Year: 2023
Type: conference-paper
Abstract: Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models, but it can be computationally expensive.To reduce costs, Multi-fidelity HPO (MF-HPO) leverages intermediate accuracy levels in the learning process and discards low-performing models early on.We conducted a comparison of various representative MF-HPO methods against a simple baseline on classical benchmark data.The baseline involved discarding all models except the Top-K after training for only one epoch, followed by further training to select the best model.Surprisingly, this baseline achieved similar results to its counterparts, while requiring an order of magnitude less computation.Upon analyzing the learning curves of the benchmark data, we observed a few dominant learning curves, which explained the success of our baseline.This suggests that researchers should (1) always use the suggested baseline in benchmarks and (2) broaden the diversity of MF-HPO benchmarks to include more complex cases.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4386815423