Yes, but Did It Work?: Evaluating Variational Inference

@InProceedings{pmlr-v80-yao18a,
  title = 	 {Yes, but Did It Work?: Evaluating Variational Inference},
  author =       {Yao, Yuling and Vehtari, Aki and Simpson, Daniel and Gelman, Andrew},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {5581--5590},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/yao18a/yao18a.pdf},
  url = 	 {https://proceedings.mlr.press/v80/yao18a.html},
  abstract = 	 {While it’s always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation. We propose two diagnostic algorithms to alleviate this problem. The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate. The variational simulation-based calibration (VSBC) assesses the average performance of point estimates.}
}