Coresets for Scalable Bayesian Logistic Regression

@inproceedings{Huggins2016CoresetsFS,
  title={Coresets for Scalable Bayesian Logistic Regression},
  author={Jonathan H. Huggins and Trevor Campbell and Tamara Broderick},
  booktitle={NIPS},
  year={2016}
}
The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. Standard Bayesian inference algorithms are computationally expensive, however, making their direct application to large datasets difficult or infeasible. Recent work on scaling Bayesian inference has focused on modifying the underlying algorithms to, for example, use only a random data subsample at each iteration. We… CONTINUE READING
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