Samenvatting
Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem. In this work, we propose a zero-shot method to meta-learn symbolic default hyperparameter configurations that are expressed in terms of the properties of the dataset. This enables a much faster, but still data-dependent, configuration of the ML algorithm, compared to standard hyperparameter optimization approaches. In the past, symbolic and static default values have usually been obtained as hand-crafted heuristics. We propose an approach of learning such symbolic configurations as formulas of dataset properties from a large set of prior evaluations on multiple datasets by optimizing over a grammar of expressions using an evolutionary algorithm. We evaluate our method on surrogate empirical performance models as well as on real data across 6 ML algorithms on more than 100 datasets and demonstrate that our method indeed finds viable symbolic defaults.
Originele taal-2 | Engels |
---|---|
Titel | GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion |
Subtitel | Proceedings of the Genetic and Evolutionary Computation Conference Companion |
Uitgeverij | Association for Computing Machinery, Inc |
Pagina's | 151-152 |
Aantal pagina's | 2 |
ISBN van elektronische versie | 978-1-4503-8351-6 |
DOI's | |
Status | Gepubliceerd - jul. 2021 |