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math.ST2025
Gradient descent inference in empirical risk minimization
Qiyang Han, Xiaocong Xu
Gradient descent is one of the most widely used iterative algorithms in modern statistical learning. However, its precise algorithmic dynamics in high-dimensional settings remain o…
math.ST2025
A leave-one-out approach to approximate message passing
Zhigang Bao, Qiyang Han, Xiaocong Xu
Approximate message passing (AMP) has emerged both as a popular class of iterative algorithms and as a powerful analytic tool in a wide range of statistical estimation problems and…
math.ST2025
Entrywise dynamics and universality of general first order methods
Qiyang Han
General first order methods (GFOMs), including various gradient descent and AMP algorithms, constitute a broad class of iterative algorithms in modern statistical learning problems…