Next Generation of Empirical Performance Prediction

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.