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2020 – today
- 2024
[j11]Xingcai Zhou, Guang Yang, Le Chang, Shaogao Lv
:
Efficient Byzantine-robust distributed inference with regularization: A trade-off between compression and adversary. Inf. Sci. 678: 121010 (2024)- [c4]Shiyu Liu, Wei Shi, Zenglin Xu, Shaogao Lv, Yehong Zhang, Hui Wang:
Meta-Learning via PAC-Bayesian with Data-Dependent Prior: Generalization Bounds from Local Entropy. IJCAI 2024: 4578-4586 - 2023
- [j10]Shaogao Lv, Xin He, Junhui Wang:
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches. J. Mach. Learn. Res. 24: 55:1-55:38 (2023) - [j9]Xingcai Zhou, Le Chang, Pengfei Xu, Shaogao Lv:
Communication-efficient and Byzantine-robust distributed learning with statistical guarantee. Pattern Recognit. 137: 109312 (2023) - [c3]Shiyu Liu, Linsen Wei, Shaogao Lv, Zenglin Xu:
Quantized SGD in Federated Learning: Communication, Optimization and Generalization. ICONIP (9) 2023: 205-217 - [c2]Shiyu Liu, Linsen Wei, Shaogao Lv, Ming Li:
Stability and Generalization of lp-Regularized Stochastic Learning for GCN. IJCAI 2023: 5685-5693 - [i8]Shiyu Liu, Linsen Wei, Shaogao Lv, Ming Li:
Stability and Generalization of 𝓁p-Regularized Stochastic Learning for GCN. CoRR abs/2305.12085 (2023) - [i7]Shaogao Lv, Gang Wen, Shiyu Liu, Linsen Wei, Ming Li:
Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix. CoRR abs/2307.02126 (2023) - [i6]Shiyu Liu, Shaogao Lv, Dun Zeng, Zenglin Xu, Hui Wang, Yue Yu:
Personalized Federated Learning via Amortized Bayesian Meta-Learning. CoRR abs/2307.02222 (2023) - 2022
- [j8]Shaogao Lv, Heng Lian:
Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions. J. Mach. Learn. Res. 23: 2:1-2:32 (2022) - [j7]Shaogao Lv, Linsen Wei, Qian Zhang, Bin Liu, Zenglin Xu:
Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting. IEEE Trans. Neural Networks Learn. Syst. 33(11): 6346-6359 (2022) - 2021
- [c1]Shaogao Lv, Junhui Wang, Jiankun Liu, Yong Liu:
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation. NeurIPS 2021: 21467-21479 - [i5]Shaogao Lv:
Generalization bounds for graph convolutional neural networks via Rademacher complexity. CoRR abs/2102.10234 (2021) - [i4]Xingcai Zhou, Le Chang, Pengfei Xu, Shaogao Lv:
Communication-efficient Byzantine-robust distributed learning with statistical guarantee. CoRR abs/2103.00373 (2021) - [i3]Jia Cai, Zhilong Xiong, Shaogao Lv:
SStaGCN: Simplified stacking based graph convolutional networks. CoRR abs/2111.08228 (2021) - 2020
- [j6]Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu:
A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. Comput. Stat. Data Anal. 152: 107039 (2020)
2010 – 2019
- 2019
- [i2]Shaogao Lv, Yongchao Hou, Hongwei Zhou:
Financial Market Directional Forecasting With Stacked Denoising Autoencoder. CoRR abs/1912.00712 (2019) - 2018
- [j5]Shaogao Lv, Mengying You, Huazhen Lin, Heng Lian, Jian Huang:
On the sign consistency of the Lasso for the high-dimensional Cox model. J. Multivar. Anal. 167: 79-96 (2018) - [i1]Xin He, Junhui Wang, Shaogao Lv:
Scalable kernel-based variable selection with sparsistency. CoRR abs/1802.09246 (2018) - 2017
- [j4]Guo Niu, Zhengming Ma, Shaogao Lv:
Ensemble Multiple-Kernel Based Manifold Regularization. Neural Process. Lett. 45(2): 539-552 (2017) - 2016
- [j3]Lei Yang, Shaogao Lv, Junhui Wang:
Model-free Variable Selection in Reproducing Kernel Hilbert Space. J. Mach. Learn. Res. 17: 82:1-82:24 (2016) - 2015
- [j2]Shaogao Lv, Fanyin Zhou:
Optimal learning rates of lp-type multiple kernel learning under general conditions. Inf. Sci. 294: 255-268 (2015) - 2010
- [j1]Shaogao Lv, Lei Shi:
Learning theory viewpoint of approximation by positive linear operators. Comput. Math. Appl. 60(12): 3177-3186 (2010)
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