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Minimax-optimal nonparametric regression in high dimensions
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Minimax-optimal nonparametric regression in high dimensions

Yun Yang
S. Tokdar
ArXiv (abs)PDFHTML

Papers citing "Minimax-optimal nonparametric regression in high dimensions"

17 / 17 papers shown
Title
Deep Horseshoe Gaussian Processes
Deep Horseshoe Gaussian Processes
Ismael Castillo
Thibault Randrianarisoa
BDLUQCV
108
5
0
04 Mar 2024
Estimating individual treatment effects under unobserved confounding using binary instruments
Estimating individual treatment effects under unobserved confounding using binary instruments
Dennis Frauen
Stefan Feuerriegel
CML
91
20
0
17 Aug 2022
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors
Yuling Jiao
Guohao Shen
Yuanyuan Lin
Jian Huang
123
52
0
14 Apr 2021
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
Alicia Curth
M. Schaar
CML
183
149
0
26 Jan 2021
Estimation and Inference with Trees and Forests in High Dimensions
Estimation and Inference with Trees and Forests in High Dimensions
Vasilis Syrgkanis
Manolis Zampetakis
57
32
0
07 Jul 2020
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
Taiji Suzuki
Atsushi Nitanda
93
63
0
28 Oct 2019
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery
Minshuo Chen
Haoming Jiang
Wenjing Liao
T. Zhao
157
92
0
05 Aug 2019
Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models
Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models
Zijian Guo
Wei Yuan
Cun-Hui Zhang
47
2
0
30 Jul 2019
Incremental Intervention Effects in Studies with Dropout and Many Timepoints
Incremental Intervention Effects in Studies with Dropout and Many Timepoints
Kwangho Kim
Edward H. Kennedy
A. Naimi
51
7
0
09 Jul 2019
A Bayesian Perspective of Statistical Machine Learning for Big Data
A Bayesian Perspective of Statistical Machine Learning for Big Data
R. Sambasivan
Sourish Das
S. Sahu
BDLGP
61
20
0
09 Nov 2018
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Ahmed Alaa
M. Schaar
72
89
0
20 Feb 2018
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
Ahmed Alaa
Mihaela van der Schaar
CML
73
43
0
24 Dec 2017
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
Rafael Izbicki
Ann B. Lee
195
78
0
26 Apr 2017
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood
Yun Yang
D. Pati
87
20
0
02 Jan 2017
Fast Gaussian Process Regression for Big Data
Fast Gaussian Process Regression for Big Data
Sourish Das
Sasanka Roy
R. Sambasivan
GP
113
48
0
17 Sep 2015
Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
Junwei Lu
Mladen Kolar
Han Liu
107
23
0
10 Mar 2015
Adaptive Bayesian Estimation of Conditional Densities
Adaptive Bayesian Estimation of Conditional Densities
Andriy Norets
D. Pati
118
36
0
22 Aug 2014