Yiming Ying

According to our database, Yiming Ying authored at least 86 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

Differentially private stochastic gradient descent with low-noise.

Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou

Neurocomputing, 2024

On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning.

Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang

CoRR, 2024

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms.

Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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., April, 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

AUC Maximization in the Era of Big Data and AI: A Survey.

Tianbao Yang, Yiming Ying

ACM Comput. Surv., 2023

Differentially Private Non-convex Learning for Multi-layer Neural Networks.

Hanpu Shen, Cheng-Long Wang, Zihang Xiang, Yiming Ying, Di Wang

CoRR, 2023

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks.

Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou

CoRR, 2023

Fairness-aware Differentially Private Collaborative Filtering.

Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity.

Dixian Zhu, Yiming Ying, Tianbao Yang

Proceedings of the International Conference on Machine Learning, 2023

Generalization Analysis for Contrastive Representation Learning.

Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou

Proceedings of the International Conference on Machine Learning, 2023

Outlier Robust Adversarial Training.

Shu Hu, Zhenhuan Yang, Xin Wang, Yiming Ying, Siwei Lyu

Proceedings of the Asian Conference on Machine Learning, 2023

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence.

Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

Average Top-k Aggregate Loss for Supervised Learning.

Siwei Lyu, Yanbo Fan, Yiming Ying, Bao-Gang Hu

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Sum of Ranked Range Loss for Supervised Learning.

Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu

J. Mach. Learn. Res., 2022

Differentially private SGDA for minimax problems.

Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Stability and Generalization for Markov Chain Stochastic Gradient Methods.

Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks.

Yunwen Lei, Rong Jin, Yiming Ying

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Circular Capacitive Coupler for Stable Output Under Horizontal Misalignment.

Heyuan Li, Yipeng Liu, Yiming Ying, Minfan Fu

Proceedings of the 31st IEEE International Symposium on Industrial Electronics, 2022

2021

Stochastic Proximal AUC Maximization.

Yunwen Lei, Yiming Ying

J. Mach. Learn. Res., 2021

Differentially private empirical risk minimization for AUC maximization.

Puyu Wang, Zhenhuan Yang, Yunwen Lei, Yiming Ying, Hai Zhang

Neurocomputing, 2021

Memory-based Optimization Methods for Model-Agnostic Meta-Learning.

Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang

CoRR, 2021

Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity.

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

CoRR, 2021

Differentially Private SGD with Non-Smooth Loss.

Puyu Wang, Yunwen Lei, Yiming Ying, Hai Zhang

CoRR, 2021

Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning.

Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalization Guarantee of SGD for Pairwise Learning.

Yunwen Lei, Mingrui Liu, Yiming Ying

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity.

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

Proceedings of the 38th International Conference on Machine Learning, 2021

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems.

Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying

Proceedings of the 38th International Conference on Machine Learning, 2021

Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions.

Yunwen Lei, Yiming Ying

Proceedings of the 9th International Conference on Learning Representations, 2021

Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss.

Zhenhuan Yang, Yunwen Lei, Siwei Lyu, Yiming Ying

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning.

Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning by Minimizing the Sum of Ranked Range.

Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent.

Yunwen Lei, Yiming Ying

Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic AUC Maximization with Deep Neural Networks.

Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang

Proceedings of the 8th International Conference on Learning Representations, 2020

Online AUC Optimization for Sparse High-Dimensional Datasets.

Baojian Zhou, Yiming Ying, Steven Skiena

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Stochastic Hard Thresholding Algorithms for AUC Maximization.

Zhenhuan Yang, Baojian Zhou, Yunwen Lei, Yiming Ying

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

2019

Online optimization for residential PV-ESS energy system scheduling.

Zhenhuan Yang, Yiming Ying, Qilong Min

Math. Found. Comput., 2019

Stochastic AUC Optimization Algorithms With Linear Convergence.

Michael Natole, Yiming Ying, Siwei Lyu

Frontiers Appl. Math. Stat., 2019

Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning.

Wei Shen, Zhenhuan Yang, Yiming Ying, Xiaoming Yuan

CoRR, 2019

Dual Averaging Method for Online Graph-structured Sparsity.

Baojian Zhou, Feng Chen, Yiming Ying

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization.

Baojian Zhou, Feng Chen, Yiming Ying

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Learning local metrics from pairwise similarity data.

Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil

Pattern Recognit., 2018

Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise.

Yunlong Feng, Yiming Ying

CoRR, 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

A Univariate Bound of Area Under ROC.

Siwei Lyu, Yiming Ying

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Kernelized Convex Hull Approximation and its Applications in Data Description Tasks.

Chengqiang Huang, Yulei Wu, Geyong Min, Yiming Ying

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Explain Black-box Image Classifications Using Superpixel-based Interpretation.

Yi Wei, Ming-Ching Chang, Yiming Ying, Ser Nam Lim, Siwei Lyu

Proceedings of the 24th International Conference on Pattern Recognition, 2018

Stochastic Proximal Algorithms for AUC Maximization.

Michael Natole, Yiming Ying, Siwei Lyu

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Online regularized learning with pairwise loss functions.

Zheng-Chu Guo, Yiming Ying, Ding-Xuan Zhou

Adv. Comput. Math., 2017

Learning with Average Top-k Loss.

Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Online Pairwise Learning Algorithms.

Yiming Ying, Ding-Xuan Zhou

Neural Comput., 2016

Generalization bounds for metric and similarity learning.

Qiong Cao, Zheng-Chu Guo, Yiming Ying

Mach. Learn., 2016

Stochastic Online AUC Maximization.

Yiming Ying, Longyin Wen, Siwei Lyu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Fast Convergence of Online Pairwise Learning Algorithms.

Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Co-Regularized PLSA for Multi-Modal Learning.

Xin Wang, Ming-Ching Chang, Yiming Ying, Siwei Lyu

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Unregularized Online Learning Algorithms with General Loss Functions.

Yiming Ying, Ding-Xuan Zhou

CoRR, 2015

Online Pairwise Learning Algorithms with Kernels.

Yiming Ying, Ding-Xuan Zhou

CoRR, 2015

2014

Guaranteed Classification via Regularized Similarity Learning.

Zheng-Chu Guo, Yiming Ying

Neural Comput., 2014

Large Margin Local Metric Learning.

Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil

Proceedings of the Computer Vision - ECCV 2014, 2014

2013

Similarity Metric Learning for Face Recognition.

Qiong Cao, Yiming Ying, Peng Li

Proceedings of the IEEE International Conference on Computer Vision, 2013

2012

Distance Metric Learning with Eigenvalue Optimization.

Yiming Ying, Peng Li

J. Mach. Learn. Res., 2012

Learning the coordinate gradients.

Yiming Ying, Qiang Wu, Colin Campbell

Adv. Comput. Math., 2012

Distance Metric Learning Revisited.

Qiong Cao, Yiming Ying, Peng Li

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

2011

Learning with Support Vector Machines

Colin Campbell, Yiming Ying

Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01552-6, 2011

Generalized sparse metric learning with relative comparisons.

Kaizhu Huang, Yiming Ying, Colin Campbell

Knowl. Inf. Syst., 2011

2010

Rademacher Chaos Complexities for Learning the Kernel Problem.

Yiming Ying, Colin Campbell

Neural Comput., 2010

2009

Enhanced protein fold recognition through a novel data integration approach.

Yiming Ying, Kaizhu Huang, Colin Campbell

BMC Bioinform., 2009

Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm.

Yiming Ying, Colin Campbell, Theodoros Damoulas, Mark A. Girolami

Proceedings of the Pattern Recognition in Bioinformatics, 2009

Sparse Metric Learning via Smooth Optimization.

Yiming Ying, Kaizhu Huang, Colin Campbell

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Analysis of SVM with Indefinite Kernels.

Yiming Ying, Colin Campbell, Mark A. Girolami

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

GSML: A Unified Framework for Sparse Metric Learning.

Kaizhu Huang, Yiming Ying, Colin Campbell

Proceedings of the ICDM 2009, 2009

A Variational Approach to Semi-Supervised Clustering.

Peng Li, Yiming Ying, Colin Campbell

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

Generalization Bounds for Learning the Kernel Problem.

Yiming Ying, Colin Campbell

Proceedings of the COLT 2009, 2009

2008

Universal Multi-Task Kernels.

Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying

J. Mach. Learn. Res., 2008

Online Gradient Descent Learning Algorithms.

Yiming Ying, Massimiliano Pontil

Found. Comput. Math., 2008

Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins.

Theodoros Damoulas, Yiming Ying, Mark A. Girolami, Colin Campbell

Proceedings of the Seventh International Conference on Machine Learning and Applications, 2008

Learning Coordinate Gradients with Multi-Task Kernels.

Yiming Ying, Colin Campbell

Proceedings of the 21st Annual Conference on Learning Theory, 2008

2007

Learnability of Gaussians with Flexible Variances.

Yiming Ying, Ding-Xuan Zhou

J. Mach. Learn. Res., 2007

Multi-kernel regularized classifiers.

Qiang Wu, Yiming Ying, Ding-Xuan Zhou

J. Complex., 2007

Convergence analysis of online algorithms.

Yiming Ying

Adv. Comput. Math., 2007

A Spectral Regularization Framework for Multi-Task Structure Learning.

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Online Regularized Classification Algorithms.

Yiming Ying, Ding-Xuan Zhou

IEEE Trans. Inf. Theory, 2006

Learning Rates of Least-Square Regularized Regression.

Qiang Wu, Yiming Ying, Ding-Xuan Zhou

Found. Comput. Math., 2006

2004

Support Vector Machine Soft Margin Classifiers: Error Analysis.

Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou

J. Mach. Learn. Res., 2004