2 papers
math.ST2024
High-dimensional logistic regression with missing data: Imputation, regularization, and universality
Kabir Aladin Verchand, Andrea Montanari
We study high-dimensional, ridge-regularized logistic regression in a setting in which the covariates may be missing or corrupted by additive noise. When both the covariates and th…
stat.ME2024
Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition
Andrea Montanari, Yuchen Wu
We consider the problem of sampling from the posterior distribution of a -dimensional coefficient vector , given linear observations $\boldsymbol{y} = \boldsymbol…