Approximation and estimation bounds for free knot splines. - Yunwen Lei; Lixin Ding (Comput. Math. Appl., 2013)

Universal learning using free multivariate splines. - Yunwen Lei; Lixin Ding; Weili Wu 001 (Neurocomputing, 2013)

Refined Rademacher Chaos Complexity Bounds with Applications to the Multikernel Learning Problem. - Yunwen Lei; Lixin Ding (Neural Comput., 2014)

Generalization ability of fractional polynomial models. - Yunwen Lei; Lixin Ding; Yiming Ding (Neural Networks, 2014)

Theory and Algorithms for the Localized Setting of Learning Kernels. - Yunwen Lei; Alexander Binder; Ürün Dogan; Marius Kloft (FE@NIPS, 2015)

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms. - Yunwen Lei; Ürün Dogan; Alexander Binder; Marius Kloft (NIPS, 2015)

Generalization Performance of Radial Basis Function Networks. - Yunwen Lei; Lixin Ding; Wensheng Zhang (IEEE Trans. Neural Networks Learn. Syst., 2015)

Localized Multiple Kernel Learning - A Convex Approach. - Yunwen Lei; Alexander Binder; Ürün Dogan; Marius Kloft (ACML, 2016)

Local Rademacher complexity bounds based on covering numbers. - Yunwen Lei; Lixin Ding; Yingzhou Bi (Neurocomputing, 2016)

Generalization Error Bounds for Extreme Multi-class Classification. - Yunwen Lei; Ürün Dogan; Ding-Xuan Zhou; Marius Kloft (CoRR, 2017)

Online pairwise learning algorithms with convex loss functions. - Junhong Lin; Yunwen Lei; Bo Zhang; Ding-Xuan Zhou (Inf. Sci., 2017)

Convergence of Unregularized Online Learning Algorithms. - Yunwen Lei; Lei Shi 010; Zheng-Chu Guo (J. Mach. Learn. Res., 2017)

Analysis of Online Composite Mirror Descent Algorithm. - Yunwen Lei; Ding-Xuan Zhou (Neural Comput., 2017)

An Enhanced Firefly Algorithm with Orthogonal Centroid Opposition-Based Learning. - Lingyun Zhou; Lixin Ding; Yunwen Lei (CEC, 2018)

Generalization Bounds for Regularized Pairwise Learning. - Yunwen Lei; Shao-Bo Lin; Ke Tang 001 (IJCAI, 2018)

Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities. - Yunwen Lei; Ke Tang 001 (NeurIPS, 2018)

Convergence of Online Mirror Descent Algorithms. - Yunwen Lei; Ding-Xuan Zhou (CoRR, 2018)

Refined bounds for online pairwise learning algorithms. - Xiaming Chen; Yunwen Lei (Neurocomputing, 2018)

Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning. - Niloofar Yousefi 001; Yunwen Lei; Marius Kloft; Mansooreh Mollaghasemi; Georgios C. Anagnostopoulos (J. Mach. Learn. Res., 2018)

Learning Theory of Randomized Sparse Kaczmarz Method. - Yunwen Lei; Ding-Xuan Zhou (SIAM J. Imaging Sci., 2018)

Optimal Stochastic and Online Learning with Individual Iterates. - Yunwen Lei; Peng Yang 008; Ke Tang 001; Ding-Xuan Zhou (NeurIPS, 2019)

Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions. - Yunwen Lei; Ting Hu 002; Ke Tang 001 (CoRR, 2019)

A Generalization Error Bound for Multi-class Domain Generalization. - Aniket Anand Deshmukh; Yunwen Lei; Srinagesh Sharma; Ürün Dogan; James W. Cutler; Clayton Scott (CoRR, 2019)

Improved Generalisation Bounds for Deep Learning Through L∞ Covering Numbers. - Antoine Ledent; Yunwen Lei; Marius Kloft (CoRR, 2019)

Convergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms. - Ming Chen; Yunwen Lei; Lixin Ding; Zhao Tong 001 (Int. J. Pattern Recognit. Artif. Intell., 2019)

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. - Shao-Bo Lin; Yunwen Lei; Ding-Xuan Zhou (J. Mach. Learn. Res., 2019)

Data-Dependent Generalization Bounds for Multi-Class Classification. - Yunwen Lei; Ürün Dogan; Ding-Xuan Zhou; Marius Kloft (IEEE Trans. Inf. Theory, 2019)

On Performance Estimation in Automatic Algorithm Configuration. - Shengcai Liu; Ke Tang 001; Yunwen Lei; Xin Yao 001 (AAAI, 2020)

Stochastic Hard Thresholding Algorithms for AUC Maximization. - Zhenhuan Yang; Baojian Zhou; Yunwen Lei; Yiming Ying (ICDM, 2020)

Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent. - Yunwen Lei; Yiming Ying (ICML, 2020)

Sharper Generalization Bounds for Pairwise Learning. - Yunwen Lei; Antoine Ledent; Marius Kloft (NeurIPS, 2020)

Design of a novel low voltage cell lysing instrument. - Feng Jiang; Mao Li; Yunwen Lei; Jun Yu (J. Comput. Methods Sci. Eng., 2020)

Stochastic Gradient Descent for Nonconvex Learning Without Bounded Gradient Assumptions. - Yunwen Lei; Ting Hu 002; Guiying Li; Ke Tang 001 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks. - Antoine Ledent; Waleed Mustafa; Yunwen Lei; Marius Kloft (AAAI, 2021)

Fine-grained Generalization Analysis of Vector-Valued Learning. - Liang Wu 015; Antoine Ledent; Yunwen Lei; Marius Kloft (AAAI, 2021)

Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss. - Zhenhuan Yang; Yunwen Lei; Siwei Lyu; Yiming Ying (AISTATS, 2021)

Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions. - Yunwen Lei; Yiming Ying (ICLR, 2021)

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. - Yunwen Lei; Zhenhuan Yang; Tianbao Yang; Yiming Ying (ICML, 2021)

Fine-grained Generalization Analysis of Structured Output Prediction. - Waleed Mustafa; Yunwen Lei; Antoine Ledent; Marius Kloft (IJCAI, 2021)

Learning Interpretable Concept Groups in CNNs. - Saurabh Varshneya; Antoine Ledent; Robert A. Vandermeulen; Yunwen Lei; Matthias Enders; Damian Borth; Marius Kloft (IJCAI, 2021)

Stability and Generalization for Randomized Coordinate Descent. - Puyu Wang; Liang Wu 015; Yunwen Lei (IJCAI, 2021)

Fine-grained Generalization Analysis of Inductive Matrix Completion. - Antoine Ledent; Rodrigo Alves; Yunwen Lei; Marius Kloft (NeurIPS, 2021)

Generalization Guarantee of SGD for Pairwise Learning. - Yunwen Lei; Mingrui Liu; Yiming Ying (NeurIPS, 2021)

Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. - Zhenhuan Yang; Yunwen Lei; Puyu Wang; Tianbao Yang; Yiming Ying (NeurIPS, 2021)

Differentially Private SGD with Non-Smooth Loss. - Puyu Wang; Yunwen Lei; Yiming Ying; Hai Zhang 001 (CoRR, 2021)

Differentially private empirical risk minimization for AUC maximization. - Puyu Wang; Zhenhuan Yang; Yunwen Lei; Yiming Ying; Hai Zhang 001 (Neurocomputing, 2021)

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions. - Yunwen Lei; Ting Hu 002; Ke Tang 001 (J. Mach. Learn. Res., 2021)

Stochastic Proximal AUC Maximization. - Yunwen Lei; Yiming Ying (J. Mach. Learn. Res., 2021)

Learning Rates for Stochastic Gradient Descent With Nonconvex Objectives. - Yunwen Lei; Ke Tang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

On the Generalization Analysis of Adversarial Learning. - Waleed Mustafa; Yunwen Lei; Marius Kloft (ICML, 2022)

Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks. - Yunwen Lei; Rong Jin; Yiming Ying (NeurIPS, 2022)

A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. - Mingrui Liu; Zhenxun Zhuang; Yunwen Lei; Chunyang Liao (NeurIPS, 2022)

Stability and Generalization for Markov Chain Stochastic Gradient Methods. - Puyu Wang; Yunwen Lei; Yiming Ying; Ding-Xuan Zhou (NeurIPS, 2022)

Noise-Efficient Learning of Differentially Private Partitioning Machine Ensembles. - Zhanliang Huang; Yunwen Lei; Ata Kabán (ECML/PKDD (4), 2022)

Differentially private SGDA for minimax problems. - Zhenhuan Yang; Shu Hu 001; Yunwen Lei; Kush R. Varshney; Siwei Lyu; Yiming Ying (UAI, 2022)

SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions. - Ming Chen; Wei Jiang; Yi Pan 001; Jianhua Dai; Yunwen Lei; Chunyan Ji (J. Comput. Biol., 2022)

Early Stopping for Iterative Regularization with General Loss Functions. - Ting Hu 002; Yunwen Lei (J. Mach. Learn. Res., 2022)

Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings. - Antoine Ledent; Rodrigo Alves; Yunwen Lei; Yann Guermeur; Marius Kloft (AAAI, 2023)

Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems. - Yunwen Lei (COLT, 2023)

Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process. - Shi Fu; Yunwen Lei; Qiong Cao; Xinmei Tian 001; Dacheng Tao (ICLR, 2023)

Generalization Analysis for Contrastive Representation Learning. - Yunwen Lei; Tianbao Yang; Yiming Ying; Ding-Xuan Zhou (ICML, 2023)

Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms. - Sijia Zhou; Yunwen Lei; Ata Kabán (NeurIPS, 2023)

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks. - Puyu Wang; Yunwen Lei; Di Wang 015; Yiming Ying; Ding-Xuan Zhou (CoRR, 2023)

Stability and Generalization for Minibatch SGD and Local SGD. - Yunwen Lei; Tao Sun 005; Mingrui Liu (CoRR, 2023)

Pairwise Learning with Adaptive Online Gradient Descent. - Tao Sun 005; Qingsong Wang; Yunwen Lei; Dongsheng Li 001; Bao Wang 001 (Trans. Mach. Learn. Res., 2023)

Optimization and Learning With Randomly Compressed Gradient Updates. - Zhanliang Huang; Yunwen Lei; Ata Kabán (Neural Comput., 2023)

Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems. - Jintao Song; Wenqi Lu 001; Yunwen Lei; Yuchao Tang; Zhenkuan Pan 001; Jinming Duan 001 (AAAI, 2024)

Self-certified Tuple-Wise Deep Learning. - Sijia Zhou; Yunwen Lei; Ata Kabán (ECML/PKDD (2), 2024)

Bootstrap SGD: Algorithmic Stability and Robustness. - Andreas Christmann; Yunwen Lei (CoRR, 2024)

Differentially private stochastic gradient descent with low-noise. - Puyu Wang; Yunwen Lei; Yiming Ying; Ding-Xuan Zhou (Neurocomputing, 2024)

Generalization Bounds with Logarithmic Negative-Sample Dependence for Adversarial Contrastive Learning. - Naghmeh Ghanooni; Waleed Mustafa; Yunwen Lei; Anthony Widjaja Lin; Marius Kloft (Trans. Mach. Learn. Res., 2024)

Few-Shot Learning With Dynamic Graph Structure Preserving. - Sichao Fu; Qiong Cao; Yunwen Lei; Yujie Zhong; Yibing Zhan; Xinge You (IEEE Trans. Ind. Informatics, 2024)

Generalization Analysis for Deep Contrastive Representation Learning. - Nong Minh Hieu; Antoine Ledent; Yunwen Lei; Cheng Yeaw Ku (AAAI, 2025)

Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learning. - Liang Wu 015; Ruixi Hu; Yunwen Lei (AAAI, 2025)

On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning. - Bokun Wang; Yunwen Lei; Yiming Ying; Tianbao Yang (ICLR, 2025)

Learning to Sample in Stochastic Optimization. - Sijia Zhou; Yunwen Lei; Ata Kabán (UAI, 2025)

Generalization Guarantees of Gradient Descent for Shallow Neural Networks. - Puyu Wang; Yunwen Lei; Di Wang 015; Yiming Ying; Ding-Xuan Zhou (Neural Comput., 2025)

Randomized Pairwise Learning with Adaptive Sampling: A PAC-Bayes Analysis. - Sijia Zhou; Yunwen Lei; Ata Kabán (CoRR, 2025)

Convergence of Adaptive Stochastic Mirror Descent. - Ting Hu 002; Xiaotong Liu; Kai Ji; Yunwen Lei (IEEE Trans. Neural Networks Learn. Syst., 2025)