6 citations · 6 across the 1 of their papers we have counts for
3 papers
stat.ML2019
Correcting Predictions for Approximate Bayesian Inference
Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami
Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate infere…
stat.ML2019★ 6 cited
Variational Bayesian Decision-making for Continuous Utilities
Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami
Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not…
stat.ML2017
Importance Sampled Stochastic Optimization for Variational Inference
Joseph Sakaya, Arto Klami
Variational inference approximates the posterior distribution of a probabilistic model with a parameterized density by maximizing a lower bound for the model evidence. Modern solut…