Tag: driftmoe

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts arXiv:2507.18464v1 Announce Type: new Abstract: Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or simple voting schemes that fail to optimally leverage specialized knowledge. This…