Advancing Adaptive Learning in Non-Stationary Environments: Challenges, Methods, and Future Directions

This report explores the current landscape and challenges of adaptive learning in non-stationary environments, where systems must adjust to continuous changes in data distribution, known as concept drift. While significant progress has been made, existing methods often remain constrained by narrow applicability to specific domains and require specialized expertise. This report reviews existing techniques, emphasizing the need for universally accessible adaptive learning systems that is applicable to all kind of non-stationary environments. We propose an innovative framework inspired by drift velocity profiles [3], aiming to infer system configurations over time and address the ’what’ and ’why’ of drifts. Key desirable features for future adaptive learning algorithms are outlined, targeting improved versatility, accuracy, and explainability in industrial settings. By advancing beyond mere detection and adaptation, this work sets the stage for developing robust, model-agnostic solutions capable of proactive drift management in diverse applications.