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An upper bound on squared Hellinger risk for looped sieve maximum likelihood and a minimax lower bound over the tuned untied family are established, revealing a parameter--iteration--accuracy tradeoff: repeated computation can improve approximation without adding parameters, while increasing computational cost and fitted-class complexity.
Ditch slow diffusion models: MAGT offers faster, single-pass generative sampling with better support fidelity by learning a manifold-aligned transport.
