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Cross-Fitting for Neural Posterior Estimation

Jeffrey Regier · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26733 · cited by 0 works (OpenAlex) · open-access PDF

Korean

NPE의 훈련 목적, log-probability는 훈련 포인트에 자발적으로 높은 밀도를 할당하는 후속 접근을 선호하며, 흡수 조건 밀도 추정기의 전형적인 가족 (예를 들어, 정상화 흐름, Gaussian 인코더)은 훈련 실현의 시뮬레이션 세트에 충분히 유연합니다. NPE의 훈련 목적, log-probability, 훈련 포인트에 자발적으로 높은 밀도를 할당하는 후속 접근을 선호합니다. 극단적 인 과도한 신뢰성의 결과 정책은 거래를 피하기 위해 나쁘게 번역합니다. 초기 멈추기 및 기타 규칙화는이 퇴행을 방지 할 수 있지만, 그들은 샘플에 의해 설정된 불리한 최적화 경로를 따라

Machine translation (Meta M2M100); the English original follows.

Abstract

Neural Posterior Estimation (NPE) fits an amortized conditional density estimator to a simulated training set of complete-data realizations. NPE's training objective, log-likelihood, favors posterior approximations that assign arbitrarily high density to the training points, and typical families of amortized conditional density estimators (e.g., normalizing flows, Gaussian encoders) are flexible enough to oblige. The resulting policy of extreme overconfidence translates poorly to held-out data. Early stopping and other regularizers can prevent this degeneracy, but they are limited to selecting posterior approximations along an unfavorable optimization trajectory set by in-sample residuals, where the posterior mean is underfit while the posterior variance is overfit. To avoid this trade-off, we propose Cross-Fitting NPE (CF-NPE), which uses sample splitting to separate conditional-mean estimation from the estimation of the posterior's higher moments. Using $K$-fold cross-fitting, CF-NPE fits $K$ posterior-mean regressors and then fits a conditional density estimator to the resulting out-of-fold residuals. In experiments with synthetic data and in a cosmological application, CF-NPE achieves higher held-out log-likelihood than standard NPE and typically improves calibration. CF-NPE is most useful in high-dimensional, simulation-limited settings.

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Topics: Estimator, Mathematics, Density estimation, Algorithm, Gaussian, Set (abstract data type)

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