Poster
Variational Bayesian Decision-making for Continuous Utilities
Tomasz Kuśmierczyk · Joseph Sakaya · Arto Klami

Tue Dec 10th 05:30 -- 07:30 PM @ East Exhibition Hall B + C #172

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 have access to the full posterior and resort to approximate inference strategies. In such cases, taking the eventual decision-making task into account while performing the inference allows for calibrating the posterior approximation to maximize the utility. We present an automatic pipeline that co-opts continuous utilities into variational inference algorithms to account for decision-making. We provide practical strategies for approximating and maximizing the gain, and empirically demonstrate consistent improvement when calibrating approximations for specific utilities.

Author Information

Tomasz Kuśmierczyk (University of Helsinki)
Joseph Sakaya (University of Helsinki)
Arto Klami (University of Helsinki)