Research

My research is mainly about statistical network analysis and statistical learning.  

Google Scholar Profile

Publications

• Mingyu Qi and Tianxi Li. The non-overlapping statistical approximation to overlapping group lasso. Journal of Machine Learning Research,  25(115), 1-70, 2024 [Code]

• Tianxi Li. Contribution to the Discussion of “Root and Community Inference on the Latent Growth Process of a Network” by Crane and Xu. Journal of the Royal Statistical Society: Series B, to appear.

• Tianxi Li, Elizaveta Levina, and Ji Zhu. Community models for networks observed through edge nominations. Journal of Machine Learning Research 24(282):1−36, 2023. [R Code included in the randnet package (version >= 0.3)]

• Tianxi Li and Can M. Le. Network Estimation by Mixing: Adaptivity and More. Journal of American Statistical Association, Theory & Methods, 2023 [R implementation included in the randnet package (version >= 0.4)]

• Tianxi Li, Xiwei Tang, and Ajay Chatrath. Compressed spectral screening for large-scale differential correlation analysis with application in selecting Glioblastoma gene modules. Annals of Applied Statistics (to appear). [R implementation included in the fastCorrDiff package (version >= 0.5)]

• Angus Chan and Tianxi Li. Fitting low-rank network models for egocentrically sampled partial networks. The 2023 International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. [Code]

• Tianxi Li, Yun-Jhong Wu, Elizaveta Levina, and Ji Zhu. Link prediction for egocentrically sampled networks. Journal of Computational and Graphical Statistics, 2023. [Code available on journal website]

• Ruizhong Miao and Tianxi Li. Informative core identification in complex networks. Journal of the Royal Statistical Society: Series B,  Volume 85, Issue 1, February 2023, Pages 108–126. [R Code].

• Ashwinkumar Badanidiyurua, Zhe Feng, Tianxi Li, and Haifeng Xu. Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Reward.  The 2022 Conference on Neural Infomation Processing Systems (NeurIPS 2022).

• Can M. Le and Tianxi Li. Linear regression and its inference on noisy network-linked data. Journal of the Royal Statistical Society: Series B, 2022. [R package NetworkReg on CRAN]

• Quinlan Dawkins, Tianxi Li, Haifeng Xu. Diffusion Source Identification on Networks with Statistical Confidence. International Conference of Machine Learning (ICML 2021) [Code]

• Anna Costine, Paige Delsa, Tianxi Li, Petra Reinke, Prasanna V. Balachandra. Data-Driven Assessment of Chemical Vapor Deposition Grown MoS2 Monolayer Thin Films. Journal of Applied Physics, 128, no. 23 (2020): 235303.

• Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter Bickel, and Elizaveta Levina.  Hierarchical community detection by recursive partitioning. Journal of American Statistical Association, Theory and Methods (2020) 1-18. [Code and Data]

• Tianxi Li, Cheng Qian, Elizaveta Levina, and Ji Zhu. High-dimensional Gaussian graphical model on network-linked data. Journal of Machine Learning Research, 2020, Vol 21, No. 74. [Code and Data]

• Tianxi Li, Elizaveta Levina and Ji Zhu. Rejoinder: "Network cross-validation by edge sampling" .  Biometrika, 2020, asaa021. 

• Tianxi Li, Elizaveta Levina and Ji Zhu. Network cross-validation by edge sampling (with discussion).  Biometrika, 2020, asaa006. [Code]

• Ajay Chatrath, Roza Przanowska, Shashi Kiran, Zhangli Su, Shekhar Saha, Briana Wilson, Takaaki Tsunematsu, Ji-Hye Ahn, Kyung Yong Lee, Teressa Paulsen, Ewelina Sobierajska, Manjari Kiran, Xiwei Tang, Tianxi Li, Pankaj Kumar, Aakrosh Ratan, Anindya Dutta. The Pan-Cancer Landscape of Prognostic Germline Variants in 10,582 Patients. Genome Medicine, 2020, 12, 15 

• Eric Chi and Tianxi Li. Matrix completion from a computational statistics perspective. WIRES: Computational Statistics, 2019;e1469.

• Tianxi Li, Elizaveta Levina, and Ji Zhu.  Prediction models for network-linked data. Annals of Applied Statistics,2019,  Vol. 13, No. 1, 132-164.  [Code] [R package]

• Haizi Yu, Tianxi Li, Lav R. Varshney. Probabilistic rule realization and selection. Neural Information Processing Systems (NIPS 2017).

• Jie Cheng, Tianxi Li, Elizaveta Levina, and Ji Zhu.  High-dimensional Mixed Graphical Models. Journal of Computational and Graphical Statistics 26.2 (2017): 367-378. [Code]

• Alex Deng, Tianxi Li and Yu Guo. Statistical Inference in Two-Stage Online Controlled Experiments with Treatment Selection and Replication. In Proceedings of World Wide Web Conference (WWW) 2014, 609-618.

• Tianxi Li, Chao Gao, Meng Xu, and Bala Rajaratnam. Detecting the Impact Area of BP Deep-water Horizon Oil Discharge: An Analysis by Time Varying Coefficient Logistic Models and Boosting Trees. Computational Statistics, 2013, Vol.29 (1-2), 141-157.

• Tianxi Li, Branislav Kveton, Yu Wu and Ashwin Kashyap. Incorporating Metadata into Dynamic Topic Analysis. Bayesian Modeling Application workshop in UAI’12.

• Tianxi Li, Yu Wu, and Yu Zhang . Twitter Hashtag Prediction Algorithm. In Proceedings of ICOMP’11.

Preprints and unpublished manuscripts

• Wanwan Xu, Selena Wang, Chichun Tan, Xilin Shen, Wenjing Lu, Todd Constable, Tianxi Li, Yize Zhao. Supervised brain node and network construction under voxel-level functional imaging.

• Zhe Sun, Wanwan Xu, Tianxi Li, Jian Kang, Gregorio Alanis-Lobato, Yize Zhao. Bayesian thresholded modeling for integrating brain node and network predictors.

• Emanuel Ben-David, Tianxi Li, Helene Massam and Bala Rajaratnam.  High dimensional Bayesian inference for Gaussian directed acyclic graph models. [Code]

• Tianxi Li. A note on the statistical view of matrix completion.