Yunwen Lei

Orcid: 0000-0002-5383-467X

According to our database1, Yunwen Lei authored at least 68 papers between 2013 and 2024.

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Bibliography

2024
Few-Shot Learning With Dynamic Graph Structure Preserving.
IEEE Trans. Ind. Informatics, March, 2024

Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Optimization and Learning With Randomly Compressed Gradient Updates.
Neural Comput., July, 2023

Stability and Generalization for Minibatch SGD and Local SGD.
CoRR, 2023

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks.
CoRR, 2023

Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Generalization Analysis for Contrastive Representation Learning.
Proceedings of the International Conference on Machine Learning, 2023

Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Early Stopping for Iterative Regularization with General Loss Functions.
J. Mach. Learn. Res., 2022

SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions.
J. Comput. Biol., 2022

Differentially Private Stochastic Gradient Descent with Low-Noise.
CoRR, 2022

Differentially private SGDA for minimax problems.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Noise-Efficient Learning of Differentially Private Partitioning Machine Ensembles.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Stability and Generalization for Markov Chain Stochastic Gradient Methods.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks.
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.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Generalization Analysis of Adversarial Learning.
Proceedings of the International Conference on Machine Learning, 2022

2021
Learning Rates for Stochastic Gradient Descent With Nonconvex Objectives.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Stochastic Proximal AUC Maximization.
J. Mach. Learn. Res., 2021

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions.
J. Mach. Learn. Res., 2021

Differentially private empirical risk minimization for AUC maximization.
Neurocomputing, 2021

Differentially Private SGD with Non-Smooth Loss.
CoRR, 2021

Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning.
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.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fine-grained Generalization Analysis of Inductive Matrix Completion.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stability and Generalization for Randomized Coordinate Descent.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Learning Interpretable Concept Groups in CNNs.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Fine-grained Generalization Analysis of Structured Output Prediction.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems.
Proceedings of the 38th International Conference on Machine Learning, 2021

Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions.
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.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Fine-grained Generalization Analysis of Vector-Valued Learning.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Stochastic Gradient Descent for Nonconvex Learning Without Bounded Gradient Assumptions.
IEEE Trans. Neural Networks Learn. Syst., 2020

Design of a novel low voltage cell lysing instrument.
J. Comput. Methods Sci. Eng., 2020

Sharper Generalization Bounds for Pairwise Learning.
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.
Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Hard Thresholding Algorithms for AUC Maximization.
Proceedings of the 20th IEEE International Conference on Data Mining, 2020

On Performance Estimation in Automatic Algorithm Configuration.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Data-Dependent Generalization Bounds for Multi-Class Classification.
IEEE Trans. Inf. Theory, 2019

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping.
J. Mach. Learn. Res., 2019

Convergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms.
Int. J. Pattern Recognit. Artif. Intell., 2019

Improved Generalisation Bounds for Deep Learning Through L<sup>∞</sup> Covering Numbers.
CoRR, 2019

A Generalization Error Bound for Multi-class Domain Generalization.
CoRR, 2019

Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions.
CoRR, 2019

Optimal Stochastic and Online Learning with Individual Iterates.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Learning Theory of Randomized Sparse Kaczmarz Method.
SIAM J. Imaging Sci., 2018

Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning.
J. Mach. Learn. Res., 2018

Refined bounds for online pairwise learning algorithms.
Neurocomputing, 2018

Convergence of Online Mirror Descent Algorithms.
CoRR, 2018

Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Generalization Bounds for Regularized Pairwise Learning.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

An Enhanced Firefly Algorithm with Orthogonal Centroid Opposition-Based Learning.
Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018

2017
Analysis of Online Composite Mirror Descent Algorithm.
Neural Comput., 2017

Convergence of Unregularized Online Learning Algorithms.
J. Mach. Learn. Res., 2017

Online pairwise learning algorithms with convex loss functions.
Inf. Sci., 2017

Generalization Error Bounds for Extreme Multi-class Classification.
CoRR, 2017

2016
Local Rademacher complexity bounds based on covering numbers.
Neurocomputing, 2016

Localized Multiple Kernel Learning - A Convex Approach.
Proceedings of The 8th Asian Conference on Machine Learning, 2016

2015
Generalization Performance of Radial Basis Function Networks.
IEEE Trans. Neural Networks Learn. Syst., 2015

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Theory and Algorithms for the Localized Setting of Learning Kernels.
Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

2014
Generalization ability of fractional polynomial models.
Neural Networks, 2014

Refined Rademacher Chaos Complexity Bounds with Applications to the Multikernel Learning Problem.
Neural Comput., 2014

2013
Universal learning using free multivariate splines.
Neurocomputing, 2013

Approximation and estimation bounds for free knot splines.
Comput. Math. Appl., 2013


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