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This paper establishes a general theory of the Adam optimizer for time-varying and nonstationary stochastic systems, providing parameter tracking and output prediction error bounds under a stochastic excitation condition that allows nonstationary and dependent data.
This paper proposes Continuous Power Forecasting, treating power forecasting as a continual learning problem to handle nonstationary conditions. It evaluates six CL approaches on real-world datasets, showing benefits in adaptation and mitigating catastrophic forgetting.
Proposes CPSS, a runtime safety mechanism that converts cumulative cost constraints into adaptive state-level thresholds for safe reinforcement learning in nonstationary environments, demonstrating reduced violations in highway merging scenarios.