Two-block Hadamard rotations match uniform rotations in each coordinate with improving accuracy as dimension grows but differ substantially in their overall distribution, with explicit bounds.
Fundamentals of stein’s method
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Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
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Approximating Uniform Random Rotations by Two-Block Structured Hadamard Rotations in High Dimensions
Two-block Hadamard rotations match uniform rotations in each coordinate with improving accuracy as dimension grows but differ substantially in their overall distribution, with explicit bounds.
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Robust Conditional Conformal Prediction via Branched Normalizing Flow
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.