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Almost-sure consistency and optimal convergence rates for sparse function recovery reveal critical insights into statistical inverse learning under noise and indirect observations.
By transforming the training of neural likelihood surrogates into a strictly convex problem, this framework guarantees convergence to the true likelihood, overcoming traditional modeling limitations.
