谢传龙 - 北京师范大学 - 自然科学高等研究院

个人简介

学习和工作经历 2007年9月-2011年6月山东师范大学学士 2011年9月-2014年6月北京师范大学硕士 2014年9月-2017年9月香港浸会大学博士 2017年9月-2019年7月暨南大学助理教授 2019年9月-2022年3月华为诺亚方舟实验室研究员主持、参与的科研项目国家自然科学基金面上项目, 回归中的假设检验：一个模型自适应方法. (参与) 国家自然科学基金青年项目,深度神经网络稳健性的若干统计学问题研究. (主持)

研究领域

稳健泛化，深度学习，模型检验

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Guan, Y., Xie, C., & Zhu, L. (2017). Sufficient Dimension Reduction with Mixture Multivariate Skew-Elliptical Distributions. Statistica Sinica, 335-355. Zhao, J., & Xie, C. (2018). A Nonparametric Test for Covariate-Adjusted Models. Statistics & Probability Letters, 133, 65-70. Xie, C., & Zhu, L. (2018). A Minimum Projected-Distance Test for Parametric Single-Index Berkson Models. Test, 27(3), 700-715. Koul, H. L., Xie, C., & Zhu, L. (2019). An Adaptive-to-Model Test for Parametric Single-Index Errors-in-Variables Models. Statistica Sinica, 29(3), 1511-1534. Xie, C., & Zhu, L. (2019). A Goodness-of-Fit Test for Variable-Adjusted Models. Computational Statistics & Data Analysis, 138, 27-48. Gadau, M., Zhang, S., Wang, F., Liguori, S., Li, W., Liu, W., …, & Xie, C. (2020). A Multi-Center International Study of Acupuncture for Lateral Elbow Pain-Results of a Randomized Controlled Trial. European Journal of Pain, 24(8), 1458-1470. Xie, C., & Zhu, L. (2020). Generalized Kernel-Based Inverse Regression Methods for Sufficient Dimension Reduction. Computational Statistics & Data Analysis, 150, 106995. Peng, H., Xie, C., & Zhao, J. (2021). Fast inference for semi-varying coefficient models via local averaging.Computational Statistics & Data Analysis, 157, 107126. Zhou, F., Li, J., Xie, C., Chen, F., Hong, L., Sun, R., & Li, Z. (2021, AAAI). MetaAugment: Sample-Aware Data Augmentation Policy Learning. Li, Z., Xie, C., & Wang, Q. (2021, ICML). Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator. Xu, H., Kang, N., Zhang, G., Xie, C., Liang, X., & Li, Z. (2021, ICCV). NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models. Ye, H., Xie, C., Cai, T., Li, R., Li, Z., & Wang, L. (2021, NeurlPS). Towards a theoretical framework of out-of-distribution generalization. Muhammad, A., Zhou, F., Xie, C., Li, J., Bae, S. H., & Li, Z. (2021, NeurlPS). MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps. Hu, S., Xie, C., Liang, X., & Chang, X. (2022, ICML). Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL. Zhu, Y., Chen, Y., Xie, C., Li, X., Zhang, R., Xue, H., Tian, X. and Chen, Y. (2022,NeurlPS). Boosting Out-of-distribution Detection with Typical Features. Dong, Q., Muhammad, A., Zhou, F., Xie, C., Hu, T., Yang, Y., Bae, S.H. and Li, Z., (2022, NeurlPS). ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization. Dong, Q., Zhou, F., King, N., Xie, C., Zhang, S., Li, J., Peng, H., and Li., Z. (2023, AAAI) DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization. Sun, R., Zhou, F., Dong, Z., Xie, C., Hong, L., Li, J., Zhang, R., Li, Z., and Li, Z. (2023, AAAI) Fair CDA: Continuous and Directional Augmentation for Group Fairness.

学术兼职

ICML,NeurlPS, ICLR, CVPR, AAAI, AISTATS审稿人