DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process
Mohammad Abu-Shaira, Ajita Rattani, Weishi Shi
TL;DR
The paper addresses online non-linear regression under concept drift by introducing DAO-GP, a drift-aware Gaussian Process framework that is hyperparameter-free, decayed, and sparse. It combines drift detection, on-demand NLML-based hyperparameter optimization, a dynamic kernel pool, and a principled inducing-point decay mechanism to maintain memory efficiency. Empirical results show DAO-GP robustly adapts to abrupt, incremental, and gradual drift while outperforming or matching state-of-the-art online models like KPA and FITC-GP across diverse datasets. The approach offers practical advantages for real-time, non-stationary environments by balancing adaptability, uncertainty quantification, and resource constraints.
Abstract
Real-world datasets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept drift, can significantly diminish a model's predictive accuracy. Furthermore, the presence of hyperparameters in online models exacerbates this issue. These parameters are typically fixed and cannot be dynamically adjusted by the user in response to the evolving data distribution. Gaussian Process (GP) models offer powerful non-parametric regression capabilities with uncertainty quantification, making them ideal for modeling complex data relationships in an online setting. However, conventional online GP methods face several critical limitations, including a lack of drift-awareness, reliance on fixed hyperparameters, vulnerability to data snooping, absence of a principled decay mechanism, and memory inefficiencies. In response, we propose DAO-GP (Drift-Aware Online Gaussian Process), a novel, fully adaptive, hyperparameter-free, decayed, and sparse non-linear regression model. DAO-GP features a built-in drift detection and adaptation mechanism that dynamically adjusts model behavior based on the severity of drift. Extensive empirical evaluations confirm DAO-GP's robustness across stationary conditions, diverse drift types (abrupt, incremental, gradual), and varied data characteristics. Analyses demonstrate its dynamic adaptation, efficient in-memory and decay-based management, and evolving inducing points. Compared with state-of-the-art parametric and non-parametric models, DAO-GP consistently achieves superior or competitive performance, establishing it as a drift-resilient solution for online non-linear regression.