Ding-Xuan Zhou

18 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0003-0224-9216

University of Sydney

Publications

• Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width — Journal of Machine Learning Research 2026 (name-matched, not ORCID-linked)
• Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos — Journal of Machine Learning Research 2025 (name-matched, not ORCID-linked)
• Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks — Journal of Machine Learning Research 2024 (name-matched, not ORCID-linked)
• Classification with Deep Neural Networks and Logistic Loss — Journal of Machine Learning Research 2024 (name-matched, not ORCID-linked)
• On ADMM in Deep Learning: Convergence and Saturation-Avoidance — Journal of Machine Learning Research 2021 (name-matched, not ORCID-linked)
• Distributed Kernel Ridge Regression with Communications — Journal of Machine Learning Research 2020 (name-matched, not ORCID-linked)
• Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping — Journal of Machine Learning Research 2019 (name-matched, not ORCID-linked)
• Distributed Learning with Regularized Least Squares — Journal of Machine Learning Research 2017 (name-matched, not ORCID-linked)
• Distributed Semi-supervised Learning with Kernel Ridge Regression — Journal of Machine Learning Research 2017 (name-matched, not ORCID-linked)
• Minimax optimal rates of estimation in high dimensional additive models — The Annals of Statistics 2016
• Iterative Regularization for Learning with Convex Loss Functions — Journal of Machine Learning Research 2016 (name-matched, not ORCID-linked)
• Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes — Journal of Machine Learning Research 2016 (name-matched, not ORCID-linked)
• Learning Theory of Randomized Kaczmarz Algorithm — Journal of Machine Learning Research 2015
• Classification with Gaussians and Convex Loss — Journal of Machine Learning Research 2009
• Online Learning with Samples Drawn from Non-identical Distributions — Journal of Machine Learning Research 2009
• Learnability of Gaussians with Flexible Variances — Journal of Machine Learning Research 2007
• Learning Coordinate Covariances via Gradients — Journal of Machine Learning Research 2006
• Support Vector Machine Soft Margin Classifiers: Error Analysis — Journal of Machine Learning Research 2004