DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process

TL;DR

DAO-GP is a novel online Gaussian Process model that adaptively detects and responds to concept drift without fixed hyperparameters, offering robust, efficient non-linear regression in dynamic data environments.

Contribution

It introduces DAO-GP, a fully adaptive, hyperparameter-free, drift-aware Gaussian Process model with a built-in drift detection and decay mechanism for online regression.

Findings

Outperforms state-of-the-art models across various drift scenarios

Demonstrates robust adaptation to different types of concept drift

Maintains high predictive accuracy with efficient memory management

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.