← Authors

Ding-Xuan Zhou

23 papers · 2004–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (21) 🐝 Cross-Pollinator (14) πŸ† Keyword Champion (4) πŸ”¬ Deep Specialist (11) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (7) πŸ—ƒοΈ Keyword Collector (103) πŸ’Ž Century Club (23)

Conferences

JMLR (17) NIPS (2) AISTATS (1) EMNLP (1) ICML (1) IJCAI (1)

Top co-authors

Shao-Bo Lin (6) Yiming Ying (5) Yunwen Lei (4) Junhong Lin (2) Jinshan Zeng (2) Xin Guo (2) Jun Fan (2) Di Wang (2) Lorenzo Rosasco (1) Ke Tang (1)

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