HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO (2109.06716v3)

Abstract: To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications. Over the last years, the number of efficient algorithms and tools for HPO grew substantially. At the same time, the community is still lacking realistic, diverse, computationally cheap, and standardized benchmarks. This is especially the case for multi-fidelity HPO methods. To close this gap, we propose HPOBench, which includes 7 existing and 5 new benchmark families, with a total of more than 100 multi-fidelity benchmark problems. HPOBench allows to run this extendable set of multi-fidelity HPO benchmarks in a reproducible way by isolating and packaging the individual benchmarks in containers. It also provides surrogate and tabular benchmarks for computationally affordable yet statistically sound evaluations. To demonstrate HPOBench's broad compatibility with various optimization tools, as well as its usefulness, we conduct an exemplary large-scale study evaluating 13 optimizers from 6 optimization tools. We provide HPOBench here: https://github.com/automl/HPOBench.

An Expert Overview of HPOBench: A Benchmarking Suite for Hyperparameter Optimization

The recent proliferation of complex ML models, along with the corresponding increase in hyperparameter spaces, has underscored the importance of efficient Hyperparameter Optimization (HPO) methods. The paper "HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO," seeks to address a critical gap in HPO research: the dearth of standardized, reliable, and computationally manageable benchmarks. This paper introduces HPOBench, a suite that encompasses a wide array of multi-fidelity benchmark problems designed to advance our understanding and implementation of HPO algorithms.

Key Contributions

1. Extensive Collection of Benchmarks: HPOBench contains 12 benchmark families, comprising over 100 multi-fidelity problems. This suite includes both existing benchmarks drawn from the academic community and newly developed benchmarks, highlighting HPOBench's role in setting a new standard for comprehensive evaluation.
2. Reproducibility Through Containerization: Each benchmark is encapsulated within a Singularity container, ensuring long-term usability and robustness against evolving software dependencies. This design choice facilitates seamless repeats of experiments, empowering researchers to develop, test, and compare HPO methods without rebuilding the operating environment.
3. Efficiency and Compatibility: The suite integrates surrogate and tabular benchmarks that emulate the behavior of raw benchmarks at a fraction of the computational cost. By offering this, HPOBench significantly reduces the barrier to conducting large-scale studies, encouraging broader engagement from the research community.

Powerful Experimental Framework

HPOBench is designed to support a diverse set of HPO methodologies, including single-fidelity and multi-fidelity optimizers. The utility of this suite is demonstrated through comprehensive experiments involving 13 state-of-the-art optimization techniques across extensive benchmark families. The experiments provide valuable insights: they validate that advanced optimization methods surpass basic approaches like random search and identify scenarios where multi-fidelity optimization outperforms its single-fidelity counterparts, particularly within constrained computational budgets.

Impact and Implications

The strategic design of HPOBench has vital implications beyond mere benchmarking. It enhances our ability to evaluate HPO techniques systematically, initiate new studies in areas like multi-fidelity and transfer HPO, and foster a balanced trade-off between computational expense and statistical reliability. By addressing the practical challenges of reproducibility, integration, and execution efficiency, this benchmark suite substantially contributes to the methodological foundation of HPO research.

Furthermore, HPOBench opens avenues for future explorations, such as multi-objective optimization and meta-learning across datasets. The availability of surrogate models suggests potential rapid prototyping of HPO algorithms, stimulating research that can drive more adaptive and intelligent HPO strategies.

Conclusion

HPOBench is positioned as a pivotal resource in the landscape of HPO research, underpinned by its comprehensive benchmarks, focus on reproducibility, and support for varied optimizer families. As researchers harness this resource, it is likely to shape future innovations in hyperparameter tuning, bolster reproducible science, and facilitate the development of efficient, scalable ML models. The paper ultimately sets a benchmark (literally and figuratively) for future efforts, calling for a collaborative push towards more robust, interdisciplinary applications of machine learning.