| MULTIVARIATE QUANTILES AND MULTIPLE-OUTPUT REGRESSION QUANTILES: FROM L ₁ OPTIMIZATION TO HALFSPACE DEPTH [with Discussion and Rejoinder] M Hallin, D Paindaveine, M Šiman, Y Wei, R Serfling, Y Zuo, L Kong, ... The Annals of Statistics, 635-703, 2010 | 288 | 2010 |
| Quantile tomography: Using quantiles with multivariate data L Kong, I Mizera Statistica Sinica 22, 1589-1610, 2012 | 202 | 2012 |
| Evaluation of openai o1: Opportunities and challenges of agi T Zhong, Z Liu, Y Pan, Y Zhang, Y Zhou, S Liang, Z Wu, Y Lyu, P Shu, ... arXiv preprint arXiv:2409.18486, 2024 | 186 | 2024 |
| Multivariate varying coefficient model for functional responses H Zhu, R Li, L Kong Annals of statistics 40 (5), 2634, 2012 | 168 | 2012 |
| Spatially varying coefficient model for neuroimaging data with jump discontinuities H Zhu, J Fan, L Kong Journal of the American Statistical Association 109 (507), 1084-1098, 2014 | 151 | 2014 |
| Distributional reinforcement learning for efficient exploration B Mavrin, S Zhang, H Yao, L Kong, K Wu, Y Yu Proceedings of the Thirty-sixth International Conference on Machine Learning, 2019 | 130 | 2019 |
| FADTTS: functional analysis of diffusion tensor tract statistics H Zhu, L Kong, R Li, M Styner, G Gerig, W Lin, JH Gilmore NeuroImage 56 (3), 1412-1425, 2011 | 99 | 2011 |
| Story Forest: Extracting Events and Telling Stories from Breaking News B LIU, FX HAN, DI NIU, L KONG, K LAI ACM Transactions on Knowledge Discovery from Data (TKDD) 14 (3), Article 31, 2020 | 68 | 2020 |
| Partial functional linear quantile regression for neuroimaging data analysis D Yu, L Kong, I Mizera Neurocomputing 195, 74-87, 2016 | 68 | 2016 |
| Exposure to urban air pollution and bone health in clinically healthy six-year-old children L Calderón-Garcidueñas, A Mora-Tiscareño, M Franco-Lira, ... Arhiv za higijenu rada i toksikologiju 64 (1), 23-23, 2013 | 66 | 2013 |
| A review of statistical methods in imaging genetics FS Nathoo, L Kong, H Zhu, Alzheimer's Disease Neuroimaging Initiative Canadian Journal of Statistics 47 (1), 108-131, 2019 | 64 | 2019 |
| Discussion of "Multivariate quantiles and multiple-output regression quantiles: From optimization to halfspace depth" L Kong, I Mizera arXiv preprint arXiv:1002.4509, 2010 | 63* | 2010 |
| Smooth depth contours characterize the underlying distribution L Kong, Y Zuo Journal of Multivariate Analysis 101 (9), 2222-2226, 2010 | 61 | 2010 |
| A general framework for quantile estimation with incomplete data P Han, L Kong, J Zhao, X Zhou Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2019 | 53 | 2019 |
| Growing story forest online from massive breaking news B Liu, D Niu, K Lai, L Kong, Y Xu Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 53 | 2017 |
| Learning privately over distributed features: An ADMM sharing approach Y Hu, K Ge, P Liu, L Kong, B Jiang, D Niu NeurIPS-SpicyFL 2020, 2020 | 52* | 2020 |
| Association of pre-pregnancy BMI and gestational weight gain with fat mass distribution and accretion during pregnancy and early postpartum: a prospective study of Albertan women FB Subhan, L Shulman, Y Yuan, LJ McCargar, L Kong, RC Bell BMJ open 9 (7), e026908, 2019 | 52 | 2019 |
| A method for generating synthetic longitudinal health data L Mosquera, K El Emam, L Ding, V Sharma, XH Zhang, SE Kababji, ... BMC Medical Research Methodology 23 (1), 67, 2023 | 47 | 2023 |
| Advanced algorithms for penalized quantile and composite quantile regression M Pietrosanu, J Gao, L Kong, B Jiang, D Niu Computational Statistics 36 (1), 333-346, 2021 | 42 | 2021 |
| QUOTA: The quantile option architecture for reinforcement learning S Zhang, B Mavrin, L Kong, B Liu, H Yao Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5797-5804, 2019 | 37 | 2019 |
Bei JiangAssociate Professor of Statistics, Canada CIFAR AI Chair, University of AlbertaVerified email at ualberta.ca
