# Variational Bayesian Decision-making for Continuous Utilities

arXiv: https://arxiv.org/abs/1902.00792 · HTML: https://mlsanity.com/paper/1902.00792/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 16 of 16)

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- [Amortized Bayesian Decision Making for simulation-based models](https://mlsanity.com/paper/2312.02674/) (2023)
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- [Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention](https://mlsanity.com/paper/2503.05622/) (2025)
- [Inverse decision-making using neural amortized Bayesian actors](https://mlsanity.com/paper/2409.03710/) (2024)
- [Robust Bayesian Decision Making under Adversarial Uncertainty](https://mlsanity.com/paper/2607.08590/) (2026)
- [SCOPE Before You Sample: Conformal Scores Guide Posterior Exploration for Fast and Calibrated Bayesian Predictive Inference](https://mlsanity.com/paper/2610.17146/) (2026)

## Abstract (arXiv)

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.