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2018 ICML ICML 2018

Yes, but Did It Work?: Evaluating Variational Inference

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.

❓ The Questioner
πŸŒ‰ Interdisciplinary Bridge β€” Deep Learning and Machine Learning
🧭 Keyword Pioneer β€” diagnostic algorithm
🐝 Cross-Pollinator β€” Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Yuling Yao , Aki Vehtari , Daniel Simpson , Andrew Gelman

Topics

Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Optimization & Theory > Theory Deep Learning > Models > Variational Inference

Keywords

variational inference posterior distribution importance sampling goodness of fit diagnostic algorithm
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