Guanhua Fang is currently an assistant professor at school of Management Fudan University. He was a postdoctoral researcher at Baidu USA (under supervision of Dr. Ping Li) from June 2020 to June 2022. In 2020 May, he finished his Ph.D. (supervised by Prof. Zhiliang Ying) at Department of Statistics, Columbia University. Before this, he received his B.S. in Mathematics and graduated with the highest honor from Fudan University in 2015. His research interests include statistical modeling and computational algorithms for large complex data, latent class modeling, variational Bayes inference, event history analysis, perturbation analysis, heavy-tailed data, reinforcement learning, etc.
Publication List:
News: a paper on cover time analysis with connection to RL is now available on arxiv! (coming soon)
Conference Paper
Peng, H., Fang, G., Li, P. (2023). Copula for instance-wise Feature Selection and Rank. 39th Conference on Uncertainty in Artificial Intelligence. (UAI 2023); PMLR 1651-1661.
Fang, G., Li, P. (2023). Regression with Label Permutation in Log-linear Model. Proceedings of the 40th International Conference on Machine Learning (ICML 2023); PMLR 9716-9760.
Bhatt, S., Fang, G., Li, P. (2023). Piecewise Stationary Bandits under Risk Criteria. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics. (AISTATS 2023); PMLR 206: 4313-4335.
Bhatt, S., Fang, G., Li, P. (2022). Offline Change Detection under Contamination. 38th Conference on Uncertainty in Artificial Intelligence. (UAI 2022); PMLR 180:191-201.
Bhatt, S., Fang, G., Li, P., Samorodnitsky, G. (2022). Nearly optimal catoni’s M-estimator for infinite variance. Proceedings of the 39th International Conference on Machine Learning (ICML 2022); PMLR 1925-1944.
Bhatt, S., Fang, G., Li, P., Samorodnitsky, G. (2022). Minimax M-estimation under Adversarial Contamination. Proceedings of the 39th International Conference on Machine Learning (ICML 2022); PMLR 1906-1924.
Cai, Y., Fang, G., Li, P. (2022). Sensitivity of Under-Determined Linear System. 2022 IEEE International Symposium on Information Theory (ISIT), 2267-2272.
Bhatt, S., Fang, G., Li, P., Samorodnitsky, G. (2022). Regret Analysis for RL using Renewal Bandit Feedback. 2022 IEEE Information Theory Workshop (ITW), 137-142.
Cai, Y., Fang, G., Li, P. (2021). A Note on Sparse Generalized Eigenvalue Problem. 35th Conference on Neural Information Processing Systems. (NeurIPS 2021).
Fang, H., Fang, G., Yu, T., Li, P. (2021). Efficient Greedy Coordinate Descent via Variable Partitioning. 37th Conference on Uncertainty in Artificial Intelligence. (UAI 2021).
Fang, G., Li, P. (2021). On Variational Inference in Biclustering Models. Proceedings of the 38th International Conference on Machine Learning (ICML 2021); PMLR 139:3111-3121.
Fang, G., Li, P. (2021). On Estimation in Latent Variable Models. Proceedings of the 38th International Conference on Machine Learning (ICML 2021); PMLR 139:3100-3010.
Journal Paper
Xu, X., Fang, G., Guo, J., Ying, Z., Zhang, S. (2024). Diagnostic Classification Models for Testlets: Methods and Theory. Psychometrika, in press.
Fang, G., Ward, O., Zheng, T. (2023). Online Estimation and Community Detection For Event Streams on Large Networks. Statisics and Computing, in press.
Fang, G.*, Xu, G.*, Xu, H., Zhu, X., Guan, Y. (2023). Group Network Hawkes Process. Journal of the American Statistical Association, in press.
Fang, G., Guo, J., Xu, X., Ying, Z., Zhang, S. (2021). Identifiability of Bifactor Models. Statistica Sinica. 31, 2309-2330.
Fang, G., Ying, Z., (2020). Latent Class Model for Finding Co-occurent Patterns in Process Data. Psychometrika, 85(3):775-811 .
Xu, H., Fang, G., Ying, Z. (2020). A Latent Topic Model with Markovian Transition for Process Data. British Journal of Mathematical and Statistical Psychology, 73(3), 474 - 505 .
Fang, G., Liu, J., Ying Z., (2019 ). On the Identifiability of Diagnostic Classification. Psychometrika, 84(1), 19-40
Xu, H., Fang, G., Chen, Y., Liu, J., Ying, Z., (2018). Latent Class Analysis of Recurrent Event in Problem Solving Items. Applied Psychological Measurement, 42(6), 478-498
Technical Report
Ling, H., Fang, G., Liu, J., Ying, Z. (2017). A Graphical Latent Class Model for Multivariate Categorical Data. (Available upon request)
Fang, G., Liu, J., Ying, Z., (2017). Latent Variable Selection via Overlap Group LASSO with Applications to Cognitive Assessment. (Available upon request)
Hobbies
Sketch, Xiao (chinese flute), Swimming
Central Park, New York
