Yunwen Lei

Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width JMLR 2026 Stochastic Gradient Methods: Bias, Stability and Generalization JMLR 2026 Data-dependent Algorithmic Robustness Analysis of Pairwise Learning ACML 2025 Generalization Analysis for Deep Contrastive Representation Learning AAAI 2025 Learning to Sample in Stochastic Optimization UAI 2025 On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning ICLR 2025 Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness ICML 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 Generalization Analysis for Contrastive Representation Learning ICML 2023 Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings AAAI 2023 Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process ICLR 2023 Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems COLT 2023 Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms NeurIPS 2023 A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks NeurIPS 2022 Differentially private SGDA for minimax problems UAI 2022 Early Stopping for Iterative Regularization with General Loss Functions JMLR 2022 On the Generalization Analysis of Adversarial Learning ICML 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 Fine-grained Generalization Analysis of Inductive Matrix Completion NeurIPS 2021 Fine-grained Generalization Analysis of Structured Output Prediction IJCAI 2021 Fine-grained Generalization Analysis of Vector-Valued Learning AAAI 2021 Generalization Guarantee of SGD for Pairwise Learning NeurIPS 2021 Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions JMLR 2021 Learning Interpretable Concept Groups in CNNs IJCAI 2021 Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks AAAI 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 for Randomized Coordinate Descent IJCAI 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 Sharper Generalization Bounds for Pairwise Learning NeurIPS 2020 Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping JMLR 2019 Optimal Stochastic and Online Learning with Individual Iterates NeurIPS 2019 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 Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities NeurIPS 2018 Localized Multiple Kernel Learning—A Convex Approach ACML 2016 Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms NeurIPS 2015