A unifying tutorial on approximate message passing
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …
extremely popular in various structured high-dimensional statistical problems. Although the …
Approximate message passing algorithms for rotationally invariant matrices
Z Fan - The Annals of Statistics, 2022 - projecteuclid.org
Approximate Message Passing algorithms for rotationally invariant matrices Page 1 The
Annals of Statistics 2022, Vol. 50, No. 1, 197–224 https://doi.org/10.1214/21-AOS2101 © …
Annals of Statistics 2022, Vol. 50, No. 1, 197–224 https://doi.org/10.1214/21-AOS2101 © …
Tfpnp: Tuning-free plug-and-play proximal algorithms with applications to inverse imaging problems
Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal
algorithms, for example, the alternating direction method of multipliers (ADMM), with …
algorithms, for example, the alternating direction method of multipliers (ADMM), with …
Optimization-Based AMP for Phase Retrieval: The Impact of Initialization and Regularization
We consider an ℓ 2-regularized non-convex optimization problem for recovering signals
from their noisy phaseless observations. We design and study the performance of a …
from their noisy phaseless observations. We design and study the performance of a …
Which bridge estimator is the best for variable selection?
Which bridge estimator is the best for variable selection? Page 1 The Annals of Statistics 2020,
Vol. 48, No. 5, 2791–2823 https://doi.org/10.1214/19-AOS1906 © Institute of Mathematical …
Vol. 48, No. 5, 2791–2823 https://doi.org/10.1214/19-AOS1906 © Institute of Mathematical …
A scalable estimate of the out-of-sample prediction error via approximate leave-one-out cross-validation
The paper considers the problem of out-of-sample risk estimation under the high
dimensional settings where standard techniques such as K-fold cross-validation suffer from …
dimensional settings where standard techniques such as K-fold cross-validation suffer from …
Sharp asymptotics and optimal performance for inference in binary models
We study convex empirical risk minimization for high-dimensional inference in binary
models. Our first result sharply predicts the statistical performance of such estimators in the …
models. Our first result sharply predicts the statistical performance of such estimators in the …
Inference for high-dimensional instrumental variables regression
This paper concerns statistical inference for the components of a high-dimensional
regression parameter despite possible endogeneity of each regressor. Given a first-stage …
regression parameter despite possible endogeneity of each regressor. Given a first-stage …
Algorithmic analysis and statistical estimation of SLOPE via approximate message passing
SLOPE is a relatively new convex optimization procedure for high-dimensional linear
regression via the sorted ℓ 1 penalty: the larger the rank of the fitted coefficient, the larger the …
regression via the sorted ℓ 1 penalty: the larger the rank of the fitted coefficient, the larger the …
Algorithmic analysis and statistical estimation of slope via approximate message passing
SLOPE is a relatively new convex optimization procedure for high-dimensional linear
regression via the sorted $\ell_1 $ penalty: the larger the rank of the fitted coefficient, the …
regression via the sorted $\ell_1 $ penalty: the larger the rank of the fitted coefficient, the …