The paper develops three convex learning settings for hybrid models that enforce interpretability via reference regularization, subspace restrictions, and nonlinear manifold restrictions, re-parameterized through lifted operator features as kernel mixtures of interpretable components.
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Convex Hybrid Modeling: An Operator-Based Approach
The paper develops three convex learning settings for hybrid models that enforce interpretability via reference regularization, subspace restrictions, and nonlinear manifold restrictions, re-parameterized through lifted operator features as kernel mixtures of interpretable components.