Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization

Abstract: Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. We believe these benchmarks provide an easy and efficient way to conduct reproducible experiments for neural hyperparameter search. Our benchmarks consist of a large grid of configurations of a feed forward neural network on four different regression datasets including architectural hyperparameters and hyperparameters concerning the training pipeline. Based on this data, we performed an in-depth analysis to gain a better understanding of the properties of the optimization problem, as well as of the importance of different types of hyperparameters. Second, we exhaustively compared various different state-of-the-art methods from the hyperparameter optimization literature on these benchmarks in terms of performance and robustness.

• The paper introduces a benchmark suite that simulates joint hyperparameter and architecture search to significantly reduce computational expenses.
• It evaluates seven HPO methods over 62,208 configurations on four regression datasets to analyze performance metrics and hyperparameter importance.
• The findings highlight trade-offs between performance and robustness, providing reproducible baselines for future HPO and NAS research.

An Evaluation of Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization

The paper, "Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization," provides a systematic investigation into the creation and usage of tabular benchmarks designed explicitly for Hyperparameter Optimization (HPO) and Neural Architecture Search (NAS) in neural networks. These benchmarks aim to reduce the computational burden typically associated with HPO tasks, fostering a more efficient testing and validation process that remains true to the complexities of real-world optimization problems.

Problem Statement

The field of neural networks often encounters the challenge of hyperparameter selection and architecture configuration, which traditionally relies heavily on trial-and-error by practitioners. Current methods in HPO consume significant computational resources because each function evaluation involves training and validating a neural network model extensively. This overhead has impeded the field's progress and makes reproducibility and comprehensive method comparison arduous.

Contributions and Methodology

This paper offers a series of lightweight benchmarks mimicking the HPO processes, allowing for a thorough evaluation without the typical computational expense. The benchmarks encompass a feed-forward neural network architecture evaluated on four regression datasets from the UCI repository: protein structure, slice localization, naval propulsion, and Parkinson's telemonitoring. The extensive dataset encapsulates various architectural choices and hyperparameter configurations, methodically generating a grid of 62,208 possible configurations. The authors use this grid to decode the optimization landscape, focusing on hyperparameter importance and interactions.

Additionally, the paper systematically examines seven HPO methods: Random Search, SMAC, Tree Parzen Estimator, Bohamiann, Regularized Evolution, Hyperband, and BOHB, analyzing their performance and robustness using these benchmarks. This comparative study provides novel insights into the strengths and weaknesses of each method.

Key Findings and Results

• Hyperparameter Importance: Initial learning rate is recurrently observed as a significantly transformative hyperparameter across datasets when appraised through fANOVA analyses. However, importance is context-dependent, as in the case where Dropout or Batch Size may assume critical importance under specific configuration constraints.
• Comparison of HPO Methods: Regularized Evolution demonstrates superior performance in terms of mean test error while Bayesian optimization methods, notably TPE, benefit from meaningfully scaling with accumulated data, surpassing Random Search upon model convergence. Interestingly, BOHB, leveraging successively halved incumbent testing, showed proficiency in early-stage model training.
• Robustness: The study also highlights that while Regularized Evolution outperformed others in mean performance, it exhibited lower robustness in consistency. The instability was evidenced through higher variance in results when benchmarked across numerous independent runs, underscoring the necessity for deployment considerations balancing performance and stability.

Implications and Future Directions

The benchmark suite crafted in this research opens a pathway for validating HPO methods economically, availing researchers with the capability to explore method improvements or novel approaches without exhaustive computational investments. It unequivocally provides a baseline for fairness and reproducibility in a domain of research that traditionally suffers from severe resource-intense barriers.

For future studies, extending the benchmarks to incorporate more diverse neural network architectures and varied dataset complexities would be insightful. The capacity to extrapolate these findings into practical settings across diverse machine learning tasks could help to refine HPO techniques, reducing model development cycles, and ultimately achieving superior model performances more consistently.

In conclusion, this paper elucidates a practical, empirical approach to HPO evaluations, offering a comprehensive resource that ameliorates reproducibility issues and enables strategic improvements in optimization strategies.