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2020 – today
- 2024
[j31]Puyu Wang
, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Differentially private stochastic gradient descent with low-noise. Neurocomputing 585: 127557 (2024)- [j30]Lisha Chen, Heshan Devaka Fernando, Yiming Ying, Tianyi Chen:
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance. J. Mach. Learn. Res. 25: 193:1-193:53 (2024) - [c45]Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. ICML 2024 - 2023
- [j29]Ruogu Wang, Alex A. Lemus, Colin M. Henneberry, Yiming Ying, Yunlong Feng, Alex M. Valm:
Unmixing biological fluorescence image data with sparse and low-rank Poisson regression. Bioinform. 39(4) (2023) - [j28]Tianbao Yang, Yiming Ying:
AUC Maximization in the Era of Big Data and AI: A Survey. ACM Comput. Surv. 55(8): 172:1-172:37 (2023) - [j27]Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. J. Mach. Learn. Res. 24: 145:1-145:46 (2023) - [c44]Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying:
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. AAAI 2023: 11909-11917 - [c43]Shu Hu, Zhenhuan Yang, Xin Wang, Yiming Ying, Siwei Lyu:
Outlier Robust Adversarial Training. ACML 2023: 454-469 - [c42]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. ICML 2023: 19200-19227 - [c41]Dixian Zhu, Yiming Ying, Tianbao Yang:
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. ICML 2023: 43289-43325 - [c40]Lisha Chen, Heshan Devaka Fernando, Yiming Ying, Tianyi Chen:
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance. NeurIPS 2023 - [c39]Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying:
Fairness-aware Differentially Private Collaborative Filtering. WWW (Companion Volume) 2023: 927-931 - [i34]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. CoRR abs/2302.12383 (2023) - [i33]Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying:
Fairness-aware Differentially Private Collaborative Filtering. CoRR abs/2303.09527 (2023) - [i32]Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou:
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks. CoRR abs/2305.16891 (2023) - [i31]Lisha Chen, Heshan Devaka Fernando, Yiming Ying, Tianyi Chen:
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance. CoRR abs/2305.20057 (2023) - [i30]Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. CoRR abs/2307.03357 (2023) - [i29]Shu Hu, Zhenhuan Yang, Xin Wang, Yiming Ying, Siwei Lyu:
Outlier Robust Adversarial Training. CoRR abs/2309.05145 (2023) - [i28]Hanpu Shen, Cheng-Long Wang, Zihang Xiang, Yiming Ying, Di Wang:
Differentially Private Non-convex Learning for Multi-layer Neural Networks. CoRR abs/2310.08425 (2023) - 2022
- [j26]Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu:
Sum of Ranked Range Loss for Supervised Learning. J. Mach. Learn. Res. 23: 112:1-112:44 (2022) - [j25]Siwei Lyu, Yanbo Fan, Yiming Ying, Bao-Gang Hu:
Average Top-k Aggregate Loss for Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(1): 76-86 (2022) - [j24]Chengqiang Huang, Geyong Min, Yulei Wu, Yiming Ying, Ke Pei, Zuochang Xiang:
Time Series Anomaly Detection for Trustworthy Services in Cloud Computing Systems. IEEE Trans. Big Data 8(1): 60-72 (2022) - [c38]Heyuan Li, Yipeng Liu, Yiming Ying, Minfan Fu:
Circular Capacitive Coupler for Stable Output Under Horizontal Misalignment. ISIE 2022: 768-773 - [c37]Yunwen Lei, Rong Jin, Yiming Ying:
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks. NeurIPS 2022 - [c36]Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Stability and Generalization for Markov Chain Stochastic Gradient Methods. NeurIPS 2022 - [c35]Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying:
Differentially private SGDA for minimax problems. UAI 2022: 2192-2202 - [i27]Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying:
Differentially Private SGDA for Minimax Problems. CoRR abs/2201.09046 (2022) - [i26]Tianbao Yang, Yiming Ying:
AUC Maximization in the Era of Big Data and AI: A Survey. CoRR abs/2203.15046 (2022) - [i25]Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying:
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence. CoRR abs/2208.10451 (2022) - [i24]Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Differentially Private Stochastic Gradient Descent with Low-Noise. CoRR abs/2209.04188 (2022) - [i23]Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Stability and Generalization for Markov Chain Stochastic Gradient Methods. CoRR abs/2209.08005 (2022) - [i22]Yunwen Lei, Rong Jin, Yiming Ying:
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks. CoRR abs/2209.09298 (2022) - 2021
- [j23]Puyu Wang, Zhenhuan Yang, Yunwen Lei, Yiming Ying, Hai Zhang:
Differentially private empirical risk minimization for AUC maximization. Neurocomputing 461: 419-437 (2021) - [j22]Yunwen Lei, Yiming Ying:
Stochastic Proximal AUC Maximization. J. Mach. Learn. Res. 22: 61:1-61:45 (2021) - [c34]Zhenhuan Yang, Yunwen Lei, Siwei Lyu, Yiming Ying:
Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss. AISTATS 2021: 2026-2034 - [c33]Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu:
Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. AISTATS 2021: 2188-2196 - [c32]Yunwen Lei, Yiming Ying:
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions. ICLR 2021 - [c31]Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. ICML 2021: 6175-6186 - [c30]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. ICML 2021: 12219-12229 - [c29]Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying:
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. NeurIPS 2021: 20160-20171 - [c28]Yunwen Lei, Mingrui Liu, Yiming Ying:
Generalization Guarantee of SGD for Pairwise Learning. NeurIPS 2021: 21216-21228 - [i21]Puyu Wang, Yunwen Lei, Yiming Ying, Hai Zhang:
Differentially Private SGD with Non-Smooth Loss. CoRR abs/2101.08925 (2021) - [i20]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. CoRR abs/2102.04635 (2021) - [i19]Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. CoRR abs/2105.03793 (2021) - [i18]Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu:
Sum of Ranked Range Loss for Supervised Learning. CoRR abs/2106.03300 (2021) - [i17]Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-based Optimization Methods for Model-Agnostic Meta-Learning. CoRR abs/2106.04911 (2021) - [i16]Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying:
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. CoRR abs/2111.12050 (2021) - 2020
- [c27]Zhenhuan Yang, Baojian Zhou, Yunwen Lei, Yiming Ying:
Stochastic Hard Thresholding Algorithms for AUC Maximization. ICDM 2020: 741-750 - [c26]Baojian Zhou, Yiming Ying, Steven Skiena:
Online AUC Optimization for Sparse High-Dimensional Datasets. ICDM 2020: 881-890 - [c25]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. ICLR 2020 - [c24]Yunwen Lei, Yiming Ying:
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent. ICML 2020: 5809-5819 - [c23]Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu:
Learning by Minimizing the Sum of Ranked Range. NeurIPS 2020 - [i15]Yunwen Lei, Yiming Ying:
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent. CoRR abs/2006.08157 (2020) - [i14]Baojian Zhou, Yiming Ying, Steven Skiena:
Online AUC Optimization for Sparse High-Dimensional Datasets. CoRR abs/2009.10867 (2020) - [i13]Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu:
Learning by Minimizing the Sum of Ranked Range. CoRR abs/2010.01741 (2020) - [i12]Zhenhuan Yang, Baojian Zhou, Yunwen Lei, Yiming Ying:
Stochastic Hard Thresholding Algorithms for AUC Maximization. CoRR abs/2011.02396 (2020)
2010 – 2019
- 2019
- [j21]Michael Natole, Yiming Ying, Siwei Lyu:
Stochastic AUC Optimization Algorithms With Linear Convergence. Frontiers Appl. Math. Stat. 5: 30 (2019) - [j20]Zhenhuan Yang, Yiming Ying, Qilong Min:
Online optimization for residential PV-ESS energy system scheduling. Math. Found. Comput. 2(1): 55-71 (2019) - [c22]Baojian Zhou, Feng Chen, Yiming Ying:
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization. ICML 2019: 7563-7573 - [c21]Baojian Zhou, Feng Chen, Yiming Ying:
Dual Averaging Method for Online Graph-structured Sparsity. KDD 2019: 436-446 - [i11]Wei Shen, Zhenhuan Yang, Yiming Ying, Xiaoming Yuan:
Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning. CoRR abs/1904.11316 (2019) - [i10]Baojian Zhou, Feng Chen, Yiming Ying:
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization. CoRR abs/1905.03652 (2019) - [i9]Baojian Zhou, Feng Chen, Yiming Ying:
Dual Averaging Method for Online Graph-structured Sparsity. CoRR abs/1905.10714 (2019) - [i8]Yunwen Lei, Yiming Ying:
Stochastic Proximal AUC Maximization. CoRR abs/1906.06053 (2019) - [i7]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. CoRR abs/1908.10831 (2019) - 2018
- [j19]Zhikui Chen, Fangming Zhong, Geyong Min, Yonglin Leng, Yiming Ying:
Supervised Intra- and Inter-Modality Similarity Preserving Hashing for Cross-Modal Retrieval. IEEE Access 6: 27796-27808 (2018) - [j18]Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil:
Learning local metrics from pairwise similarity data. Pattern Recognit. 75: 315-326 (2018) - [c20]Michael Natole, Yiming Ying, Siwei Lyu:
Stochastic Proximal Algorithms for AUC Maximization. ICML 2018: 3707-3716 - [c19]Yi Wei, Ming-Ching Chang, Yiming Ying, Ser Nam Lim, Siwei Lyu:
Explain Black-box Image Classifications Using Superpixel-based Interpretation. ICPR 2018: 1640-1645 - [c18]Chengqiang Huang, Yulei Wu, Geyong Min, Yiming Ying:
Kernelized Convex Hull Approximation and its Applications in Data Description Tasks. IJCNN 2018: 1-8 - [c17]Siwei Lyu, Yiming Ying:
A Univariate Bound of Area Under ROC. UAI 2018: 43-52 - [i6]Yunlong Feng, Yiming Ying:
Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise. CoRR abs/1803.00183 (2018) - 2017
- [j17]Zheng-Chu Guo, Yiming Ying, Ding-Xuan Zhou:
Online regularized learning with pairwise loss functions. Adv. Comput. Math. 43(1): 127-150 (2017) - [c16]Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu:
Learning with Average Top-k Loss. NIPS 2017: 497-505 - [i5]Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu:
Learning with Average Top-k Loss. CoRR abs/1705.08826 (2017) - 2016
- [j16]Qiong Cao, Zheng-Chu Guo, Yiming Ying:
Generalization bounds for metric and similarity learning. Mach. Learn. 102(1): 115-132 (2016) - [j15]Yiming Ying, Ding-Xuan Zhou:
Online Pairwise Learning Algorithms. Neural Comput. 28(4): 743-777 (2016) - [c15]Xin Wang, Ming-Ching Chang, Yiming Ying, Siwei Lyu:
Co-Regularized PLSA for Multi-Modal Learning. AAAI 2016: 2166-2172 - [c14]Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou:
Fast Convergence of Online Pairwise Learning Algorithms. AISTATS 2016: 204-212 - [c13]Yiming Ying, Longyin Wen, Siwei Lyu:
Stochastic Online AUC Maximization. NIPS 2016: 451-459 - 2015
- [i4]Yiming Ying, Ding-Xuan Zhou:
Online Pairwise Learning Algorithms with Kernels. CoRR abs/1502.07229 (2015) - [i3]Yiming Ying, Ding-Xuan Zhou:
Unregularized Online Learning Algorithms with General Loss Functions. CoRR abs/1503.00623 (2015) - 2014
- [j14]Zheng-Chu Guo, Yiming Ying:
Guaranteed Classification via Regularized Similarity Learning. Neural Comput. 26(3): 497-522 (2014) - [c12]Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil:
Large Margin Local Metric Learning. ECCV (2) 2014: 679-694 - 2013
- [c11]Qiong Cao, Yiming Ying, Peng Li:
Similarity Metric Learning for Face Recognition. ICCV 2013: 2408-2415 - [i2]Zheng-Chu Guo, Yiming Ying:
Guaranteed Classification via Regularized Similarity Learning. CoRR abs/1306.3108 (2013) - 2012
- [j13]Yiming Ying, Qiang Wu, Colin Campbell:
Learning the coordinate gradients. Adv. Comput. Math. 37(3): 355-378 (2012) - [j12]Yiming Ying, Peng Li:
Distance Metric Learning with Eigenvalue Optimization. J. Mach. Learn. Res. 13: 1-26 (2012) - [c10]Qiong Cao, Yiming Ying, Peng Li:
Distance Metric Learning Revisited. ECML/PKDD (1) 2012: 283-298 - [i1]Qiong Cao, Zheng-Chu Guo, Yiming Ying:
Generalization Bounds for Metric and Similarity Learning. CoRR abs/1207.5437 (2012) - 2011
- [b1]Colin Campbell, Yiming Ying:
Learning with Support Vector Machines. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2011, ISBN 978-3-031-00424-7 - [j11]Kaizhu Huang, Yiming Ying, Colin Campbell:
Generalized sparse metric learning with relative comparisons. Knowl. Inf. Syst. 28(1): 25-45 (2011) - 2010
- [j10]Yiming Ying, Colin Campbell:
Rademacher Chaos Complexities for Learning the Kernel Problem. Neural Comput. 22(11): 2858-2886 (2010)
2000 – 2009
- 2009
- [j9]Yiming Ying, Kaizhu Huang, Colin Campbell:
Enhanced protein fold recognition through a novel data integration approach. BMC Bioinform. 10: 267 (2009) - [c9]Yiming Ying, Colin Campbell:
Generalization Bounds for Learning the Kernel Problem. COLT 2009 - [c8]Peng Li, Yiming Ying, Colin Campbell:
A Variational Approach to Semi-Supervised Clustering. ESANN 2009 - [c7]Kaizhu Huang, Yiming Ying, Colin Campbell:
GSML: A Unified Framework for Sparse Metric Learning. ICDM 2009: 189-198 - [c6]Yiming Ying, Colin Campbell, Mark A. Girolami:
Analysis of SVM with Indefinite Kernels. NIPS 2009: 2205-2213 - [c5]Yiming Ying, Kaizhu Huang, Colin Campbell:
Sparse Metric Learning via Smooth Optimization. NIPS 2009: 2214-2222 - [c4]Yiming Ying, Colin Campbell, Theodoros Damoulas, Mark A. Girolami:
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm. PRIB 2009: 427-438 - 2008
- [j8]Yiming Ying, Massimiliano Pontil:
Online Gradient Descent Learning Algorithms. Found. Comput. Math. 8(5): 561-596 (2008) - [j7]Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
Universal Multi-Task Kernels. J. Mach. Learn. Res. 9: 1615-1646 (2008) - [c3]Yiming Ying, Colin Campbell:
Learning Coordinate Gradients with Multi-Task Kernels. COLT 2008: 217-228 - [c2]Theodoros Damoulas, Yiming Ying, Mark A. Girolami, Colin Campbell:
Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins. ICMLA 2008: 577-582 - 2007
- [j6]Yiming Ying:
Convergence analysis of online algorithms. Adv. Comput. Math. 27(3): 273-291 (2007) - [j5]Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Multi-kernel regularized classifiers. J. Complex. 23(1): 108-134 (2007) - [j4]Yiming Ying, Ding-Xuan Zhou:
Learnability of Gaussians with Flexible Variances. J. Mach. Learn. Res. 8: 249-276 (2007) - [c1]Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
A Spectral Regularization Framework for Multi-Task Structure Learning. NIPS 2007: 25-32 - 2006
- [j3]Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Learning Rates of Least-Square Regularized Regression. Found. Comput. Math. 6(2): 171-192 (2006) - [j2]Yiming Ying, Ding-Xuan Zhou:
Online Regularized Classification Algorithms. IEEE Trans. Inf. Theory 52(11): 4775-4788 (2006) - 2004
- [j1]Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Support Vector Machine Soft Margin Classifiers: Error Analysis. J. Mach. Learn. Res. 5: 1143-1175 (2004)
Coauthor Index
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