Maximum Regularized Likelihood Estimators: A General Prediction Theory
and Applications

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Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications

9 October 2017

Johannes Lederer

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Papers citing "Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications"

9 / 9 papers shown

Title
Extremes in High Dimensions: Methods and Scalable Algorithms Johannes Lederer M. Oesting 48 10 0 07 Mar 2023
Statistical guarantees for sparse deep learning Johannes Lederer 40 11 0 11 Dec 2022
Optimization Landscapes of Wide Deep Neural Networks Are Benign Johannes Lederer 60 8 0 02 Oct 2020
Non-asymptotic Optimal Prediction Error for Growing-dimensional Partially Functional Linear Models Huiming Zhang Xiaoyu Lei 62 1 0 10 Sep 2020
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees Muhammad Osama Dave Zachariah Petre Stoica 3DPC 15 2 0 03 Jul 2020
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery M. Laszkiewicz Asja Fischer Johannes Lederer 74 6 0 01 May 2020
Prediction Error Bounds for Linear Regression With the TREX Jacob Bien Irina Gaynanova Johannes Lederer Christian L. Müller 63 18 0 04 Jan 2018
Inference for high-dimensional instrumental variables regression David Gold Johannes Lederer Jing Tao 85 36 0 18 Aug 2017
$Tuning parameter calibration for $\ell_1$-regularized logistic regression$ Tuning parameter calibration for $\ell_1$ -regularized logistic regression Wei Li Johannes Lederer 76 13 0 01 Oct 2016