SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Abstract

Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at https://github.com/automl/SMAC3.

References in corpus (5)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Scalable Global Optimization via Local Bayesian Optimization
• Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
• Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
• Squirrel: A Switching Hyperparameter Optimizer

Cited by in corpus (11)

• GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization
• In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
• TUNA: Tuning Unstable and Noisy Cloud Applications
• Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations
• HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
• Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks
• On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+($\lambda$,$\lambda$))-GA
• LMFD: Latent Monotonic Feature Discovery
• Practitioner Motives to Use Different Hyperparameter Optimization Methods
• Searching in the Forest for Local Bayesian Optimization
• Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAS