← Authors

Yunwen Lei

40 papers · 2015–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (10) 🐝 Cross-Pollinator (3) πŸ”¬ Deep Specialist (20) πŸ† Keyword Champion (5) 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ† Grand Slam 🌱 Topic Pioneer πŸ—ƒοΈ Keyword Collector (124) ⚑ Prolific Year (13) πŸ“ˆ Trend Setter πŸ’Ž Century Club (40) πŸ”₯ Unstoppable (8)

Conferences

NIPS (11) AAAI (6) JMLR (6) ICML (5) IJCAI (4) ICLR (3) UAI (2) ACML (1) AISTATS (1) COLT (1)

Top co-authors

Yiming Ying (12) Marius Kloft (11) Antoine Ledent (8) Ke Tang (4) Tianbao Yang (4) ZHENHUAN YANG (4) Ding-Xuan Zhou (4) Waleed Mustafa (3) Liang Wu (3) Puyu Wang (3)

Research topics

Computer Vision (1)

Keywords

generalization bound (21) stochastic gradient descent (14) algorithmic stability (8) pairwise learning (6) early stopping (5) metric learning (5) convex optimization (5) stochastic optimization (5) stability analysis (5) learning theory (3) differential privacy (3) neural network (3) multi-class classification (3) rademacher complexity (3) reproducing kernel hilbert space (3) minimax problem (3) online learning (2) excess risk bound (2) representation learning (2) mirror descent (2)

Papers

Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness ICML 2025
On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning ICLR 2025
Learning to Sample in Stochastic Optimization UAI 2025
Generalization Analysis for Deep Contrastive Representation Learning AAAI 2025
Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learning AAAI 2025
Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems AAAI 2024
Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process ICLR 2023
Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms NIPS 2023
Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems COLT 2023
Generalization Analysis for Contrastive Representation Learning ICML 2023
Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings AAAI 2023
On the Generalization Analysis of Adversarial Learning ICML 2022
Differentially private SGDA for minimax problems UAI 2022
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks NIPS 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
Early Stopping for Iterative Regularization with General Loss Functions JMLR 2022
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions ICLR 2021
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning NIPS 2021
Generalization Guarantee of SGD for Pairwise Learning NIPS 2021
Fine-grained Generalization Analysis of Inductive Matrix Completion NIPS 2021
Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks AAAI 2021
Fine-grained Generalization Analysis of Vector-Valued Learning AAAI 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
Learning Interpretable Concept Groups in CNNs IJCAI 2021
Fine-grained Generalization Analysis of Structured Output Prediction IJCAI 2021
Stability and Generalization for Randomized Coordinate Descent IJCAI 2021
Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions JMLR 2021
Stochastic Proximal AUC Maximization JMLR 2021
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent ICML 2020
Sharper Generalization Bounds for Pairwise Learning NIPS 2020
Optimal Stochastic and Online Learning with Individual Iterates NIPS 2019
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping JMLR 2019
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities NIPS 2018
Convergence of Unregularized Online Learning Algorithms JMLR 2018
Generalization Bounds for Regularized Pairwise Learning IJCAI 2018
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning JMLR 2018
Localized Multiple Kernel Learningβ€”A Convex Approach ACML 2016
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms NIPS 2015