Recommendations for Baselines and Benchmarking Approximate Gaussian Processes

arXiv:2402.09849v1 Announce Type: new
Abstract: Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training without user intervention. However, in many realistic settings, approximations are typically needed, which typically do require tuning. We argue that this requirement for tuning complicates evaluation, which has led to a lack of a clear recommendations on which method should be used in which situation. To address this, we …

abstract arxiv benchmarking cs.lg gaussian processes gps hyperparameter processes recommendations stat.ml training type