Constrained decision-making problems are pervasive across various disciplines.

The capability of generating high-quality long-horizon trajectories makes it a promising research direction.

Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor.

Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image.