De-biasing the Lasso: Optimal Sample Size for Gaussian Designs

11 August 2015

Papers citing "De-biasing the Lasso: Optimal Sample Size for Gaussian Designs"

22 / 22 papers shown

Title
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions Tianming Wang Ke Wei 33 1 0 28 Jul 2024
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning Frederik Hoppe C. M. Verdun Hannah Laus Felix Krahmer Holger Rauhut UQCV 29 1 0 18 Jul 2024
Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects Masahiro Kato CML 39 1 0 05 Mar 2024
Inference on Optimal Dynamic Policies via Softmax Approximation Qizhao Chen Morgane Austern Vasilis Syrgkanis OffRL 29 1 0 08 Mar 2023
Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators Yuval Oren Saharon Rosset 21 1 0 19 Feb 2023
Distributed Sparse Linear Regression under Communication Constraints R. Fonseca B. Nadler FedML 19 2 0 09 Jan 2023
Uncertainty quantification for sparse Fourier recovery F. Hoppe Felix Krahmer C. M. Verdun Marion I. Menzel Holger Rauhut 29 7 0 30 Dec 2022
Simultaneous Inference in Non-Sparse High-Dimensional Linear Models Yanmei Shi Zhiruo Li Q. Zhang 23 0 0 17 Oct 2022
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R Philipp Bach Victor Chernozhukov Malte S. Kurz Martin Spindler Jan Rabenseifner GP 28 33 0 17 Mar 2021
Spectral Methods for Data Science: A Statistical Perspective Yuxin Chen Yuejie Chi Jianqing Fan Cong Ma 40 165 0 15 Dec 2020
The Lasso with general Gaussian designs with applications to hypothesis testing Michael Celentano Andrea Montanari Yuting Wei 42 63 0 27 Jul 2020
The estimation error of general first order methods Michael Celentano Andrea Montanari Yuchen Wu 14 44 0 28 Feb 2020
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation Rungang Han Rebecca Willett Anru R. Zhang 27 65 0 26 Feb 2020
Minimax Semiparametric Learning With Approximate Sparsity Jelena Bradic Victor Chernozhukov Whitney Newey Yinchu Zhu 50 21 0 27 Dec 2019
De-biasing convex regularized estimators and interval estimation in linear models Pierre C. Bellec Cun-Hui Zhang 27 20 0 26 Dec 2019
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis Y. Deshpande Adel Javanmard M. Mehrabi AI4TS 34 31 0 04 Nov 2019
On rank estimators in increasing dimensions Yanqin Fan Fang Han Wei Li Xiao‐Hua Zhou 25 16 0 14 Aug 2019
Automatic Debiased Machine Learning of Causal and Structural Effects Victor Chernozhukov Whitney Newey Rahul Singh CML AI4CE 24 103 0 14 Sep 2018
High-dimensional regression adjustments in randomized experiments Stefan Wager Wenfei Du Jonathan E. Taylor Robert Tibshirani 40 117 0 22 Jul 2016
Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions Susan Athey Guido Imbens Stefan Wager CML 35 387 0 25 Apr 2016
SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax Weijie Su Emmanuel Candes 65 145 0 29 Mar 2015
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory Adel Javanmard Andrea Montanari 112 160 0 17 Jan 2013