| 2025 | DSAA | Optimal Utility Bounds for Differentially Private Gradient Descent in Three-Layer Neural Networks. | Puyu Wang, Yunwen Lei, Marius Kloft, Yiming Ying |
| 2025 | ICLR | On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning. | Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang |
| 2025 | ICML | How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction. | Jun Chen, Hong Chen, Yonghua Yu, Yiming Ying |
| 2024 | ICML | Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. | Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying |
| 2023 | AAAI | Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. | Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying |
| 2023 | ACML | Outlier Robust Adversarial Training. | Shu Hu, Zhenhuan Yang, Xin Wang, Yiming Ying, Siwei Lyu |
| 2023 | ICML | Generalization Analysis for Contrastive Representation Learning. | Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou |
| 2023 | ICML | Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. | Dixian Zhu, Yiming Ying, Tianbao Yang |
| 2023 | WWW | Fairness-aware Differentially Private Collaborative Filtering. | Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying |
| 2022 | UAI | Differentially private SGDA for minimax problems. | Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying |
| 2021 | AISTATS | Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. | Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu |
| 2021 | AISTATS | Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss. | Zhenhuan Yang, Yunwen Lei, Siwei Lyu, Yiming Ying |
| 2021 | ICLR | Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions. | Yunwen Lei, Yiming Ying |
| 2021 | ICML | Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. | Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying |
| 2021 | ICML | Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. | Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang |
| 2020 | ICDM | Stochastic Hard Thresholding Algorithms for AUC Maximization. | Zhenhuan Yang, Baojian Zhou, Yunwen Lei, Yiming Ying |
| 2020 | ICDM | Online AUC Optimization for Sparse High-Dimensional Datasets. | Baojian Zhou, Yiming Ying, Steven Skiena |
| 2020 | ICLR | Stochastic AUC Maximization with Deep Neural Networks. | Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang |
| 2020 | ICML | Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent. | Yunwen Lei, Yiming Ying |
| 2019 | ICML | Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization. | Baojian Zhou, Feng Chen, Yiming Ying |
| 2019 | KDD | Dual Averaging Method for Online Graph-structured Sparsity. | Baojian Zhou, Feng Chen, Yiming Ying |
| 2018 | ICML | Stochastic Proximal Algorithms for AUC Maximization. | Michael Natole, Yiming Ying, Siwei Lyu |
| 2018 | IJCNN | Kernelized Convex Hull Approximation and its Applications in Data Description Tasks. | Chengqiang Huang, Yulei Wu, Geyong Min, Yiming Ying |
| 2018 | ICPR | Explain Black-box Image Classifications Using Superpixel-based Interpretation. | Yi Wei, Ming-Ching Chang, Yiming Ying, Ser Nam Lim, Siwei Lyu |
| 2018 | UAI | A Univariate Bound of Area Under ROC. | Siwei Lyu, Yiming Ying |
| 2016 | AAAI | Co-Regularized PLSA for Multi-Modal Learning. | Xin Wang, Ming-Ching Chang, Yiming Ying, Siwei Lyu |
| 2016 | AISTATS | Fast Convergence of Online Pairwise Learning Algorithms. | Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou |
| 2014 | ECCV | Large Margin Local Metric Learning. | Julien Bohn, Yiming Ying, Stphane Gentric, Massimiliano Pontil |
| 2013 | ICCV | Similarity Metric Learning for Face Recognition. | Qiong Cao, Yiming Ying, Peng Li |
| 2009 | COLT | Generalization Bounds for Learning the Kernel Problem. | Yiming Ying, Colin Campbell |
| 2009 | ESANN | A Variational Approach to Semi-Supervised Clustering. | Peng Li, Yiming Ying, Colin Campbell |
| 2009 | ICDM | GSML: A Unified Framework for Sparse Metric Learning. | Kaizhu Huang, Yiming Ying, Colin Campbell |
| 2008 | COLT | Learning Coordinate Gradients with Multi-Task Kernels. | Yiming Ying, Colin Campbell |
| 2008 | ICMLA | Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins. | Theodoros Damoulas, Yiming Ying, Mark A. Girolami, Colin Campbell |