Self-Correcting Bayesian Optimization through Bayesian Active Learning
This work addresses a known bottleneck in Bayesian optimization and active learning for researchers and practitioners, offering incremental improvements through novel acquisition functions.
The paper tackles the problem of Gaussian processes' dependence on hyperparameters in Bayesian optimization and active learning by introducing two acquisition functions that prioritize hyperparameter learning, with SAL outperforming state-of-the-art in active learning and SCoreBO achieving improved hyperparameter learning rates and outperforming latest methods on benchmarks.
Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full potential, and little effort is devoted to finding good hyperparameters in the literature. We demonstrate the impact of selecting good hyperparameters for GPs and present two acquisition functions that explicitly prioritize hyperparameter learning. Statistical distance-based Active Learning (SAL) considers the average disagreement between samples from the posterior, as measured by a statistical distance. SAL outperforms the state-of-the-art in Bayesian active learning on several test functions. We then introduce Self-Correcting Bayesian Optimization (SCoreBO), which extends SAL to perform Bayesian optimization and active learning simultaneously. SCoreBO learns the model hyperparameters at improved rates compared to vanilla BO, while outperforming the latest Bayesian optimization methods on traditional benchmarks. Moreover, we demonstrate the importance of self-correction on atypical Bayesian optimization tasks.