Safe Active Learning for Gaussian Differential Equations
This work addresses the need for safe and efficient data collection in GPODE models, which is crucial for applications where data collection is costly or safety-critical, representing an incremental advancement in active learning for this domain.
The paper tackles the problem of efficiently and safely collecting training data for Gaussian Process differential equations (GPODE) models, proposing a Safe Active Learning (SAL) algorithm that suggests non-safety-critical data points through constrained optimization, and demonstrates its superiority over non-active methods on two examples.
Gaussian Process differential equations (GPODE) have recently gained momentum due to their ability to capture dynamics behavior of systems and also represent uncertainty in predictions. Prior work has described the process of training the hyperparameters and, thereby, calibrating GPODE to data. How to design efficient algorithms to collect data for training GPODE models is still an open field of research. Nevertheless high-quality training data is key for model performance. Furthermore, data collection leads to time-cost and financial-cost and might in some areas even be safety critical to the system under test. Therefore, algorithms for safe and efficient data collection are central for building high quality GPODE models. Our novel Safe Active Learning (SAL) for GPODE algorithm addresses this challenge by suggesting a mechanism to propose efficient and non-safety-critical data to collect. SAL GPODE does so by sequentially suggesting new data, measuring it and updating the GPODE model with the new data. In this way, subsequent data points are iteratively suggested. The core of our SAL GPODE algorithm is a constrained optimization problem maximizing information of new data for GPODE model training constrained by the safety of the underlying system. We demonstrate our novel SAL GPODE's superiority compared to a standard, non-active way of measuring new data on two relevant examples.