Generalization Bound for a General Class of Neural Ordinary Differential Equations
This work addresses the need for theoretical guarantees on unseen data performance for a broader class of neural ODE models, which is incremental as it extends prior results from linear or controlled cases to general nonlinear dynamics.
The authors tackled the problem of deriving generalization error bounds for neural ordinary differential equations (neural ODEs) with general nonlinear dynamics, establishing bounds for both time-dependent and time-independent cases and analyzing the effects of overparameterization and domain constraints.
Neural ordinary differential equations (neural ODEs) are a popular type of deep learning model that operate with continuous-depth architectures. To assess how well such models perform on unseen data, it is crucial to understand their generalization error bounds. Previous research primarily focused on the linear case for the dynamics function in neural ODEs - Marion, P. (2023), or provided bounds for Neural Controlled ODEs that depend on the sampling interval Bleistein et al. (2023). In this work, we analyze a broader class of neural ODEs where the dynamics function is a general nonlinear function, either time dependent or time independent, and is Lipschitz continuous with respect to the state variables. We showed that under this Lipschitz condition, the solutions to neural ODEs have solutions with bounded variations. Based on this observation, we establish generalization bounds for both time-dependent and time-independent cases and investigate how overparameterization and domain constraints influence these bounds. To our knowledge, this is the first derivation of generalization bounds for neural ODEs with general nonlinear dynamics.