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Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic
  Error Bounds with Polynomial Prefactors
v1v2v3v4v5v6 (latest)

Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors

Annals of Statistics (Ann. Stat.), 2021
14 April 2021
Yuling Jiao
Guohao Shen
Yuanyuan Lin
Jian Huang
ArXiv (abs)PDFHTML

Papers citing "Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors"

41 / 41 papers shown
Discrete State Diffusion Models: A Sample Complexity Perspective
Discrete State Diffusion Models: A Sample Complexity Perspective
Aadithya Srikanth
Mudit Gaur
Vaneet Aggarwal
DiffM
128
1
0
12 Oct 2025
Error Analysis of Discrete Flow with Generator Matching
Error Analysis of Discrete Flow with Generator Matching
Zhengyan Wan
Yidong Ouyang
Qiang Yao
Liyan Xie
Fang Fang
Hongyuan Zha
Guang Cheng
142
2
0
26 Sep 2025
A Conditional Distribution Equality Testing Framework using Deep Generative Learning
A Conditional Distribution Equality Testing Framework using Deep Generative Learning
Siming Zheng
Meifang Lan
Tong Wang
Yuanyuan Lin
233
0
0
22 Sep 2025
Calibration Prediction Interval for Non-parametric Regression and Neural Networks
Calibration Prediction Interval for Non-parametric Regression and Neural Networks
Kejin Wu
D. Politis
118
0
0
02 Sep 2025
Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks
Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks
Chien-Ming Chi
216
0
0
07 Jul 2025
Wasserstein Distributionally Robust Nonparametric Regression
Wasserstein Distributionally Robust Nonparametric Regression
Changyu Liu
Yuling Jiao
Junhui Wang
Jian Huang
OOD
307
1
0
12 May 2025
Learning Guarantee of Reward Modeling Using Deep Neural Networks
Learning Guarantee of Reward Modeling Using Deep Neural Networks
Yuanhang Luo
Yeheng Ge
Ruijian Han
Guohao Shen
243
2
0
10 May 2025
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
Jialong Jiang
Wenkang Hu
Jian Huang
Yuling Jiao
Xu Liu
DiffM
264
0
0
08 May 2025
Deep learning with missing data
Deep learning with missing data
Tianyi Ma
Tengyao Wang
R. Samworth
536
3
0
21 Apr 2025
Approximation Bounds for Transformer Networks with Application to Regression
Approximation Bounds for Transformer Networks with Application to Regression
Yuling Jiao
Yanming Lai
Defeng Sun
Yang Wang
Bokai Yan
462
1
0
16 Apr 2025
Inference for Deep Neural Network Estimators in Generalized Nonparametric Models
Inference for Deep Neural Network Estimators in Generalized Nonparametric Models
Xuran Meng
Yi Li
BDL
264
0
0
12 Apr 2025
Fair Sufficient Representation Learning
Fair Sufficient Representation Learning
Xueyu Zhou
Chun Yin IP
Jian Huang
FaML
230
0
0
29 Mar 2025
The Exploration of Error Bounds in Classification with Noisy Labels
The Exploration of Error Bounds in Classification with Noisy Labels
Haixia Liu
Boxiao Li
Can Yang
Yang Wang
NoLa
393
0
0
25 Jan 2025
Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression
Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression
Yingzhen Yang
Ping Li
MLT
598
1
0
05 Nov 2024
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier
William Kengne
373
3
0
29 Oct 2024
Deep Nonparametric Inference for Conditional Hazard Function
Deep Nonparametric Inference for Conditional Hazard Function
Wen Su
Kin-Yat Liu
Guosheng Yin
Jian Huang
Xingqiu Zhao
CML
230
1
0
23 Oct 2024
Deep Transfer Learning: Model Framework and Error Analysis
Deep Transfer Learning: Model Framework and Error Analysis
Yuling Jiao
Huazhen Lin
Yuchen Luo
Jerry Zhijian Yang
434
2
0
12 Oct 2024
Breaking the curse of dimensionality in structured density estimation
Breaking the curse of dimensionality in structured density estimationNeural Information Processing Systems (NeurIPS), 2024
Robert A. Vandermeulen
Wai Ming Tai
Bryon Aragam
238
3
0
10 Oct 2024
Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive
  for Conditional Distribution Estimation
Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution EstimationInternational Conference on Learning Representations (ICLR), 2024
Rong Tang
Lizhen Lin
Yun Yang
DiffM
208
4
0
30 Sep 2024
Approximation Bounds for Recurrent Neural Networks with Application to Regression
Approximation Bounds for Recurrent Neural Networks with Application to Regression
Yuling Jiao
Yang Wang
Bokai Yan
256
1
0
09 Sep 2024
Deep non-parametric logistic model with case-control data and external
  summary information
Deep non-parametric logistic model with case-control data and external summary information
Hengchao Shi
M. Zheng
Wen Yu
157
0
0
03 Sep 2024
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networksApplied and Computational Harmonic Analysis (ACHA), 2024
Yunfei Yang
402
5
0
02 Sep 2024
Deep Limit Model-free Prediction in Regression
Deep Limit Model-free Prediction in Regression
Kejin Wu
D. Politis
OOD
288
1
0
18 Aug 2024
Causal inference through multi-stage learning and doubly robust deep
  neural networks
Causal inference through multi-stage learning and doubly robust deep neural networks
Yuqian Zhang
Jelena Bradic
OODCML
244
2
0
11 Jul 2024
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
William Kengne
Modou Wade
272
2
0
12 Jun 2024
Posterior and variational inference for deep neural networks with heavy-tailed weights
Posterior and variational inference for deep neural networks with heavy-tailed weights
Ismael Castillo
Paul Egels
BDL
346
10
0
05 Jun 2024
On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point
  Processes
On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes
Zhiheng Chen
Guanhua Fang
Wen Yu
228
2
0
02 Jun 2024
Model Free Prediction with Uncertainty Assessment
Model Free Prediction with Uncertainty Assessment
Yuling Jiao
Lican Kang
Jin Liu
Heng Peng
Heng Zuo
DiffM
365
2
0
21 May 2024
Robust deep learning from weakly dependent data
Robust deep learning from weakly dependent dataNeural Networks (NN), 2024
William Kengne
Modou Wade
OOD
218
4
0
08 May 2024
Generative adversarial learning with optimal input dimension and its
  adaptive generator architecture
Generative adversarial learning with optimal input dimension and its adaptive generator architecture
Zhiyao Tan
Ling Zhou
Huazhen Lin
GAN
237
3
0
06 May 2024
On the rates of convergence for learning with convolutional neural networks
On the rates of convergence for learning with convolutional neural networks
Yunfei Yang
Han Feng
Ding-Xuan Zhou
410
4
0
25 Mar 2024
Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
Zhao Ding
Chenguang Duan
Yuling Jiao
Jerry Zhijian Yang
196
1
0
09 Jan 2024
Conditional Stochastic Interpolation for Generative Learning
Conditional Stochastic Interpolation for Generative Learning
Ding Huang
Jian Huang
Ting Li
Guohao Shen
BDLDiffM
341
6
0
09 Dec 2023
Solving PDEs on Spheres with Physics-Informed Convolutional Neural
  Networks
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
Guanhang Lei
Zhen Lei
Lei Shi
Chenyu Zeng
Ding-Xuan Zhou
186
5
0
18 Aug 2023
Wasserstein Generative Regression
Wasserstein Generative Regression
Shanshan Song
Tong Wang
Guohao Shen
Yuanyuan Lin
Jian Huang
DiffMGAN
208
4
0
27 Jun 2023
Nonparametric regression using over-parameterized shallow ReLU neural
  networks
Nonparametric regression using over-parameterized shallow ReLU neural networksJournal of machine learning research (JMLR), 2023
Yunfei Yang
Ding-Xuan Zhou
353
15
0
14 Jun 2023
Exploring the Complexity of Deep Neural Networks through Functional
  Equivalence
Exploring the Complexity of Deep Neural Networks through Functional EquivalenceInternational Conference on Machine Learning (ICML), 2023
Guohao Shen
419
6
0
19 May 2023
Deep neural network approximation of composite functions without the
  curse of dimensionality
Deep neural network approximation of composite functions without the curse of dimensionality
Adrian Riekert
170
1
0
12 Apr 2023
Semiparametric efficient estimation of genetic relatedness with machine
  learning methods
Semiparametric efficient estimation of genetic relatedness with machine learning methodsStatistics and computing (Stat. Comput.), 2023
Xu Guo
Yiyuan Qian
Hongwei Shi
Weichao Yang
Niwen Zhou
248
1
0
04 Apr 2023
Rates of convergence for nonparametric estimation of singular
  distributions using generative adversarial networks
Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networksJournal of the Korean Statistical Society (JKSS), 2022
Minwoo Chae
GAN
285
5
0
07 Feb 2022
Deep Dimension Reduction for Supervised Representation Learning
Deep Dimension Reduction for Supervised Representation LearningIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020
Jian Huang
Yuling Jiao
Xu Liao
Jin Liu
Zhou Yu
DRL
190
21
0
10 Jun 2020
1
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