Skill UI

A comprehensive guide for researchers on elevating empirical findings into substantial theoretical contributions. It teaches methods to transition from simply stating 'X affects Y' to developing novel concepts, propositions, or analytical frameworks, ensuring the work engages in genuine theoretical dialogue and positions local experiences within a broader knowledge system.

A comprehensive guide for authors submitting to venues focused on probabilistic AI, including UAI. It emphasizes achieving maximum formal rigor by enforcing strict notational discipline, clearly separating conditioning from intervention, explicitly stating assumptions, structuring the main body for maximum impact, and calibrating claims to match empirical evidence. Essential for papers involving graphical models, causality, and Bayesian methods.