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 …
Estimation and group-feature selection in sparse mixture-of-experts with diverging number of parameters
Mixture-of-experts provide flexible statistical models for a wide range of regression
(supervised learning) problems. Often a large number of covariates (features) are available …
(supervised learning) problems. Often a large number of covariates (features) are available …
Rethinking Hard Thresholding Pursuit: Full Adaptation and Sharp Estimation
Hard Thresholding Pursuit (HTP) has aroused increasing attention for its robust theoretical
guarantees and impressive numerical performance in non-convex optimization. In this …
guarantees and impressive numerical performance in non-convex optimization. In this …
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 …
Statistics in the Modern Era: High Dimensions, Decision-Making, and Privacy
S Roy - 2024 - deepblue.lib.umich.edu
High dimensional data analysis has become increasingly frequent and important in diverse
fields of sciences, engineering, genomics, and machine learning (ML), and it has quite …
fields of sciences, engineering, genomics, and machine learning (ML), and it has quite …
[PDF][PDF] On the Computational Complexity of Private High-dimensional Model Selection
SRZWA Tewari - 2024 - researchgate.net
We consider the problem of model selection in a high-dimensional sparse linear regression
model under privacy constraints. We propose a differentially private best subset selection …
model under privacy constraints. We propose a differentially private best subset selection …
On the Computational Complexity of Private High-dimensional Model Selection
We consider the problem of model selection in a high-dimensional sparse linear regression
model under privacy constraints. We propose a differentially private (DP) best subset …
model under privacy constraints. We propose a differentially private (DP) best subset …