Ding-Xuan Zhou
24 papers · 2004–2026 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (34) π Academic Marathon Β· Ultra π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
π§
Keyword Pioneer
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Deep Specialist
(12)
ποΈ
Keyword Collector
(105)
π
Trend Setter
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Century Club Β· Bronze
π₯
Unstoppable Β· Bronze
Top co-authors
Research topics
Machine Learning
(24)
Optimization & Theory Β· Machine Learning
(21)
Core Methods
(16)
Deep Learning
(6)
Mathematics & Optimization
(6)
Optimization
(6)
Learning Types Β· Machine Learning
(5)
Architectures
(3)
Learning Paradigms
(3)
Learning Types Β· Deep Learning
(3)
Application Areas
(2)
Optimization & Theory Β· Deep Learning
(2)
Natural Language Processing
(1)
Applications
(1)
Keywords
reproducing kernel hilbert space
(6)
distributed learning
(4)
generalization bound
(4)
kernel methods
(4)
kernel ridge regression
(4)
deep neural network
(3)
online learning
(3)
convergence rate
(3)
rademacher complexity
(3)
learning rate
(3)
overparameterized network
(2)
stochastic gradient descent
(2)
learning theory
(2)
sparse learning
(2)
binary classification
(2)
relu network
(2)
early stopping
(2)
convex loss
(2)
gaussian kernel
(2)
neural tangent kernel
(2)
Papers
Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width
JMLR 2026
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
JMLR 2025
Nonlinear functional regression by functional deep neural network with kernel embedding
JMLR 2025
Classification with Deep Neural Networks and Logistic Loss
JMLR 2024
Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks
JMLR 2024
Generalization Analysis for Contrastive Representation Learning
ICML 2023
Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression
EMNLP 2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods
NeurIPS 2022
On ADMM in Deep Learning: Convergence and Saturation-Avoidance
JMLR 2021
Towards Understanding the Spectral Bias of Deep Learning
IJCAI 2021
Distributed Kernel Ridge Regression with Communications
JMLR 2020
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping
JMLR 2019
Optimal Stochastic and Online Learning with Individual Iterates
NeurIPS 2019
Distributed Learning with Regularized Least Squares
JMLR 2017
Distributed Semi-supervised Learning with Kernel Ridge Regression
JMLR 2017
Fast Convergence of Online Pairwise Learning Algorithms
AISTATS 2016
Iterative Regularization for Learning with Convex Loss Functions
JMLR 2016
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes
JMLR 2016
Learning Theory of Randomized Kaczmarz Algorithm
JMLR 2015
Classification with Gaussians and Convex Loss
JMLR 2009
Online Learning with Samples Drawn from Non-identical Distributions
JMLR 2009
Learnability of Gaussians with Flexible Variances
JMLR 2007
Learning Coordinate Covariances via Gradients
JMLR 2006
Support Vector Machine Soft Margin Classifiers: Error Analysis
JMLR 2004