mlsanityData to 7 Oct 2026

Variational Bayesian Decision-making for Continuous Utilities

arXiv 1902.00792 ↗First posted 2 Feb 2019stat.ML cs.LGcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2019.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 5; 2025: 2; 2026: 1.

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  1. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 20
  2. 2022Control-Aware Prediction Objectives for Autonomous Drivingcited by 18
  3. 2021Targeted Active Learning for Bayesian Decision-Makingcited by 12
  4. 2021Post-hoc loss-calibration for Bayesian neural networkscited by 9
  5. 2024Approximation-Aware Bayesian Optimizationcited by 8
  6. 2022Loss-calibrated expectation propagation for approximate Bayesian decision-makingcited by 5
  7. 2022Online Probabilistic Model Identification using Adaptive Recursive MCMCcited by 3
  8. 2024Predictive variational inference: Learn the predictively optimal posterior distributioncited by 3
  9. 2021Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatformscited by 2
  10. 2023Amortized Bayesian Decision Making for simulation-based modelscited by 1
  11. 2024Understanding and mitigating difficulties in posterior predictive evaluationcited by 1
  12. 2025Making Reliable and Flexible Decisions in Long-tailed Classificationcited by 1
3 more
  1. 2025Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for interventioncited by 1
  2. 2024Inverse decision-making using neural amortized Bayesian actorscited by 0
  3. 2026Robust Bayesian Decision Making under Adversarial Uncertaintycited by 0