of BO, including surrogate modeling, acquisition functions, and extensions to constrained and multi-objective settings. Building upon this foundation, we introduce the emerging research area of sustainable HPO, focusing on approaches that explicitly target energy and resource efficiency, such as multi-fidelity modeling. This way, the tutorial contributes to the vertical area of Sustainable and Trustworthy AI.
| Periode | 8 mei 2026 |
|---|---|
| Evenementstitel | IEEE CAI 2026 |
| Evenementstype | Congres |
| Locatie | Granada, SpanjeToon op kaart |
| Mate van erkenning | Internationaal |