Yiming Ying

Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width JMLR 2026 How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction ICML 2025 On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning ICLR 2025 Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms ICML 2024 Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance JMLR 2024 Generalization Analysis for Contrastive Representation Learning ICML 2023 Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity ICML 2023 Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning JMLR 2023 Minimax AUC Fairness: Efficient Algorithm with Provable Convergence AAAI 2023 Outlier Robust Adversarial Training ACML 2023 Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance NeurIPS 2023 Differentially private SGDA for minimax problems UAI 2022 Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks NeurIPS 2022 Stability and Generalization for Markov Chain Stochastic Gradient Methods NeurIPS 2022 Sum of Ranked Range Loss for Supervised Learning JMLR 2022 Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning AISTATS 2021 Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity ICML 2021 Generalization Guarantee of SGD for Pairwise Learning NeurIPS 2021 Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions ICLR 2021 Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning NeurIPS 2021 Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss AISTATS 2021 Stability and Generalization of Stochastic Gradient Methods for Minimax Problems ICML 2021 Stochastic Proximal AUC Maximization JMLR 2021 Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent ICML 2020 Learning by Minimizing the Sum of Ranked Range NeurIPS 2020 Stochastic AUC Maximization with Deep Neural Networks ICLR 2020 Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization ICML 2019 Stochastic Proximal Algorithms for AUC Maximization ICML 2018 Learning with Average Top-k Loss NeurIPS 2017 Fast Convergence of Online Pairwise Learning Algorithms AISTATS 2016 Stochastic Online AUC Maximization NeurIPS 2016 Similarity Metric Learning for Face Recognition ICCV 2013 Distance Metric Learning with Eigenvalue Optimization JMLR 2012 Analysis of SVM with Indefinite Kernels NeurIPS 2009 Sparse Metric Learning via Smooth Optimization NeurIPS 2009 Universal Multi-Task Kernels JMLR 2008 A Spectral Regularization Framework for Multi-Task Structure Learning NeurIPS 2007 Learnability of Gaussians with Flexible Variances JMLR 2007 Support Vector Machine Soft Margin Classifiers: Error Analysis JMLR 2004