Selected SCI/SSCI Papers(*corresponding author):

人工智能领域部分论文(*corresponding author):

1. Shiyu Liu, Wei Shi, Zenglin Xu, Shaogao Lv*, Yehong Zhang, Hui Wang.(2024). Meta-learning via PAC-Bayesian with data-dependent prior: generalization bounds from local entropy. The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2024 ).

2. Shaogao Lv, Xin He* and Junhui Wang. (2023). Minimax kernel-based estimation for partially linear functional models. Journal of Machine Learning Research. (55):1−38.

3. Shiyu Liu, Shaogao Lv*, Linsen Wei and Ming Li. (2023). Stability and generalization of ℓp regularized stochastic learning for GCN. The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023 ).

4. Xingcai Zhou, Le Chang, Pengfei Xu and Shaogao Lv*. (2022). Communication-efficient and Byzantine-robust distributed learning with statistical guarantee. Pattern Recognition. 137, 109312.

5. Shaogao Lv* and Heng Lian. (2022). Debiased distributed learning for sparse partial linear models in high dimensions. Journal of Machine Learning Research.23:1-32.

6. Shaogao Lv, Linsen Wei, Jiankun Liu and Yong Liu*. (2021). Improved learning rates of a functional Lasso-type SVM with sparse multi-kernel representation. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia.

7. Shaogao Lv, LinSen Wei, Zenglin Xu*, Qian Zhang. (2021). Improved inference for imputed based semi-supervised learning under misspeficed setting. IEEE Transaction Neural Network and Learning Systems. Doi: 10.1109/TNNLS.2021.3077312.

8. Lei Yang, Shaogao Lv and Junhui Wang. (2016). Model-free variable selection in reproducing kernel Hilbert space. Journal of Machine Learning Research, 17, 1-24.

9. Yunlong Feng, Shaogao Lv*, Hanyuan Han, Johan A.K. Suykens. (2016). Kernelized elastic net regularization: generalization bounds and sparse recovery. Neural Computation, 28, 525-562.

10. Shaogao Lv*. (2015). Refined generalization bounds of gradient learning over reproducing kernel Hilbert spaces. Neural Computation, (27), 1294-1320.

统计类论文：

1. Yibo Deng , Xin He , and Shaogao Lv*. (2025). Efficient learning nonparametric directed acyclic graph with statistical guarantee. Statistica Sinica.

2. Xin He, Junhui Wang and Shaogao Lv. (2021). Efficient kernel-based variable selection with sparsistency. Statistica Sinica. 31, 2123-2151.

3. Xin He, Shaogao Lv* and Junhui Wang.(2020). Variable selection for classification with derivative-induced regularization. Statistica Sinica. 30, 2075-2103.

4. Heng Lian,Kaifeng Zhao and Shaogao Lv. (2019). Projected spline estimation of the nonparametric function in high-dimensional partially linear models for massive data. Annals of Statistics, (47), 2922-2949.

5. Shaogao Lv, Huazhen Lin*, Fanyin Zhou and Jian Huang. (2018). Oracle inequalities for high-dimensional additive Cox model. Scandinavian Journal of Statistics. (45), 900-922.

6. Huazhen Lin, Lixian Pan and Shaogao Lv. (2018). Semiparametric efficient estimate of the generalized additive model with unknown link and variance. Journal of Econometrics, (202), 230-244.

7 Shaogao Lv, Huazhen Lin*, Heng Lian and Jian Huang. (2018). Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space. Annals of Statistics, (46), 781-813.

8. Xin He, Junhui Wang and Shaogao Lv*. (2018). Gradient-induced model-free variable selection with composite Quantile Regression. Statistica Sinica. (28), 1521-1538.

当前的研究兴趣包括：统计赋能的大模型，强化学习方法与理论，AI驱动的量化投资策略开发等。曾主持过国家自然科学基金4项，作为子课题负责人参与1项国家自科重点专项。过去10年在统计学与人工智能领域发表学术论文20多篇，包括统计学顶刊 “Annals of Statistics”、人工智能CCF-A类期刊/会议 “JMLR”、“NeurIPS”与“IJCAI”等。我们的关注点是统计学与人工智能的交叉，研究理念：以认知科学为启发、以数据思维为引导、以数理基础为支撑。

国家级项目：

1.国家自然科学基金重点专项：海量异构金融数据协同建模与机器学习. (批准号: 72341019) , 2024-2027.子课题负责人.

2.国家自然科学基金面上项目：结构化模型的分布式学习：复杂度、隐私与统计推断. (批准号:12371291), 2024-2027.主持.

3.国家自然科学基金面上项目：半参数统计模型的分布式估计与推断，(批准号：11871277)，2019-2022.主持

4.国家自然科学基金青年项目:高维数据框架内的非参与半参分位数回归模型的研究, (批 准号11301421), 2014-2016.主持.

Selected SCI/SSCI Papers(*corresponding author):

人工智能领域部分论文(*corresponding author):

1. Shiyu Liu, Wei Shi, Zenglin Xu, Shaogao Lv*, Yehong Zhang, Hui Wang.(2024). Meta-learning via PAC-Bayesian with data-dependent prior: generalization bounds from local entropy. The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2024 ).

2. Shaogao Lv, Xin He* and Junhui Wang. (2023). Minimax kernel-based estimation for partially linear functional models. Journal of Machine Learning Research. (55):1−38.

3. Shiyu Liu, Shaogao Lv*, Linsen Wei and Ming Li. (2023). Stability and generalization of ℓp regularized stochastic learning for GCN. The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023 ).

4. Xingcai Zhou, Le Chang, Pengfei Xu and Shaogao Lv*. (2022). Communication-efficient and Byzantine-robust distributed learning with statistical guarantee. Pattern Recognition. 137, 109312.

5. Shaogao Lv* and Heng Lian. (2022). Debiased distributed learning for sparse partial linear models in high dimensions. Journal of Machine Learning Research.23:1-32.

6. Shaogao Lv, Linsen Wei, Jiankun Liu and Yong Liu*. (2021). Improved learning rates of a functional Lasso-type SVM with sparse multi-kernel representation. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia.

7. Shaogao Lv, LinSen Wei, Zenglin Xu*, Qian Zhang. (2021). Improved inference for imputed based semi-supervised learning under misspeficed setting. IEEE Transaction Neural Network and Learning Systems. Doi: 10.1109/TNNLS.2021.3077312.

8. Lei Yang, Shaogao Lv and Junhui Wang. (2016). Model-free variable selection in reproducing kernel Hilbert space. Journal of Machine Learning Research, 17, 1-24.

9. Yunlong Feng, Shaogao Lv*, Hanyuan Han, Johan A.K. Suykens. (2016). Kernelized elastic net regularization: generalization bounds and sparse recovery. Neural Computation, 28, 525-562.

10. Shaogao Lv*. (2015). Refined generalization bounds of gradient learning over reproducing kernel Hilbert spaces. Neural Computation, (27), 1294-1320.

统计类论文：

1. Yibo Deng , Xin He , and Shaogao Lv*. (2025). Efficient learning nonparametric directed acyclic graph with statistical guarantee. Statistica Sinica.

2. Xin He, Junhui Wang and Shaogao Lv. (2021). Efficient kernel-based variable selection with sparsistency. Statistica Sinica. 31, 2123-2151.

3. Xin He, Shaogao Lv* and Junhui Wang.(2020). Variable selection for classification with derivative-induced regularization. Statistica Sinica. 30, 2075-2103.

4. Heng Lian,Kaifeng Zhao and Shaogao Lv. (2019). Projected spline estimation of the nonparametric function in high-dimensional partially linear models for massive data. Annals of Statistics, (47), 2922-2949.

5. Shaogao Lv, Huazhen Lin*, Fanyin Zhou and Jian Huang. (2018). Oracle inequalities for high-dimensional additive Cox model. Scandinavian Journal of Statistics. (45), 900-922.

6. Huazhen Lin, Lixian Pan and Shaogao Lv. (2018). Semiparametric efficient estimate of the generalized additive model with unknown link and variance. Journal of Econometrics, (202), 230-244.

7 Shaogao Lv, Huazhen Lin*, Heng Lian and Jian Huang. (2018). Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space. Annals of Statistics, (46), 781-813.

8. Xin He, Junhui Wang and Shaogao Lv*. (2018). Gradient-induced model-free variable selection with composite Quantile Regression. Statistica Sinica. (28), 1521-1538.