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Validated Variational Inference via Practical Posterior Error Bounds

, , , and . (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.

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Validated Variational Inference via Practical Posterior Error Bounds, , , and . (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.A Targeted Accuracy Diagnostic for Variational Approximations., , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 8351-8372. PMLR, (2023)Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees., , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 796-805. PMLR, (2019)Practical Posterior Error Bounds from Variational Objectives., , , and . CoRR, (2019)Validated Variational Inference via Practical Posterior Error Bounds., , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 1792-1802. PMLR, (2020)