The paper proves polynomial mixing time upper bounds for three data augmentation algorithms (ProbitDA, LogitDA, LassoDA) with explicit dependence on design matrix, prior, n, and d.
arXiv preprint arXiv:1911.01469 , year=
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A proximal gradient sampler for composite log-concave distributions achieves near-optimal iteration complexity of order kappa sqrt(d) log^4(1/epsilon) in total variation distance under strong convexity and smoothness.
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Fast Mixing of Data Augmentation Algorithms: Bayesian Probit, Logit, and Lasso Regression
The paper proves polynomial mixing time upper bounds for three data augmentation algorithms (ProbitDA, LogitDA, LassoDA) with explicit dependence on design matrix, prior, n, and d.
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A proximal gradient algorithm for composite log-concave sampling
A proximal gradient sampler for composite log-concave distributions achieves near-optimal iteration complexity of order kappa sqrt(d) log^4(1/epsilon) in total variation distance under strong convexity and smoothness.