Ying, Yiming

47 publications

ICML 2025 How Does Labeling Error Impact Contrastive Learning? a Perspective from Data Dimensionality Reduction Jun Chen, Hong Chen, Yonghua Yu, Yiming Ying

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

NeurIPS 2025 Optimal Rates for Generalization of Gradient Descent for Deep ReLU Classification Yuanfan Li, Yunwen Lei, Zheng-Chu Guo, Yiming Ying

NeurIPSW 2024 On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang

ICML 2024 Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying

JMLR 2024 Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen

ICML 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

JMLR 2023 Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang

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

ACML 2023 Outlier Robust Adversarial Training Shu Hu, Zhenhuan Yang, Xin Wang, Yiming Ying, Siwei Lyu

NeurIPS 2023 Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen

UAI 2022 Differentially Private SGDA for Minimax Problems Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R Vashney, Siwei Lyu, Yiming Ying

NeurIPS 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

JMLR 2022 Sum of Ranked Range Loss for Supervised Learning Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu

AISTATS 2021 Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu

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

ICML 2021 Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

NeurIPS 2021 Generalization Guarantee of SGD for Pairwise Learning Yunwen Lei, Mingrui Liu, Yiming Ying

ICLR 2021 Sharper Generalization Bounds for Learning with Gradient-Dominated Objective Functions Yunwen Lei, Yiming Ying

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

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

JMLR 2021 Stochastic Proximal AUC Maximization Yunwen Lei, Yiming Ying

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

NeurIPS 2020 Learning by Minimizing the Sum of Ranked Range Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu

ICLR 2020 Stochastic AUC Maximization with Deep Neural Networks Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang

ICML 2019 Stochastic Iterative Hard Thresholding for Graph-Structured Sparsity Optimization Baojian Zhou, Feng Chen, Yiming Ying

UAI 2018 A Univariate Bound of Area Under ROC Siwei Lyu, Yiming Ying

ICML 2018 Stochastic Proximal Algorithms for AUC Maximization Michael Natole, Yiming Ying, Siwei Lyu

NeurIPS 2017 Learning with Average Top-K Loss Yanbo Fan, Siwei Lyu, Yiming Ying, Baogang Hu

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

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

MLJ 2016 Generalization Bounds for Metric and Similarity Learning Qiong Cao, Zheng-Chu Guo, Yiming Ying

NeurIPS 2016 Stochastic Online AUC Maximization Yiming Ying, Longyin Wen, Siwei Lyu

ECCV 2014 Large Margin Local Metric Learning Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil

ICCV 2013 Similarity Metric Learning for Face Recognition Qiong Cao, Yiming Ying, Peng Li

ECML-PKDD 2012 Distance Metric Learning Revisited Qiong Cao, Yiming Ying, Peng Li

JMLR 2012 Distance Metric Learning with Eigenvalue Optimization Yiming Ying, Peng Li

NeurIPS 2009 Analysis of SVM with Indefinite Kernels Yiming Ying, Colin Campbell, Mark Girolami

COLT 2009 Generalization Bounds for Learning the Kernel Problem Yiming Ying, Colin Campbell

NeurIPS 2009 Sparse Metric Learning via Smooth Optimization Yiming Ying, Kaizhu Huang, Colin Campbell

COLT 2008 Learning Coordinate Gradients with Multi-Task Kernels Yiming Ying, Colin Campbell

JMLR 2008 Universal Multi-Task Kernels Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying

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

JMLR 2007 Learnability of Gaussians with Flexible Variances Yiming Ying, Ding-Xuan Zhou

JMLR 2004 Support Vector Machine Soft Margin Classifiers: Error Analysis Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou