Nonparametric Regression

Nonparametric regression aims to estimate an unknown function from data without making strong assumptions about its form, focusing on achieving optimal prediction accuracy and understanding the underlying function's properties. Current research emphasizes developing efficient algorithms, including those based on kernel methods, neural networks (especially deep and shallow ReLU networks), and tensor neural networks, often incorporating techniques like regularization, adversarial training, and active learning to improve performance and generalization. These advancements are significant for various fields, enabling more accurate modeling of complex relationships in data and facilitating robust statistical inference in scenarios where parametric assumptions are unrealistic or inappropriate.

Papers

November 18, 2024

Debiased Regression for Root-N-Consistent Conditional Mean Estimation
Masahiro Kato
Novel Regression Nonparametric Regression Conditional Mean

November 14, 2024

Conditional regression for the Nonlinear Single-Variable Model
Yantao Wu, Mauro Maggioni
Nonlinear Model Nonparametric Regression Conditional Model Single Index Model

November 12, 2024

Doubly Robust Regression Discontinuity Designs
Masahiro Kato
Treatment Effect Nonparametric Regression Doubly Robust Novel Estimator Discontinuity Discrimination

November 5, 2024

Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression: A Distribution-Free Analysis
Yingzhen Yang, Ping Li
Neural Network Strong Generalization Gradient Descent Kernel Regression Nonparametric Regression Distribution Free

October 17, 2024

Ab initio nonparametric variable selection for scalable Symbolic Regression with large $p$
Shengbin Ye, Meng Li
High Dimensional Novel Regression Symbolic Regression Variable Selection Nonparametric Regression Ab Initio Non Parametric Learning Symbolic Approach

October 8, 2024

Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression
Weigutian Ou, Helmut Bölcskei
Financial Application Deep Network Average Approximation ReLU Network Sparse Network Function Approximation Nonparametric Regression Deep ReLU Network Better Approximation

October 3, 2024

September 6, 2024

Over-parameterized regression methods and their application to semi-supervised learning
Katsuyuki Hagiwara
Application Proficiency Semi Supervised Learning Least Square Regression Model Singular Value Decomposition Ridge Regression Nonparametric Regression

September 3, 2024

Deep non-parametric logistic model with case-control data and external summary information
Hengchao Shi, Ming Zheng, Wen Yu
Estimation Task Nonparametric Regression Case Control

July 25, 2024

Amortized Active Learning for Nonparametric Functions
Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer
Active Learning Nonparametric Regression Active Causal Functional Prior Active SEt

July 16, 2024

Preconditioned Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression
Yingzhen Yang
Neural Network Gradient Descent Nonparametric Regression Preconditioned Gradient Descent

June 14, 2024

An Efficient Approach to Regression Problems with Tensor Neural Networks
Yongxin Li, Yifan Wang, Zhongshuo Lin, Hehu Xie
Neural Network Related Problem Radial Basis Function Efficient Approach Nonparametric Regression Regression Function

June 10, 2024

May 22, 2024

Locally Private Estimation with Public Features
Yuheng Ma, Ke Jia, Hanfang Yang
Local Differential Privacy Nonparametric Regression Private Estimation Private Feature

May 21, 2024

Model Free Prediction with Uncertainty Assessment
Yuling Jiao, Lican Kang, Jin Liu, Heng Peng, Heng Zuo
Deep Learning Uncertainty Estimation Conditional Diffusion Model Nonparametric Regression Confidence Region Conditional Mean

January 22, 2024

December 15, 2023

Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood
Weisong Yang, Rafael Poyiadzi, Niall Twomey, Raul Santos Rodriguez
Hypothesis Testing Nonparametric Regression Class Conditional Instance Level Label

Nonparametric Regression

Papers

Debiased Regression for Root-N-Consistent Conditional Mean Estimation

Conditional regression for the Nonlinear Single-Variable Model

Doubly Robust Regression Discontinuity Designs

Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression: A Distribution-Free Analysis

Ab initio nonparametric variable selection for scalable Symbolic Regression with large $p$

Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression

Highly Adaptive Ridge

Nonuniform random feature models using derivative information

Over-parameterized regression methods and their application to semi-supervised learning

Deep non-parametric logistic model with case-control data and external summary information

Amortized Active Learning for Nonparametric Functions

Preconditioned Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression

An Efficient Approach to Regression Problems with Tensor Neural Networks

Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes

Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints

Locally Private Estimation with Public Features

Model Free Prediction with Uncertainty Assessment

Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure

Nonparametric Estimation via Variance-Reduced Sketching

Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood