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arXiv Machine Learning By Somjit Roy, Pritam Dey, Debdeep Pati, Bani K. Mallick

A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods

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The paper introduces a new variational inference framework that uses tangent transformations to handle strongly super‑Gaussian likelihoods across a wide range of probability models. By constructing tangent minorants of the log‑likelihood through convex duality, the method achieves conjugacy with Gaussian priors, enabling tractable inference where traditional approaches struggle. The authors provide algorithmic convergence guarantees and near‑parametric risk bounds, and demonstrate superior scalability and accuracy on both simulated and real‑world datasets compared to existing variational algorithms.

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