Shaogao Lv

According to our database, Shaogao Lv authored at least 23 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Efficient Byzantine-robust distributed inference with regularization: A trade-off between compression and adversary.

Xingcai Zhou, Guang Yang, Le Chang, Shaogao Lv

Inf. Sci., 2024

Meta-Learning via PAC-Bayesian with Data-Dependent Prior: Generalization Bounds from Local Entropy.

Shiyu Liu, Wei Shi, Zenglin Xu, Shaogao Lv, Yehong Zhang, Hui Wang

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

2023

Communication-efficient and Byzantine-robust distributed learning with statistical guarantee.

Xingcai Zhou, Le Chang, Pengfei Xu, Shaogao Lv

Pattern Recognit., May, 2023

Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches.

Shaogao Lv, Xin He, Junhui Wang

J. Mach. Learn. Res., 2023

Personalized Federated Learning via Amortized Bayesian Meta-Learning.

Shiyu Liu, Shaogao Lv, Dun Zeng, Zenglin Xu, Hui Wang, Yue Yu

CoRR, 2023

Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix.

Shaogao Lv, Gang Wen, Shiyu Liu, Linsen Wei, Ming Li

CoRR, 2023

Stability and Generalization of 𝓁_p-Regularized Stochastic Learning for GCN.

Shiyu Liu, Linsen Wei, Shaogao Lv, Ming Li

CoRR, 2023

Stability and Generalization of lp-Regularized Stochastic Learning for GCN.

Shiyu Liu, Linsen Wei, Shaogao Lv, Ming Li

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Quantized SGD in Federated Learning: Communication, Optimization and Generalization.

Shiyu Liu, Linsen Wei, Shaogao Lv, Zenglin Xu

Proceedings of the Neural Information Processing - 30th International Conference, 2023

2022

Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting.

Shaogao Lv, Linsen Wei, Qian Zhang, Bin Liu, Zenglin Xu

IEEE Trans. Neural Networks Learn. Syst., 2022

Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions.

Shaogao Lv, Heng Lian

J. Mach. Learn. Res., 2022

2021

SStaGCN: Simplified stacking based graph convolutional networks.

Jia Cai, Zhilong Xiong, Shaogao Lv

CoRR, 2021

Communication-efficient Byzantine-robust distributed learning with statistical guarantee.

Xingcai Zhou, Le Chang, Pengfei Xu, Shaogao Lv

CoRR, 2021

Generalization bounds for graph convolutional neural networks via Rademacher complexity.

Shaogao Lv

CoRR, 2021

Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation.

Shaogao Lv, Junhui Wang, Jiankun Liu, Yong Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model.

Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu

Comput. Stat. Data Anal., 2020

2019

Financial Market Directional Forecasting With Stacked Denoising Autoencoder.

Shaogao Lv, Yongchao Hou, Hongwei Zhou

CoRR, 2019

2018

On the sign consistency of the Lasso for the high-dimensional Cox model.

Shaogao Lv, Mengying You, Huazhen Lin, Heng Lian, Jian Huang

J. Multivar. Anal., 2018

Scalable kernel-based variable selection with sparsistency.

Xin He, Junhui Wang, Shaogao Lv

CoRR, 2018

2017

Ensemble Multiple-Kernel Based Manifold Regularization.

Guo Niu, Zhengming Ma, Shaogao Lv

Neural Process. Lett., 2017

2016

Model-free Variable Selection in Reproducing Kernel Hilbert Space.

Lei Yang, Shaogao Lv, Junhui Wang

J. Mach. Learn. Res., 2016

2015

Optimal learning rates of l^p-type multiple kernel learning under general conditions.

Shaogao Lv, Fanyin Zhou

Inf. Sci., 2015

2010

Learning theory viewpoint of approximation by positive linear operators.

Shaogao Lv, Lei Shi

Comput. Math. Appl., 2010