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On the Prediction Performance of the Lasso
v1v2 (latest)

On the Prediction Performance of the Lasso

7 February 2014
A. Dalalyan
Mohamed Hebiri
Johannes Lederer
ArXiv (abs)PDFHTML

Papers citing "On the Prediction Performance of the Lasso"

50 / 50 papers shown
Title
Joint estimation of smooth graph signals from partial linear measurements
Joint estimation of smooth graph signals from partial linear measurements
Hemant Tyagi
44
0
0
29 May 2025
Statistical guarantees for sparse deep learning
Statistical guarantees for sparse deep learning
Johannes Lederer
40
11
0
11 Dec 2022
Variance estimation in graphs with the fused lasso
Variance estimation in graphs with the fused lasso
Oscar Hernan Madrid Padilla
170
5
0
26 Jul 2022
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust
  Designs
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs
Jonathan A. Kelner
Frederic Koehler
Raghu Meka
Dhruv Rohatgi
45
3
0
05 Mar 2022
Multivariate Trend Filtering for Lattice Data
Multivariate Trend Filtering for Lattice Data
Veeranjaneyulu Sadhanala
Yu Wang
Addison J. Hu
Robert Tibshirani
67
7
0
29 Dec 2021
Tensor denoising with trend filtering
Tensor denoising with trend filtering
Francesco Ortelli
Sara van de Geer
72
5
0
26 Jan 2021
Tree-Projected Gradient Descent for Estimating Gradient-Sparse
  Parameters on Graphs
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs
Sheng Xu
Z. Fan
S. Negahban
61
0
0
31 May 2020
Statistical Guarantees for Regularized Neural Networks
Statistical Guarantees for Regularized Neural Networks
Mahsa Taheri
Fang Xie
Johannes Lederer
113
39
0
30 May 2020
Bayesian Model Selection for Change Point Detection and Clustering
Bayesian Model Selection for Change Point Detection and Clustering
Othmane Mazhar
C. Rojas
Carlo Fischione
M. Hesamzadeh
82
4
0
03 Dec 2019
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded
  Variation Functions by Optimal Decision Trees
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees
S. Chatterjee
Subhajit Goswami
125
18
0
26 Nov 2019
ERM and RERM are optimal estimators for regression problems when
  malicious outliers corrupt the labels
ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels
Chinot Geoffrey
74
13
0
24 Oct 2019
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
R. Varma
Harlin Lee
J. Kovacevic
Yuejie Chi
102
33
0
29 May 2019
Iterative Alpha Expansion for estimating gradient-sparse signals from
  linear measurements
Iterative Alpha Expansion for estimating gradient-sparse signals from linear measurements
Sheng Xu
Z. Fan
46
6
0
15 May 2019
Estimating Piecewise Monotone Signals
Estimating Piecewise Monotone Signals
Kentaro Minami
52
10
0
06 May 2019
Multivariate extensions of isotonic regression and total variation
  denoising via entire monotonicity and Hardy-Krause variation
Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation
Billy Fang
Adityanand Guntuboyina
B. Sen
96
32
0
04 Mar 2019
Oracle inequalities for square root analysis estimators with application
  to total variation penalties
Oracle inequalities for square root analysis estimators with application to total variation penalties
Francesco Ortelli
Sara van de Geer
48
6
0
28 Feb 2019
Sparse Regression and Adaptive Feature Generation for the Discovery of
  Dynamical Systems
Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems
C. S. Kulkarni
83
10
0
07 Feb 2019
New Risk Bounds for 2D Total Variation Denoising
New Risk Bounds for 2D Total Variation Denoising
S. Chatterjee
Subhajit Goswami
91
21
0
04 Feb 2019
Prediction and estimation consistency of sparse multi-class penalized
  optimal scoring
Prediction and estimation consistency of sparse multi-class penalized optimal scoring
Irina Gaynanova
63
11
0
12 Sep 2018
Frame-constrained Total Variation Regularization for White Noise
  Regression
Frame-constrained Total Variation Regularization for White Noise Regression
Miguel del Álamo Ruiz
Housen Li
Axel Munk
87
13
0
05 Jul 2018
On the total variation regularized estimator over a class of tree graphs
On the total variation regularized estimator over a class of tree graphs
Francesco Ortelli
Sara van de Geer
47
19
0
04 Jun 2018
The noise barrier and the large signal bias of the Lasso and other
  convex estimators
The noise barrier and the large signal bias of the Lasso and other convex estimators
Pierre C. Bellec
63
18
0
04 Apr 2018
On tight bounds for the Lasso
On tight bounds for the Lasso
Sara van de Geer
55
18
0
03 Apr 2018
Rare Feature Selection in High Dimensions
Rare Feature Selection in High Dimensions
Xiaohan Yan
Jacob Bien
71
39
0
18 Mar 2018
Estimating the error variance in a high-dimensional linear model
Estimating the error variance in a high-dimensional linear model
Guo Yu
Jacob Bien
72
39
0
06 Dec 2017
Maximum Regularized Likelihood Estimators: A General Prediction Theory
  and Applications
Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications
Rui Zhuang
Johannes Lederer
73
15
0
09 Oct 2017
Inference for high-dimensional instrumental variables regression
Inference for high-dimensional instrumental variables regression
David Gold
Johannes Lederer
Jing Tao
89
36
0
18 Aug 2017
Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is
  low
Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low
Rahul Mazumder
P. Radchenko
Antoine Dedieu
439
59
0
10 Aug 2017
On Lasso refitting strategies
On Lasso refitting strategies
Evgenii Chzhen
Mohamed Hebiri
Joseph Salmon
101
19
0
17 Jul 2017
Binarsity: a penalization for one-hot encoded features in linear
  supervised learning
Binarsity: a penalization for one-hot encoded features in linear supervised learning
Mokhtar Z. Alaya
Simon Bussy
Stéphane Gaïffas
Agathe Guilloux
121
31
0
24 Mar 2017
Cross-Validation with Confidence
Cross-Validation with Confidence
Jing Lei
105
87
0
23 Mar 2017
Approximate $l_0$-penalized estimation of piecewise-constant signals on
  graphs
Approximate l0l_0l0​-penalized estimation of piecewise-constant signals on graphs
Z. Fan
Leying Guan
71
21
0
04 Mar 2017
Adaptive Risk Bounds in Univariate Total Variation Denoising and Trend
  Filtering
Adaptive Risk Bounds in Univariate Total Variation Denoising and Trend Filtering
Adityanand Guntuboyina
Donovan Lieu
S. Chatterjee
B. Sen
82
67
0
16 Feb 2017
On the Exponentially Weighted Aggregate with the Laplace Prior
On the Exponentially Weighted Aggregate with the Laplace Prior
A. Dalalyan
Edwin Grappin
Q. Paris
49
19
0
25 Nov 2016
Tuning parameter calibration for $\ell_1$-regularized logistic
  regression
Tuning parameter calibration for ℓ1\ell_1ℓ1​-regularized logistic regression
Wei Li
Johannes Lederer
90
13
0
01 Oct 2016
Balancing Statistical and Computational Precision: A General Theory and
  Applications to Sparse Regression
Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression
Mahsa Taheri
Néhémy Lim
Johannes Lederer
59
4
0
23 Sep 2016
Bounds on the prediction error of penalized least squares estimators
  with convex penalty
Bounds on the prediction error of penalized least squares estimators with convex penalty
Pierre C. Bellec
Alexandre B. Tsybakov
129
43
0
21 Sep 2016
The DFS Fused Lasso: Linear-Time Denoising over General Graphs
The DFS Fused Lasso: Linear-Time Denoising over General Graphs
Oscar Hernan Madrid Padilla
James G. Scott
James Sharpnack
Robert Tibshirani
FedML
117
67
0
11 Aug 2016
Oracle Inequalities for High-dimensional Prediction
Oracle Inequalities for High-dimensional Prediction
Johannes Lederer
Lu Yu
Irina Gaynanova
92
24
0
01 Aug 2016
Approximate Recovery in Changepoint Problems, from $\ell_2$ Estimation
  Error Rates
Approximate Recovery in Changepoint Problems, from ℓ2\ell_2ℓ2​ Estimation Error Rates
Kevin Lin
James Sharpnack
Alessandro Rinaldo
Robert Tibshirani
71
27
0
21 Jun 2016
Slope meets Lasso: improved oracle bounds and optimality
Slope meets Lasso: improved oracle bounds and optimality
Pierre C. Bellec
Guillaume Lecué
Alexandre B. Tsybakov
326
192
0
27 May 2016
Optimal rates for total variation denoising
Optimal rates for total variation denoising
Jan-Christian Hütter
Philippe Rigollet
81
33
0
30 Mar 2016
Regression modeling on stratified data with the lasso
Regression modeling on stratified data with the lasso
E. Ollier
V. Viallon
59
32
0
22 Aug 2015
Sharp oracle bounds for monotone and convex regression through
  aggregation
Sharp oracle bounds for monotone and convex regression through aggregation
Pierre C. Bellec
Alexandre B. Tsybakov
101
35
0
29 Jun 2015
Optimal prediction for sparse linear models? Lower bounds for
  coordinate-separable M-estimators
Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators
Yuchen Zhang
Martin J. Wainwright
Michael I. Jordan
172
43
0
11 Mar 2015
Optimal Two-Step Prediction in Regression
Optimal Two-Step Prediction in Regression
Didier Chételat
Johannes Lederer
Joseph Salmon
120
19
0
18 Oct 2014
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality
  Guarantees
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
M. Chichignoud
Johannes Lederer
Martin J. Wainwright
100
13
0
01 Oct 2014
Sparse Partially Linear Additive Models
Sparse Partially Linear Additive Models
Yin Lou
Jacob Bien
R. Caruana
J. Gehrke
118
58
0
17 Jul 2014
A Permutation Approach for Selecting the Penalty Parameter in Penalized
  Model Selection
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
Jeremy A. Sabourin
W. Valdar
A. Nobel
124
35
0
08 Apr 2014
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High
  Dimensions With the TREX
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX
Johannes Lederer
Christian L. Müller
99
55
0
02 Apr 2014
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