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Yiming Ying

38 papers · 2004–2025 · 9 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (21) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (9) 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ† Grand Slam πŸ”¬ Deep Specialist (16) ❓ The Questioner πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (10) ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (58) πŸ’Ž Century Club (38)

Conferences

NIPS (11) ICML (9) JMLR (8) AISTATS (3) ICLR (3) AAAI (1) ACML (1) ICCV (1) UAI (1)

Top co-authors

Yunwen Lei (12) Tianbao Yang (9) Siwei Lyu (9) ZHENHUAN YANG (6) Ding-Xuan Zhou (5) Shu Hu (4) Zhuoning Yuan (3) Xin Wang (3) Peng Li (2) Bokun Wang (2)

Keywords

generalization bound (9) stochastic gradient descent (8) metric learning (7) convex optimization (7) stochastic optimization (6) algorithmic stability (5) kernel methods (4) auc maximization (4) imbalanced classification (4) gradient descent (4) pairwise learning (4) differential privacy (3) multi-class classification (3) minimax problem (3) learning theory (3) population risk (3) reproducing kernel hilbert space (3) stability analysis (3) binary classification (2) online learning (2)

Papers

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