Key Points
1. The Vizier algorithm is a successful Bayesian optimization service, tuned for over 70 million objectives across Google and available to the public through Vertex Vizier, a Google Cloud product.
2. The default algorithm of Vizier is based on Gaussian process bandit optimization and has evolved to improve performance metrics, user experience, inference speed, flexibility, scalability, and reliability over time.
3. The algorithm was initially designed with a focus on the programming philosophy of C++ for low-level control and multithreading support, but it has evolved to support Python with the availability of packages such as TensorFlow Probability (TF Probability) and JAX.
4. The paper presents comprehensive details of the latest version of the Vizier default algorithm, its features, design choices, and iterations, making it useful for both researchers and practitioners.
5. An open-source Python implementation of the original Google C++ based Vizier algorithm, powered by TF Probability on JAX, has been provided, along with benchmarking against industry-wide baselines.
6. The unconventional design choice of using a zeroth-order evolutionary acquisition optimizer is discussed in detail, highlighting its key strengths.
7. Vizier's optimization study consists of a problem statement, search space, and measurement space, focusing on optimizing a blackbox function to improve performance measures over multiple iterations.
8. The paper provides a detailed overview of the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm for Bayesian optimization and its crucial components, such as input preprocessing, output preprocessing, model choice, acquisition function, and trust region.
9. Vizier's robustness and efficiency are demonstrated through benchmarking against well-established industry baselines across various practical modes, including continuous, categorical, batched, and multi-objective optimization scenarios.
Summary
The paper discusses the Vizier Gaussian Process Bandit algorithm, which has been improved considerably over multiple years through the collective experiences of numerous research efforts and user feedback. The default algorithm of the Google Vizier has been implemented with the overarching goal of robustness and versatility.
The authors provide thorough details about the algorithm's implementation choices, focusing on trial preprocessing, response surface modeling with Gaussian process prior and kernel, acquisition function definition, and evolutionary acquisition optimization. The authors emphasize the versatility and robustness of the Vizier algorithm, demonstrating its performance against well-established industry baselines on multiple practical modes.
The algorithm's performance is evaluated across various benchmarks, including standardized benchmarks and synthetic functions for continuous spaces, categorical spaces, hybrid mixed spaces, and multi-objective optimization. The algorithm's performance is compared against well-known baselines, such as Ax, BayesianOptimization, HEBO, HyperOpt, Optuna, and Scikit-Optimize.
The authors highlight the algorithm's successful handling of high-dimensional optimization, multi-objective optimization, and categorical variables. They also provide insights into the computational aspects, demonstrating the algorithm's performance on GPUs for efficient serving.
The paper concludes with an emphasis on the algorithm's robustness, versatility, and potential value for the wider research community. The implementation details and experimental results highlight the algorithm's competitive performance against established baselines in various optimization scenarios, supporting its potential usefulness for a wide variety of real-world use-cases both within Google and externally.
Reference: https://arxiv.org/abs/2408.11527