Robust, Accurate Stochastic Optimization for Variational Inference

Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari

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We consider the problem of fitting variational posterior approximations using stochastic optimization methods. The performance of these approximations depends on (1) how well the variational family matches the true posterior distribution,(2) the choice of divergence, and (3) the optimization of the variational objective. We show that even in the best-case scenario when the exact posterior belongs to the assumed variational family, common stochastic optimization methods lead to poor variational approximations if the problem dimension is moderately large. We also demonstrate that these methods are not robust across diverse model types. Motivated by these findings, we develop a more robust and accurate stochastic optimization framework by viewing the underlying optimization algorithm as producing a Markov chain. Our approach is theoretically motivated and includes a diagnostic for convergence and a novel stopping rule, both of which are robust to noisy evaluations of the objective function. We show empirically that the proposed framework works well on a diverse set of models: it can automatically detect stochastic optimization failure or inaccurate variational approximation
Original languageEnglish
Title of host publicationProceedings of 34th Conference on Neural Information Processing Systems
Number of pages13
Publication date2020
Publication statusPublished - 2020
Event34th Conference on Neural Information Processing Systems - Virtual event
Duration: 6 Dec 202012 Dec 2020


Conference34th Conference on Neural Information Processing Systems
LocationVirtual event
Internet address

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