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Disambiguation of "Huggins, Jonathan H."

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Using bagged posteriors for robust inference and model criticism

, and . (2019)cite arxiv:1912.07104.

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H -H Kaatz

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Other publications of authors with the same name

A Statistical Learning Theory Framework for Supervised Pattern Discovery., and . SDM, page 506-514. SIAM, (2014)Using bagged posteriors for robust inference and model criticism, and . (2019)cite arxiv:1912.07104.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)LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 6315-6324. PMLR, (2019)Toward a Theory of Pattern Discovery., and . ISAIM, (2014)Validated Variational Inference via Practical Posterior Error Bounds., , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 1792-1802. PMLR, (2020)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.Risk and Regret of Hierarchical Bayesian Learners., and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1442-1451. JMLR.org, (2015)Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data., , , , , , and . CoRR, (2018)Data-dependent compression of random features for large-scale kernel approximation., , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1822-1831. PMLR, (2019)