Variational Bayesian Decision-making for Continuous Utilities

2019/02/02 by Tomasz Kuśmierczyk, Kuśmierczyk, Tomasz, Joseph Sakaya +3 · 1 citation
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms

paper · pdf · doi:10.48550/arxiv.1902.00792

openalex publication_date 2019/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Auto-Encoding Variational Bayes
• Edward: A library for probabilistic modeling, inference, and criticism
• Boosting Black Box Variational Inference
• Generalized Variational Inference: Three arguments for deriving new Posteriors
• Variational Inference: A Review for Statisticians

Cited by

• Advances in Projection Predictive Inference

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