The University of Queensland

Capacity dependent analysis for functional online learning algorithms

Guo, Xin, Guo, Zheng-Chu and Shi, Lei (2023). Capacity dependent analysis for functional online learning algorithms. Applied and Computational Harmonic Analysis, 67 101567, 101567. doi: 10.1016/j.acha.2023.06.002

Gone with the Weed: Incidents of Adolescent Marijuana Use in the United States, 1976-2021

Gu, Jiaxin, Guo, Xin, Liu, Xiaoxi, Yuan, Yue, Zhu, Yushu, Chen, Minheng, Zhou, Tian-Yi and Fu, Qiang (2023). Gone with the Weed: Incidents of Adolescent Marijuana Use in the United States, 1976-2021. Annals of Epidemiology, 88, 23-29. doi: 10.1016/j.annepidem.2023.10.002

The design and optimality of survey counts: a unified framework via the Fisher Information Maximizer

Guo, Xin and Fu, Qiang (2022). The design and optimality of survey counts: a unified framework via the Fisher Information Maximizer. Sociological Methods & Research, 004912412211138. doi: 10.1177/00491241221113877

Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces

Guo, Xin, Lin, Junhong and Zhou, Ding-Xuan (2022). Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces. Applied and Computational Harmonic Analysis, 61, 288-318. doi: 10.1016/j.acha.2022.07.005

Online gradient descent algorithms for functional data learning

Chen, Xiaming, Tang, Bohao, Fan, Jun and Guo, Xin (2022). Online gradient descent algorithms for functional data learning. Journal of Complexity, 70 101635, 101635. doi: 10.1016/j.jco.2021.101635

Sleeping lion or sick man? Machine learning approaches to deciphering heterogeneous images of Chinese in North America

Fu, Qiang, Zhuang, Yufan, Zhu, Yushu and Guo, Xin (2022). Sleeping lion or sick man? Machine learning approaches to deciphering heterogeneous images of Chinese in North America. Annals of the American Association of Geographers, 112 (7), 1-19. doi: 10.1080/24694452.2022.2042180

Detecting temporal anomalies with pseudo age groups: homeownership in Canada, 1981 to 2016

Yuan, Yue, Gu, Jiaxin, Guo, Xin, Zhu, Yushu and Fu, Qiang (2022). Detecting temporal anomalies with pseudo age groups: homeownership in Canada, 1981 to 2016. Population, Space and Place, 28 (1) e2532, 1-18. doi: 10.1002/psp.2532

Modified Poisson regression analysis of grouped and right-censored counts

Fu, Qiang, Zhou, Tian-Yi and Guo, Xin (2021). Modified Poisson regression analysis of grouped and right-censored counts. Journal of the Royal Statistical Society. Series A: Statistics in Society, 184 (4), 1347-1367. doi: 10.1111/rssa.12678

Adolescent marijuana use in the United States and structural breaks: an age-period-cohort analysis, 1991–2018

Gu, Jiaxin, Guo, Xin, Veenstra, Gerry, Zhu, Yushu and Fu, Qiang (2021). Adolescent marijuana use in the United States and structural breaks: an age-period-cohort analysis, 1991–2018. American Journal of Epidemiology, 190 (6), 1056-1063. doi: 10.1093/aje/kwaa269

Agreeing to disagree: choosing among eight topic-modeling methods

Fu, Qiang, Zhuang, Yufan, Gu, Jiaxin, Zhu, Yushu and Guo, Xin (2021). Agreeing to disagree: choosing among eight topic-modeling methods. Big Data Research, 23 100173, 100173. doi: 10.1016/j.bdr.2020.100173

The uses and abuses of an age-period-cohort method: on the linear algebra and statistical properties of intrinsic and related estimators

Fu, Qiang, Guo, Xin, Jeon, Sun Young, Reither, Eric N., Zang, Emma and Land, Kenneth C. (2021). The uses and abuses of an age-period-cohort method: on the linear algebra and statistical properties of intrinsic and related estimators. Mathematical Foundations of Computing, 4 (1), 45-59. doi: 10.3934/mfc.2021001

Modeling interactive components by coordinate kernel polynomial models

Guo, Xin, Li, Lexin and Wu, Qiang (2020). Modeling interactive components by coordinate kernel polynomial models. Mathematical Foundations of Computing, 3 (4), 263-277. doi: 10.3934/mfc.2020010

Preface of the special issue on analysis in data science: M\methods and applications

Guo, Xin and Shi, Lei (2020). Preface of the special issue on analysis in data science: M\methods and applications. Mathematical Foundations of Computing, 3 (4), i-ii. doi: 10.3934/mfc.2020026

A numerical method to compute Fisher information for a special case of heterogeneous negative binomial regression

Guo, Xin, Fu, Qiang, Wang, Yue and Land, Kenneth C. (2020). A numerical method to compute Fisher information for a special case of heterogeneous negative binomial regression. Communications on Pure and Applied Analysis, 19 (8), 4179-4189. doi: 10.3934/cpaa.2020187

Optimizing count responses in surveys: a machine-learning approach

Fu, Qiang, Guo, Xin and Land, Kenneth C. (2020). Optimizing count responses in surveys: a machine-learning approach. Sociological Methods and Research, 49 (3), 637-671. doi: 10.1177/0049124117747302

Distributed minimum error entropy algorithms

Guo, Xin, Hu, Ting and Wu, Qiang (2020). Distributed minimum error entropy algorithms. Journal of Machine Learning Research, 21, 1-31.

Semi-supervised learning with summary statistics

Qin, Huihui and Guo, Xin (2019). Semi-supervised learning with summary statistics. Analysis and Applications, 17 (5), 837-851. doi: 10.1142/S0219530519400037

A Poisson-multinomial mixture approach to grouped and right-censored counts

Fu, Qiang, Guo, Xin and Land, Kenneth C. (2018). A Poisson-multinomial mixture approach to grouped and right-censored counts. Communications in Statistics: Theory and Methods, 47 (2), 427-447. doi: 10.1080/03610926.2017.1303736

Distributed learning with regularized least squares

Lin, Shao-Bo, Guo, Xin and Zhou, Ding-Xuan (2017). Distributed learning with regularized least squares. Journal of Machine Learning Research, 18, 1-31.

Thresholded spectral algorithms for sparse approximations

Guo, Zheng-Chu, Xiang, Dao-Hong, Guo, Xin and Zhou, DIng-Xuan (2017). Thresholded spectral algorithms for sparse approximations. Analysis and Applications, 15 (3), 433-455. doi: 10.1142/S0219530517500026

Playing with the rules and making misleading statements: A response to Luo, Hodges, Winship, and powers

Land, Kenneth C., Fu, Qiang, Guo, Xin, Jeon, Sun Y., Reither, Eric N. and Zang, Emma (2016). Playing with the rules and making misleading statements: A response to Luo, Hodges, Winship, and powers. American Journal of Sociology, 122 (3), 962-973. doi: 10.1086/689853

The Local Edge Machine: inference of dynamic models of gene regulation

McGoff, Kevin A., Guo, Xin, Deckard, Anastasia, Kelliher, Christina M., Leman, Adam R., Francey, Lauren J., Hogenesch, John B., Haase, Steven B. and Harer, John L. (2016). The Local Edge Machine: inference of dynamic models of gene regulation. Genome Biology, 17 (1) 214, 214. doi: 10.1186/s13059-016-1076-z

Sparsity and error analysis of empirical feature-based regularization schemes

Guo, Xin, Fan, Jun and Zhou, Ding-Xuan (2016). Sparsity and error analysis of empirical feature-based regularization schemes. Journal of Machine Learning Research, 17, 1-34.

Introduction to the Peptide Binding Problem of Computational Immunology: New Results

Shen, Wen-Jun, Wong, Hau-San, Xiao, Quan-Wu, Guo, Xin and Smale, Stephen (2014). Introduction to the Peptide Binding Problem of Computational Immunology: New Results. Foundations of Computational Mathematics, 14 (5), 951-984. doi: 10.1007/s10208-013-9173-9

MHC binding prediction with KernelRLSpan and its variations

Shen, Wen-Jun, Wei, Yu Ting, Guo, Xin, Smale, Stephen, Wong, Hau-San and Li, Shuai Cheng (2014). MHC binding prediction with KernelRLSpan and its variations. Journal of Immunological Methods, 406, 10-20. doi: 10.1016/j.jim.2014.02.007

An empirical feature-based learning algorithm producing sparse approximations

Guo, Xin and Zhou, Ding-Xuan (2012). An empirical feature-based learning algorithm producing sparse approximations. Applied and Computational Harmonic Analysis, 32 (3), 389-400. doi: 10.1016/j.acha.2011.07.005

Learning gradients via an early stopping gradient descent method

Guo, Xin (2010). Learning gradients via an early stopping gradient descent method. Journal of Approximation Theory, 162 (11), 1919-1944. doi: 10.1016/j.jat.2010.05.004

Hermite learning with gradient data

Shi, Lei, Guo, Xin and Zhou, Ding-Xuan (2010). Hermite learning with gradient data. Journal of Computational and Applied Mathematics, 233 (11), 3046-3059. doi: 10.1016/j.cam.2009.11.059

Search for K: assessing five topic-modeling approaches to 120,000 Canadian articles

Fu, Qiang, Zhuang, Yufan, Gu, Jiaxin, Zhu, Yushu, Qin, Huihui and Guo, Xin (2019). Search for K: assessing five topic-modeling approaches to 120,000 Canadian articles. 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA United States, 9-12 December 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/BigData47090.2019.9006160