Using GIS-based order weight average (OWA) methods to predict suitable locations for the artificial recharge of groundwater
M Mokarram, S Negahban, A Abdolali… - Environmental Earth …, 2021 - Springer
This study aims to determine suitable locations for artificial recharge of groundwater (ARG)
using the GIS-based analytic hierarchy process (AHP) and order weight average (OWA). To …
using the GIS-based analytic hierarchy process (AHP) and order weight average (OWA). To …
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
We consider the problem of learning a sparse graph underlying an undirected Gaussian
graphical model, a key problem in statistical machine learning. Given $ n $ samples from a …
graphical model, a key problem in statistical machine learning. Given $ n $ samples from a …
On the computational complexity of private high-dimensional model selection via the exponential mechanism
We consider the problem of model selection in a high-dimensional sparse linear regression
model under the differential privacy framework. In particular, we consider the problem of …
model under the differential privacy framework. In particular, we consider the problem of …
Sparse PCA: A new scalable estimator based on integer programming
K Behdin, R Mazumder - arXiv preprint arXiv:2109.11142, 2021 - arxiv.org
We consider the Sparse Principal Component Analysis (SPCA) problem under the well-
known spiked covariance model. Recent work has shown that the SPCA problem can be …
known spiked covariance model. Recent work has shown that the SPCA problem can be …
Group Selection and Shrinkage: Structured Sparsity for Semiparametric Additive Models
R Thompson, F Vahid - Journal of Computational and Graphical …, 2024 - Taylor & Francis
Sparse regression and classification estimators that respect group structures have
application to an assortment of statistical and machine learning problems, from multitask …
application to an assortment of statistical and machine learning problems, from multitask …
Supervised homogeneity fusion: a combinatorial approach
Supervised homogeneity fusion: A combinatorial approach Page 1 The Annals of Statistics
2024, Vol. 52, No. 1, 285–310 https://doi.org/10.1214/23-AOS2347 © Institute of …
2024, Vol. 52, No. 1, 285–310 https://doi.org/10.1214/23-AOS2347 © Institute of …
High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective
We study the problem of exact support recovery for high-dimensional sparse linear
regression under independent Gaussian design when the signals are weak, rare, and …
regression under independent Gaussian design when the signals are weak, rare, and …
[PDF][PDF] Direction Identification and Minimax Estimation by Generalized Eigenvalue Problem in High Dimensional Sparse Regression
M Sauvenier, S Van Bellegem - 2023 - dial.uclouvain.be
In high-dimensional sparse linear regression, the selection and the estimation of the
parameters are studied based on an l0− constraint on the direction of the vector of …
parameters are studied based on an l0− constraint on the direction of the vector of …
Understanding Best Subset Selection: A Tale of Two C (omplex) ities
For decades, best subset selection (BSS) has eluded statisticians mainly due to its
computational bottleneck. However, until recently, modern computational breakthroughs …
computational bottleneck. However, until recently, modern computational breakthroughs …
[PDF][PDF] Differentially Private Best Subset Selection Via Integer Programming
K Behdin, P Prastakos… - Privacy Regulation and …, 2024 - pml-workshop.github.io
Differentially Private Best Subset Selection Via Integer Programming Page 1 Differentially
Private Best Subset Selection Via Integer Programming Kayhan Behdin, Peter Prastakos …
Private Best Subset Selection Via Integer Programming Kayhan Behdin, Peter Prastakos …