Support Vector Machine Soft Margin Classifiers: Error Analysis. - Di-Rong Chen; Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (J. Mach. Learn. Res., 2004)

Learning Rates of Least-Square Regularized Regression. - Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (Found. Comput. Math., 2006)

Online Regularized Classification Algorithms. - Yiming Ying; Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2006)

A Spectral Regularization Framework for Multi-Task Structure Learning. - Andreas Argyriou; Charles A. Micchelli; Massimiliano Pontil; Yiming Ying (NIPS, 2007)

Convergence analysis of online algorithms. - Yiming Ying (Adv. Comput. Math., 2007)

Multi-kernel regularized classifiers. - Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (J. Complex., 2007)

Learnability of Gaussians with Flexible Variances. - Yiming Ying; Ding-Xuan Zhou (J. Mach. Learn. Res., 2007)

Learning Coordinate Gradients with Multi-Task Kernels. - Yiming Ying; Colin Campbell (COLT, 2008)

Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins. - Theodoros Damoulas; Yiming Ying; Mark A. Girolami; Colin Campbell (ICMLA, 2008)

Online Gradient Descent Learning Algorithms. - Yiming Ying; Massimiliano Pontil (Found. Comput. Math., 2008)

Universal Multi-Task Kernels. - Andrea Caponnetto; Charles A. Micchelli; Massimiliano Pontil; Yiming Ying (J. Mach. Learn. Res., 2008)

Generalization Bounds for Learning the Kernel Problem. - Yiming Ying; Colin Campbell (COLT, 2009)

A Variational Approach to Semi-Supervised Clustering. - Peng Li; Yiming Ying; Colin Campbell (ESANN, 2009)

GSML: A Unified Framework for Sparse Metric Learning. - Kaizhu Huang; Yiming Ying; Colin Campbell (ICDM, 2009)

Analysis of SVM with Indefinite Kernels. - Yiming Ying; Colin Campbell; Mark A. Girolami (NIPS, 2009)

Sparse Metric Learning via Smooth Optimization. - Yiming Ying; Kaizhu Huang; Colin Campbell (NIPS, 2009)

Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm. - Yiming Ying; Colin Campbell; Theodoros Damoulas; Mark A. Girolami (PRIB, 2009)

Enhanced protein fold recognition through a novel data integration approach. - Yiming Ying; Kaizhu Huang; Colin Campbell (BMC Bioinform., 2009)

Rademacher Chaos Complexities for Learning the Kernel Problem. - Yiming Ying; Colin Campbell (Neural Comput., 2010)

Generalized sparse metric learning with relative comparisons. - Kaizhu Huang; Yiming Ying; Colin Campbell (Knowl. Inf. Syst., 2011)

Learning with Support Vector Machines - Colin Campbell; Yiming Ying (Morgan & Claypool Publishers, 2011)

Distance Metric Learning Revisited. - Qiong Cao; Yiming Ying; Peng Li (ECML/PKDD (1), 2012)

Learning the coordinate gradients. - Yiming Ying; Qiang Wu 003; Colin Campbell (Adv. Comput. Math., 2012)

Distance Metric Learning with Eigenvalue Optimization. - Yiming Ying; Peng Li (J. Mach. Learn. Res., 2012)

Similarity Metric Learning for Face Recognition. - Qiong Cao; Yiming Ying; Peng Li (ICCV, 2013)

Large Margin Local Metric Learning. - Julien Bohné; Yiming Ying; Stéphane Gentric; Massimiliano Pontil (ECCV (2), 2014)

Guaranteed Classification via Regularized Similarity Learning. - Zheng-Chu Guo; Yiming Ying (Neural Comput., 2014)

Online Pairwise Learning Algorithms with Kernels. - Yiming Ying; Ding-Xuan Zhou (CoRR, 2015)

Unregularized Online Learning Algorithms with General Loss Functions. - Yiming Ying; Ding-Xuan Zhou (CoRR, 2015)

Co-Regularized PLSA for Multi-Modal Learning. - Xin Wang 045; Ming-Ching Chang; Yiming Ying; Siwei Lyu (AAAI, 2016)

Fast Convergence of Online Pairwise Learning Algorithms. - Martin Boissier 002; Siwei Lyu; Yiming Ying; Ding-Xuan Zhou (AISTATS, 2016)

Stochastic Online AUC Maximization. - Yiming Ying; Longyin Wen; Siwei Lyu (NIPS, 2016)

Generalization bounds for metric and similarity learning. - Qiong Cao; Zheng-Chu Guo; Yiming Ying (Mach. Learn., 2016)

Online Pairwise Learning Algorithms. - Yiming Ying; Ding-Xuan Zhou (Neural Comput., 2016)

Learning with Average Top-k Loss. - Yanbo Fan; Siwei Lyu; Yiming Ying; Bao-Gang Hu (NIPS, 2017)

Online regularized learning with pairwise loss functions. - Zheng-Chu Guo; Yiming Ying; Ding-Xuan Zhou (Adv. Comput. Math., 2017)

Stochastic Proximal Algorithms for AUC Maximization. - Michael Natole; Yiming Ying; Siwei Lyu (ICML, 2018)

Explain Black-box Image Classifications Using Superpixel-based Interpretation. - Yi Wei 006; Ming-Ching Chang; Yiming Ying; Ser Nam Lim; Siwei Lyu (ICPR, 2018)

Kernelized Convex Hull Approximation and its Applications in Data Description Tasks. - Chengqiang Huang; Yulei Wu; Geyong Min; Yiming Ying (IJCNN, 2018)

A Univariate Bound of Area Under ROC. - Siwei Lyu; Yiming Ying (UAI, 2018)

Supervised Intra- and Inter-Modality Similarity Preserving Hashing for Cross-Modal Retrieval. - Zhikui Chen; Fangming Zhong; Geyong Min; Yonglin Leng; Yiming Ying (IEEE Access, 2018)

Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise. - Yunlong Feng; Yiming Ying (CoRR, 2018)

Learning local metrics from pairwise similarity data. - Julien Bohné; Yiming Ying; Stéphane Gentric; Massimiliano Pontil (Pattern Recognit., 2018)

Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization. - Baojian Zhou; Feng Chen 001; Yiming Ying (ICML, 2019)

Dual Averaging Method for Online Graph-structured Sparsity. - Baojian Zhou; Feng Chen 001; Yiming Ying (KDD, 2019)

Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning. - Wei Shen; Zhenhuan Yang; Yiming Ying; Xiaoming Yuan (CoRR, 2019)

Stochastic AUC Optimization Algorithms With Linear Convergence. - Michael Natole; Yiming Ying; Siwei Lyu (Frontiers Appl. Math. Stat., 2019)

Online optimization for residential PV-ESS energy system scheduling. - Zhenhuan Yang; Yiming Ying; Qilong Min (Math. Found. Comput., 2019)

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

Online AUC Optimization for Sparse High-Dimensional Datasets. - Baojian Zhou; Yiming Ying; Steven Skiena (ICDM, 2020)

Stochastic AUC Maximization with Deep Neural Networks. - Mingrui Liu; Zhuoning Yuan; Yiming Ying; Tianbao Yang (ICLR, 2020)

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

Learning by Minimizing the Sum of Ranked Range. - Shu Hu 001; Yiming Ying; Xin Wang 045; Siwei Lyu (NeurIPS, 2020)

Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. - Hitesh Sapkota; Yiming Ying; Feng Chen 001; Qi Yu 001 (AISTATS, 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)

Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. - Zhuoning Yuan; Zhishuai Guo; Yi Xu 008; Yiming Ying; Tianbao Yang (ICML, 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)

Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. - Zhuoning Yuan; Zhishuai Guo; Yi Xu 008; Yiming Ying; Tianbao Yang (CoRR, 2021)

Memory-based Optimization Methods for Model-Agnostic Meta-Learning. - Bokun Wang; Zhuoning Yuan; Yiming Ying; Tianbao Yang (CoRR, 2021)

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

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

Circular Capacitive Coupler for Stable Output Under Horizontal Misalignment. - Heyuan Li; Yipeng Liu; Yiming Ying; Minfan Fu (ISIE, 2022)

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

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

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

Sum of Ranked Range Loss for Supervised Learning. - Shu Hu 001; Yiming Ying; Xin Wang 045; Siwei Lyu (J. Mach. Learn. Res., 2022)

Average Top-k Aggregate Loss for Supervised Learning. - Siwei Lyu; Yanbo Fan; Yiming Ying; Bao-Gang Hu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Time Series Anomaly Detection for Trustworthy Services in Cloud Computing Systems. - Chengqiang Huang; Geyong Min; Yulei Wu; Yiming Ying; Ke Pei; Zuochang Xiang (IEEE Trans. Big Data, 2022)

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. - Zhenhuan Yang; Yan Lok Ko; Kush R. Varshney; Yiming Ying (AAAI, 2023)

Outlier Robust Adversarial Training. - Shu Hu 001; Zhenhuan Yang; Xin Wang 045; Yiming Ying; Siwei Lyu (ACML, 2023)

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

Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. - Dixian Zhu; Yiming Ying; Tianbao Yang (ICML, 2023)

Fairness-aware Differentially Private Collaborative Filtering. - Zhenhuan Yang; Yingqiang Ge; Congzhe Su; Dingxian Wang; Xiaoting Zhao; Yiming Ying (WWW (Companion Volume), 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)

Differentially Private Non-convex Learning for Multi-layer Neural Networks. - Hanpu Shen; Cheng-Long Wang 003; Zihang Xiang; Yiming Ying; Di Wang 015 (CoRR, 2023)

AUC Maximization in the Era of Big Data and AI: A Survey. - Tianbao Yang; Yiming Ying (ACM Comput. Surv., 2023)

Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. - Bokun Wang; Zhuoning Yuan; Yiming Ying; Tianbao Yang (J. Mach. Learn. Res., 2023)

Unmixing biological fluorescence image data with sparse and low-rank Poisson regression. - Ruogu Wang; Alex A. Lemus; Colin M. Henneberry; Yiming Ying; Yunlong Feng; Alex M. Valm (Bioinform., 2023)

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. - Ming Yang; Xiyuan Wei; Tianbao Yang; Yiming Ying (ICML, 2024)

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

Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance. - Lisha Chen; Heshan Devaka Fernando; Yiming Ying; Tianyi Chen (J. Mach. Learn. Res., 2024)

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

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction. - Jun Chen; Hong Chen; Yonghua Yu; Yiming Ying (CoRR, 2025)