Ding-Xuan Zhou
23 papers · 2004–2025 · 6 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (14) π Interdisciplinary Bridge π§ Keyword Pioneer π£ Hot Topic Early Bird π Conference Polyglot (6)
π
Academic Marathon
(21)
π
Cross-Pollinator
(14)
π
Keyword Champion
(4)
π¬
Deep Specialist
(11)
π
Trend Setter
π₯
Unstoppable
(7)
ποΈ
Keyword Collector
(103)
π
Century Club
(23)
Conferences
JMLR (17)
NIPS (2)
AISTATS (1)
EMNLP (1)
ICML (1)
IJCAI (1)
Top co-authors
Keywords
reproducing kernel hilbert space
(6)
kernel methods
(4)
distributed learning
(4)
kernel ridge regression
(4)
online learning
(3)
generalization bound
(3)
convergence rate
(3)
deep neural network
(3)
learning rate
(3)
learning theory
(2)
stochastic gradient descent
(2)
binary classification
(2)
over-parameterized network
(2)
sparse learning
(2)
convex loss
(2)
rademacher complexity
(2)
early stopping
(2)
gaussian kernel
(2)
text classification
(1)
transfer learning
(1)
Papers
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
NIPS 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
Optimal Stochastic and Online Learning with Individual Iterates
NIPS 2019
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping
JMLR 2019
Distributed Semi-supervised Learning with Kernel Ridge Regression
JMLR 2017
Distributed Learning with Regularized Least Squares
JMLR 2017
Iterative Regularization for Learning with Convex Loss Functions
JMLR 2016
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes
JMLR 2016
Fast Convergence of Online Pairwise Learning Algorithms
AISTATS 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