IPW is justified in post-Bayesian inference by reweighting the KL divergence to produce generalized belief posteriors that correct for selection bias.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
A relative inexact proximal ALM with a tailored semismooth Newton solver solves sparse spectral-risk optimization faster than ADMM while matching stationarity and sparsity on synthetic and real data.
citing papers explorer
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Inverse Probability Weighting in a Post-Bayesian World
IPW is justified in post-Bayesian inference by reweighting the KL divergence to produce generalized belief posteriors that correct for selection bias.
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A Semismooth Newton Augmented Lagrangian Method for Sparse Spectral Risk Optimization
A relative inexact proximal ALM with a tailored semismooth Newton solver solves sparse spectral-risk optimization faster than ADMM while matching stationarity and sparsity on synthetic and real data.