%0 Journal Article %1 kusmierczyk2019correcting %A Kuśmierczyk, Tomasz %A Sakaya, Joseph %A Klami, Arto %D 2019 %K approximate bayesian sampling uncertainty %T Correcting Predictions for Approximate Bayesian Inference %U http://arxiv.org/abs/1909.04919 %X Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.

@article{kusmierczyk2019correcting, abstract = {Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.}, added-at = {2020-01-13T12:35:55.000+0100}, author = {Kuśmierczyk, Tomasz and Sakaya, Joseph and Klami, Arto}, biburl = {https://www.bibsonomy.org/bibtex/2e12db6bdd904d5a1b61903f2f414da96/kirk86}, description = {[1909.04919] Correcting Predictions for Approximate Bayesian Inference}, interhash = {62d524eeb35b6f473f93f8dbf5aec29d}, intrahash = {e12db6bdd904d5a1b61903f2f414da96}, keywords = {approximate bayesian sampling uncertainty}, note = {cite arxiv:1909.04919}, timestamp = {2020-01-13T12:35:55.000+0100}, title = {Correcting Predictions for Approximate Bayesian Inference}, url = {http://arxiv.org/abs/1909.04919}, year = 2019 }