Calibrated Test-Time Guidance for Bayesian Inference
arXiv:2602.22428v1 Announce Type: new Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existing approaches, however, focus on maximizing reward rather than sampling from the true Bayesian posterior, leading to miscalibrated inference. In this work, we show that common test-time guidance methods do not recover the correct posterior distribution and identify the structural approximations responsible for this failure. We then propose consistent alternative estimators that enable calibrated sampling from the Bayesian posterior. We significantly outperform previous methods on a set of Bayesian inference tasks, and match state-of-the-art in black hole image reconstruction.
arXiv:2602.22428v1 Announce Type: new Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existing approaches, however, focus on maximizing reward rather than sampling from the true Bayesian posterior, leading to miscalibrated inference. In this work, we show that common test-time guidance methods do not recover the correct posterior distribution and identify the structural approximations responsible for this failure. We then propose consistent alternative estimators that enable calibrated sampling from the Bayesian posterior. We significantly outperform previous methods on a set of Bayesian inference tasks, and match state-of-the-art in black hole image reconstruction.
Executive Summary
This article presents a novel approach to test-time guidance for Bayesian inference, addressing the limitations of existing methods that focus on maximizing reward rather than sampling from the true Bayesian posterior. The authors identify the structural approximations responsible for the failure of existing methods and propose consistent alternative estimators that enable calibrated sampling from the Bayesian posterior. The proposed approach outperforms previous methods on a set of Bayesian inference tasks and matches state-of-the-art in black hole image reconstruction. The article makes a significant contribution to the field by providing a more accurate and reliable method for Bayesian inference, with implications for various applications in science, engineering, and finance.
Key Points
- ▸ Existing test-time guidance methods focus on maximizing reward rather than sampling from the true Bayesian posterior.
- ▸ The authors identify the structural approximations responsible for the failure of existing methods.
- ▸ A novel approach to test-time guidance for Bayesian inference is proposed, using consistent alternative estimators.
Merits
Strength
The proposed approach provides a more accurate and reliable method for Bayesian inference, enabling calibrated sampling from the Bayesian posterior.
Strength
The approach outperforms previous methods on a set of Bayesian inference tasks and matches state-of-the-art in black hole image reconstruction.
Demerits
Limitation
The proposed approach may require significant computational resources and expertise in Bayesian inference.
Limitation
The approach may not be suitable for all types of Bayesian inference tasks, particularly those with complex or high-dimensional parameter spaces.
Expert Commentary
The article presents a significant contribution to the field of Bayesian inference, providing a more accurate and reliable method for test-time guidance. The proposed approach has the potential to revolutionize the field of Bayesian inference, enabling more accurate and reliable decision-making in various applications. However, the approach may require significant computational resources and expertise in Bayesian inference, which may be a limitation for some users. Additionally, the approach may not be suitable for all types of Bayesian inference tasks, particularly those with complex or high-dimensional parameter spaces.
Recommendations
- ✓ Future research should focus on developing more efficient and scalable implementations of the proposed approach.
- ✓ The proposed approach should be evaluated on a broader range of Bayesian inference tasks to validate its performance and limitations.
