Learning theory viewpoint of approximation by positive linear operators. - Shaogao Lv; Lei Shi 010 (Comput. Math. Appl., 2010)

Optimal learning rates of lp-type multiple kernel learning under general conditions. - Shaogao Lv; Fanyin Zhou (Inf. Sci., 2015)

Model-free Variable Selection in Reproducing Kernel Hilbert Space. - Lei Yang; Shaogao Lv; Junhui Wang (J. Mach. Learn. Res., 2016)

Ensemble Multiple-Kernel Based Manifold Regularization. - Guo Niu; Zhengming Ma; Shaogao Lv (Neural Process. Lett., 2017)

Scalable kernel-based variable selection with sparsistency. - Xin He; Junhui Wang; Shaogao Lv (CoRR, 2018)

On the sign consistency of the Lasso for the high-dimensional Cox model. - Shaogao Lv; Mengying You; Huazhen Lin; Heng Lian; Jian Huang 003 (J. Multivar. Anal., 2018)

Financial Market Directional Forecasting With Stacked Denoising Autoencoder. - Shaogao Lv; Yongchao Hou; Hongwei Zhou (CoRR, 2019)

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)

Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation. - Shaogao Lv; Junhui Wang; Jiankun Liu; Yong Liu 018 (NeurIPS, 2021)

Generalization bounds for graph convolutional neural networks via Rademacher complexity. - Shaogao Lv (CoRR, 2021)

Communication-efficient Byzantine-robust distributed learning with statistical guarantee. - Xingcai Zhou; Le Chang; Pengfei Xu; Shaogao Lv (CoRR, 2021)

SStaGCN: Simplified stacking based graph convolutional networks. - Jia Cai; Zhilong Xiong; Shaogao Lv (CoRR, 2021)

Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions. - Shaogao Lv; Heng Lian (J. Mach. Learn. Res., 2022)

Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting. - Shaogao Lv; Linsen Wei; Qian Zhang; Bin Liu 022; Zenglin Xu (IEEE Trans. Neural Networks Learn. Syst., 2022)

Quantized SGD in Federated Learning: Communication, Optimization and Generalization. - Shiyu Liu; Linsen Wei; Shaogao Lv; Zenglin Xu (ICONIP (9), 2023)

Stability and Generalization of lp-Regularized Stochastic Learning for GCN. - Shiyu Liu; Linsen Wei; Shaogao Lv; Ming Li 065 (IJCAI, 2023)

Stability and Generalization of 𝓁p-Regularized Stochastic Learning for GCN. - Shiyu Liu; Linsen Wei; Shaogao Lv; Ming Li (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)

Personalized Federated Learning via Amortized Bayesian Meta-Learning. - Shiyu Liu; Shaogao Lv; Dun Zeng; Zenglin Xu; Hui Wang 013; Yue Yu 001 (CoRR, 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)

Communication-efficient and Byzantine-robust distributed learning with statistical guarantee. - Xingcai Zhou; Le Chang; Pengfei Xu; Shaogao Lv (Pattern Recognit., 2023)

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 013 (IJCAI, 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)