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arXiv Machine Learning By Jiafang Song, Sandipan Pramanik, Abhirup Datta

Neural Variational Cut Posteriors without Upstream Data

Read the original on arXiv Machine Learning →

The paper introduces NeVI‑Cut, a modular variational inference method for cut‑Bayes that does not require access to upstream data or models. It approximates the cut‑posterior by minimizing the expected downstream conditional Kullback‑Leibler divergence, using conditional normalizing flows as the variational family. The authors provide fixed‑data convergence rates, establish uniform KL approximation results for flow classes, and demonstrate the algorithm’s speed and accuracy on several applications.

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