Year: 2026
Type: conference-paper
Abstract: Empirical performance models (EPMs) predict algorithm performance without execution, enabling applications such as algorithm selection, surrogate-based optimisation, and benchmarking. However, their effectiveness is currently constrained by the quality of feature representations and the predictive models themselves. My thesis advances EPMs by addressing both limitations. To further enhance usability and foster broader adoption, I also introduce a Python library that unifies state-of-the-art methods under a single API. These contributions aim to make EPMs more accurate, versatile, and accessible to the broader AI community.
Author: Hadar Shavit
Institution: RWTH Aachen University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7138836986