ML Anthology

Yes, but Did It Work?: Evaluating Variational Inference

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman
ICML 2018 pp. 5581-5590
/icml/2018/yao2018icml-yes/

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.

PDF ICML Semantic Scholar

Cite

Text

Yao et al. "Yes, but Did It Work?: Evaluating Variational Inference." International Conference on Machine Learning, 2018.

Markdown

[Yao et al. "Yes, but Did It Work?: Evaluating Variational Inference." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/yao2018icml-yes/)

BibTeX

@inproceedings{yao2018icml-yes,
  title     = {{Yes, but Did It Work?: Evaluating Variational Inference}},
  author    = {Yao, Yuling and Vehtari, Aki and Simpson, Daniel and Gelman, Andrew},
  booktitle = {International Conference on Machine Learning},
  year      = {2018},
  pages     = {5581-5590},
  volume    = {80},
  url       = {https://mlanthology.org/icml/2018/yao2018icml-yes/}
}