YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization (2109.03670v4)

Abstract: When developing and analyzing new hyperparameter optimization methods, it is vital to empirically evaluate and compare them on well-curated benchmark suites. In this work, we propose a new set of challenging and relevant benchmark problems motivated by desirable properties and requirements for such benchmarks. Our new surrogate-based benchmark collection consists of 14 scenarios that in total constitute over 700 multi-fidelity hyperparameter optimization problems, which all enable multi-objective hyperparameter optimization. Furthermore, we empirically compare surrogate-based benchmarks to the more widely-used tabular benchmarks, and demonstrate that the latter may produce unfaithful results regarding the performance ranking of HPO methods. We examine and compare our benchmark collection with respect to defined requirements and propose a single-objective as well as a multi-objective benchmark suite on which we compare 7 single-objective and 7 multi-objective optimizers in a benchmark experiment. Our software is available at [https://github.com/slds-lmu/yahpo_gym].

• The paper presents YAHPO Gym, a surrogate-based benchmark that supports multi-fidelity and multi-objective evaluations in hyperparameter optimization.
• It offers 14 diverse scenarios with over 700 tasks, ensuring realistic and adaptable performance assessments for various ML algorithms.
• The authors demonstrate that surrogate benchmarks reduce bias inherent in tabular methods, promoting more reliable and reproducible HPO research.

YAHPO Gym: An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization

The paper "YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization" introduces YAHPO Gym, an innovative benchmarking library designed to evaluate hyperparameter optimization (HPO) methods. The authors provide a comprehensive collection of surrogate-based benchmark problems, which aims to overcome the limitations of traditional tabular benchmarks.

Overview of YAHPO Gym

YAHPO Gym is posited as a versatile and efficient benchmarking suite tailored to the rapidly evolving needs of hyperparameter optimization research. It stands out by providing:

1. Multi-fidelity and Multi-objective Benchmarking: The library offers scenarios that allow evaluation at varying fidelity levels and consider multiple objectives, reflecting the needs of real-world applications.
2. Surrogate-based Benchmarks: Unlike tabular benchmarks that rely on static datasets, YAHPO Gym utilizes surrogate models that dynamically predict outcomes, based on neural networks trained on historical evaluation data. These surrogates facilitate realistic approximations of complex HPO problems, maintaining computational efficiency while providing flexibility in experimental design.
3. Comprehensive Coverage: The benchmark suite encompasses 14 distinct scenarios, totaling over 700 HPO tasks. These scenarios are based on common machine learning algorithms applied to various datasets, thus ensuring rich diversity and representativeness of practical optimization tasks.
4. Open Source and Extensibility: YAHPO Gym's software is open source, allowing for community contributions and extensions. The repository features comprehensive documentation, significantly easing integration into existing HPO workflows.

Methodological Contributions and Findings

The paper critically compares surrogate-based benchmarks with tabular benchmarks, demonstrating the latter's potential to yield skewed performance rankings due to inherent discretization biases. Through empirical evaluations, the authors show that surrogate benchmarks produce more faithful performance approximations, which is paramount for deriving valid conclusions in HPO method assessment.

The authors also propose two benchmark suites—YAHPO-SO for single-objective and YAHPO-MO for multi-objective optimization. These suites are meticulously curated to balance diversity, difficulty, and efficiency, offering a standard for reliable and reproducible HPO benchmarking.

Implications and Future Directions

Practical Implications: By providing a robust testbed for hyperparameter optimization research, YAHPO Gym facilitates fair and rigorous comparison between different HPO methods. This is critical for advancing the state of the art in automated machine learning, a field whose progress is often measured through empirical performance on benchmarks.

Theoretical Implications: The introduction of surrogate models into benchmark suites allows exploration beyond fixed configurations, offering insights into the impact of hyperparameter choices on model generalization across various contexts. Researchers can exploit this flexibility to devise novel HPO strategies that potentially leverage multi-fidelity and multi-objective optimizations.

Future Developments: The paper suggests that future iterations of YAHPO Gym might include features for asynchronous evaluation and expanded multi-objective scenarios. Such extensions could further accommodate evolving research demands, embracing more complex and realistic HPO challenges reflective of industrial settings.

In conclusion, YAHPO Gym marks a significant step towards improving the reliability and scope of benchmarking in hyperparameter optimization. Its surrogate-based approach addresses critical shortcomings of traditional methods, providing a rich, adaptable framework for future HPO research. However, continuous updates and community engagement remain vital to maintaining its relevance and utility as an essential tool in the HPO landscape.