AI RESEARCH
Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)
arXiv CS.LG
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ArXi:2603.26923v1 Announce Type: cross Parametric models deployed in non-stationary environments degrade as the underlying data distribution evolves over time (a phenomenon known as temporal domain drift). In the current work, we present KOMET (Koopman Operator identification of Model parameter Evolution under Temporal drift), a model-agnostic, data-driven framework that treats the sequence of trained parameter vectors as the trajectory of a nonlinear dynamical system and identifies its governing linear operator via Extended Dynamic Mode Decomposition (EDMD). A warm-start sequential.