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Global sharpness regularization can lead to underfitting in Probabilistic Circuits, but a new adaptive approach recovers generalization while maintaining robustness.
Sampling from a new probabilistic framework yields diverse and plausible recourse options without sacrificing feasibility, revolutionizing how we approach algorithmic decision-making.
