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1502.04071
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The generalized Lasso with non-linear observations
13 February 2015
Y. Plan
Roman Vershynin
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Papers citing
"The generalized Lasso with non-linear observations"
50 / 90 papers shown
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Beyond Discreteness: Finite-Sample Analysis of Straight-Through Estimator for Quantization
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Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
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A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
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Jonathan Scarlett
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25 Sep 2023
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models
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Learning linear dynamical systems under convex constraints
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Quantized Low-Rank Multivariate Regression with Random Dithering
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Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
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Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery
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Michael Kwok-Po Ng
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Improved Support Recovery in Universal One-bit Compressed Sensing
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Misspecified Phase Retrieval with Generative Priors
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Xinshao Wang
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254
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Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
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Non-Iterative Recovery from Nonlinear Observations using Generative Models
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Observable adjustments in single-index models for regularized M-estimators
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High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization
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On Model Selection Consistency of Lasso for High-Dimensional Ising Models
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T. Obuchi
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Support Recovery in Universal One-bit Compressed Sensing
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Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
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Subhro Ghosh
Jonathan Scarlett
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29 Jun 2021
Convergence guarantee for the sparse monotone single index model
Electronic Journal of Statistics (EJS), 2021
Ran Dai
Hyebin Song
Rina Foygel Barber
Garvesh Raskutti
262
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Asymmetric compressive learning guarantees with applications to quantized sketches
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Laurent Jacques
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Matey Neykov
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A theory of capacity and sparse neural encoding
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Roman Vershynin
214
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19 Feb 2021
Ising Model Selection Using
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Learning Deep ReLU Networks Is Fixed-Parameter Tractable
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Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models
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441
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345
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The Restricted Isometry of ReLU Networks: Generalization through Norm Concentration
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Gitta Kutyniok
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The Generalized Lasso with Nonlinear Observations and Generative Priors
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Jonathan Scarlett
358
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Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Hossein Taheri
Ramtin Pedarsani
Christos Thrampoulidis
334
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Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance
Jie Shen
Chicheng Zhang
440
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Non-Sparse PCA in High Dimensions via Cone Projected Power Iteration
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Matey Neykov
233
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Learning Polynomials of Few Relevant Dimensions
Annual Conference Computational Learning Theory (COLT), 2020
Sitan Chen
Raghu Meka
253
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Generic Error Bounds for the Generalized Lasso with Sub-Exponential Data
Sampling Theory, Signal Processing, and Data Analysis (TSPDA), 2020
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Christian Kipp
404
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11 Apr 2020
II. High Dimensional Estimation under Weak Moment Assumptions: Structured Recovery and Matrix Estimation
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Sharp Asymptotics and Optimal Performance for Inference in Binary Models
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Hossein Taheri
Ramtin Pedarsani
Christos Thrampoulidis
293
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High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework
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Jiarui Lu
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Hongzhe Li
231
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A Model of Double Descent for High-dimensional Binary Linear Classification
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Estimating covariance and precision matrices along subspaces
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