3 citations · 4 across the 3 of their papers we have counts for
3 papers
math.ST2023
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models
Michael Celentano, Zhou Fan, Licong Lin +1
We study mean-field variational inference in a Bayesian linear model when the sample size n is comparable to the dimension p. In high dimensions, the common approach of minimizing…
math.ST2023★ 1 cited
Challenges of the inconsistency regime: Novel debiasing methods for missing data models
Michael Celentano, Martin J. Wainwright
We study semi-parametric estimation of the population mean when data is observed missing at random (MAR) in the "inconsistency regime", in which neither the outcome model n…
math.PR2022★ 3 cited
Sudakov-Fernique post-AMP, and a new proof of the local convexity of the TAP free energy
Michael Celentano
In many problems in modern statistics and machine learning, it is often of interest to establish that a first order method on a non-convex risk function eventually enters a region…