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Peer-reviewed Journals +, *, and - indicate equal contributions, corresponding author, and trainees supervised, respectively.

  1. Wang, K., Chen, X., Han, Y.,-, Xu, W.*, and Kong, L.* (2024). Adaptive Selection for False Discovery Rate Control Leveraging Symmetry, Journal of the American Statistical Association, invited revision submitted.
  2. Xie, J.-, Yan, X., Jiang, B., and Kong, L.* (2023). Statistical inference for smoothed quantile regression with streaming data, Journal of Econometrics, invited revision submitted.
  3. Han, D., Xie, J.-, Liu, J.-, Sun, L., Huang, J., Jiang, B., and Kong, L. (2023). Inference on High-dimensional Single-index Models with Streaming Data, Journal of Machine Learning Research, invited revision submitted.
  4. Xie, J.-, Shi, E.-, Shang, Z., Sang, P., Jiang, B., and Kong, L.* (2023). Scalable inference in functional linear regression with streaming data, Annals of Applied Statistics, invited revision submitted.
  5. Kong, L., Luo, X., Xie, J.-, Zhu, L., and Zhu, H. (2023). A functional nonlinear mixed effects modeling framework for longitudinal functional responses, Electronic Journal of Statistics, invited revision submitted.
  6. Zhou, X.-, Kong, D., Pietrosanu, M.-, Karunamuni, R., and Kong, L.* (2023). Empirical likelihood and robust regression on varying coefficients model with functional responses, Scandinavian Journal of Statistics, invited revision submitted.
  7. Liu, Y.-, Tu, W., Bao, Y., Jiang, B. and Kong, L.* (2023). Asymmetric estimation for varying-coefficient additive model with functional response in reproducing kernel Hilbert space, Statistica Sinica, invited revision submitted.
  8. Li, J. , Kong, L., Jiang, B. and Tu, W. (2023). High-dimensional outlier detection and variable selection via adaptive weighted mean regression, Statistica Sinica, invited revision submitted.
  9. Shi, E.-, Liu, Y.-, Sun, K.-, Li, L., and Kong, L.*(2023). An adaptive model checking test for functional linear model, Bernoulli, invited revision submitted.
  10. Zhou, X., Ding, S., Wang, J., Liu, R.,Kong, L., and Huang, C. (2023). Density-on-scalar Single-index Quantile Regression Model, Technometrics, invited revision submitted.
  11. Yu, D.-, Pietrosanu, M.-, Tu, W., Mizera, I., Jiang, B., Kong, L.* (2023). Functional Linear Partial Quantile Regression with Guaranteed Convergence for Neuroimaging Data Analysis, Statistics in Bioscience, accepted.
  12. Li, M., Kong, L, Pan, B. and Kong, L. (2023). Algorithmic generalization ability of PALM for double sparse regularized regression, Applied Intelligence, accepted.
  13. Guo, W. -, Zhang, X.-, Jiang, B., Kong, L.*, and Hu, Y. (2023). Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis, Computational Statistics, accepted.
  14. Wang, Y.-, Jiang, B., Kong, L.*, and Zhang, Z.* (2023). M-estimation on varying coefficient model with functional response in reproducing kernel Hilbert space, Bernoulli, accepted.
  15. Xie, H.-, Pietrosanu, M.-, Liu, Y.-, Tu, W., Jiang, B., and Kong, L.* (2023). Differentially Private Regularized Stochastic Convex Optimization with Heavy-Tailed Data, Statistica Sinica, accepted.
  16. Zhang, N., Liu, P.-, Kong, L., Jiang, B., and Huang, J-Z. (2023). Functional Linear Quantile Regression on a Two-dimensional Domain, Bernoulli, accepted.
  17. Xie, H.- and Kong, L.*, (2023). Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space, Journal of Multivariate Analysis, accepted.
  18. Benny, C., Pietrosanu, M.- , Lowe, S., Yamamoto, S., Kong, L., McDonald, S., and Paboya, R. (2023). An investigation into the relationship between community engagement and maternal mental health in Calgary, Alberta using the All our Families Cohort> Social Psychiatry and Psychiatric Epidemiology, accepted.
  19. Yan, X., Xie, J., Tu, W., Jiang, B.,and Kong, L.. (2023). Scalable inference for individual treatment effect, Statistics and its Interface, accepted.
  20. Xie, J., Ding, X., Jiang, B., Yan, X.*, and Kong, L.*, (2023). High dimensional model averaging for quantile regression, Canadian Journal of Statistics, accepted.
  21. Sang, P., Kashlak, A., and Kong, L.*, (2022). A reproducing kernel Hilbert space framework for functional classification, Journal of Computational and Graphical Statistics, accepted.
  22. Zhou, X., Kong, D., Kashlak, A., Kong, L.*, Karunamuni, and Zhu, H. (2022). Functional Response Quantile Regression Model, Statistica Sinica, accepted.
  23. Pietrosanu, M.-, Shu, H.-, Jiang, B., Kong, L.* , Heo, G. He, Q., Gilmore, J. and Zhu, H. (2023). Estimation for the bivariate quantile varying coefficient model with application to diffusion tensor imaging data analysis, Biostatistics, Vol. 24, No. 2, 465-48.
  24. Mosquera, L., El Emam, K., Ding, L.-, Sharma, V., Zhang, X.H., Kababji, S.E., Carvalho, C., Hamilton, B., Palfrey, D., Kong, L. and Jiang, B., (2023). A method for generating synthetic longitudinal health data. BMC Medical Research Methodology, 23(1), pp.1-21.
  25. Mohammadzadeh, N., Zhang, N., Branton, W., Ouafa Zghidi-Abouzid, O., Cohen, E., Gelman, B., Estaquier, J. Kong, L. and Power, P. (2023). HIV restriction factor profile in the brain is associated with clinical status and viral burden, Viruses, Vol. 12, No. 2, 316.
  26. Tang, Q., Tu, W.-, and Kong, L. (2023). Estimation for partial functional partially linear additive model, l, Computational Statistics & Data Analysis, Vol. 177, 107584
  27. Tu, W., Jiang, B., and Kong, L.*. (2022). Comment on "Measuring Housing Vitality from Multi-source Big Data and Machine Learning", Journal of the American Statistical Association, Vol. 117, No. 539, 1060-1062.
  28. Zhang, Z., Wang, X., Kong, L., and Zhu, H. (2021) . High-Dimensional Spatial Quantile Function-on-Scalar Regression. Journal of the American Statistical Association, Vol. 117, No. 539, 1563-1570. [PDF]
  29. Jiang, Y., Mosquera, L., Jiang, B., Kong, L. , and Emam, K el. (2022). Re-identification risk assessment using a synthetic estimator, PLoS ONE, 17 (6), e0269097. [PDF]
  30. Tu, W.-, Fu, F.-, Cobzas, D., Kong, L.*, Jiang, B. and Huang, C. (2022). Low Rank plus Sparse Decomposition of fMRI Data with Application to Alzheimer's Disease, Frontiers in Neurosciences, Vol 16, 826316. [PDF]
  31. Liu, M.-, Pietrasonu, M.-, Liu, P., Jiang, B., Zhou, X.* and Kong, L.*(2022). Reproducing Kernel based partial functional expectile regression, Canadian Journal of Statistics, 50(1), 241- 266. [PDF]
  32. Hu, S., Alshehabi J., Hughes, K., Denier, N., Konnikov, A., Ding, L., Xie, J., Hu, Y., Tarafdar, M., Jiang, B., Kong, L., and Dai, H. (2022), Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation, Frontiers in Big Data, Vol 5, 805713. [PDF]
  33. Pietrosanu, M.-, Kong, L., Yuan, Y., Bell, R., Letourneau, N. and Jiang, B. (2022). Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach, Entropy, 24, 232. [PDF]
  34. Agarwal, G., Tu, W.-, Sun, Y. and Kong, L. (2022). Flexible Quantile Contours for Multivariate Functional Data: Beyond Convexity, Computational Statistics & Data Analysis, Vol. 168, 107400.[PDF]
  35. Shaque, A., Xie, H.-, Danyluk, H., Wheatley, B., Broad, R., Kong, L., and Sankar, T. (2022). Comparison of prognostic scoring systems to predict durable pain relief after microvascular decompression for trigeminal neuralgia, World Neurosurgery, Vol. 157, e432-e440. [PDF]
  36. Liu, B.-, Zhang, H., Kong, L. and Niu, D. (2022). Factorizing Historical User Actions for Next-Day Purchase Prediction, ACM Transactions on the Web (TWEB), 12(1), 1. [PDF]
  37. Tu, W.-, Johnson, E., Fujiwara, E., Gill, J., Kong, L., and Power, C. (2021). Predictive variables for peripheral neuropathy prevalence and phenotypes in HIV/AIDS: Risk variables uncovered by machine learning, AIDS, 35(11), 1785-1793. [PDF]
  38. Pietrosanu, M-., Zhang, L., Seres, P., Elkady, A., Wilman, A. Kong, L.*, and Cobzas, D*. (2021). Stable anatomy detection in multimodal imaging through sparse group regularization: a comparative study of iron accumulation in the aging brain. Frontiers in Human Neuroscience, 15: 76. [PDF]
  39. Lai, T., Zhang Z., Wang, Y.- and Kong, L. (2021). Testing independence of functional variables by angle covariance. Journal of Multivariate Analysis, Vol. 182, 104711. [PDF]
  40. Pietrosanu, M.-, Gao, J.-, Kong, L.*, Jiang, B., Niu, D. (2021). Advanced Algorithms for Penalized Quantile and Composite Quantile Regression, Computational Statistics, 36(1), 333-346. [PDF]
  41. Selvaratnam, S.-, Kong, L. and Wiens, P. (2021). Model-robust designs for nonlinear quantile regression, Statistical Methods in Medical Research, 30 (1): 221-232. [PDF]
  42. Tang, Q., Kong, L., Karunamuni, R. and Ruppert, D. (2021). Partial Functional Partially Linear Single Index Model. Statistica Sinica, , 31 (1), 107-133. [PDF]
  43. Kashlak, A. and Kong, L. (2021). Nonasymptotic estimation and support recovery for high dimensional sparse covariance matrices, STAT, 10 (1): e316. [PDF]
  44. Su, T.-, Wang, Y.-, Liu, Y.-, Branton, W. G., Asahchop, E., Power, C., Jiang, B., Kong, L.*, Tang, N*. (2020). Sparse multicategory generalized distance weighted discrimination in ultra-high dimensions. Entropy. 22(11), 1257 [PDF]
  45. Liu, B.-, Niu, D., Han, F., Kong, L., Lai, K., and Xu, Y. (2020). Story Forest: Extracting Events and Telling Stories from Breaking News, ACM Transactions on Knowledge Discovery from Data (TKDD), 14 (3), Article 31. [PDF]
  46. Tu, W.-, Chen, P., Koenig, N., Gomez, D., Fujiwara, E., Gill, J., Kong, L.*, and Power, C.*. (2020) . Machine learning models reveal neurocognitive impairment type and prevalence are associated with distinct variables in HIV/AIDS. Journal of NeuroVirology, Vol. 26, No.1, 41-51. [PDF]
  47. Subhan, F., Shulman, L.-, Yuan, Y., McCargar, L., Kong, L., and Bell, R. (2019). Fat Mass istribution and Accretion During Pregnancy and Early Postpartum - A Prospective Study of Albertan Women. BMJ Open, Vol. 9, No. 7, e026908. [PDF]
  48. Karunamuni, R., Kong, L. and Tu, W.- (2019). Efficient Robust Doubly Adaptive Regularized Regression. Statistical Methods in Medical Research, Vol. 28, No. 7, 2210-2226. [PDF]
  49. Liu, B.-, Mavrin, B.-, Kong, L., and Niu, D. (2019). Spatial Data Reconstruction via ADMM and Spatial Spline Regression. Applied Sciences, Vol. 9, No. 9, 1733. [PDF]
  50. Yu, D.+-, Zhang, L.+, Jiang, B., Mizera, I. and Kong, L.* (2019). Sparse Wavelet Estimation in Quantile Regression with Multiple Functional Predictors. Computational Statistics & Data Analysis, Vol. 136, 12-29. [PDF]
  51. Tu, W.-, Kong, L., Karunamuni, R., Butcher, K., Zheng, L.-, and McCourt, R. (2019). Non-local Spatial Clustering in Automated Brain Hematoma and Edema Segmentation. Applied Stochastic Models in Business and Industry, Vol. 35, 321-329. [PDF]
  52. Han, P., Kong, L.*, Zhao, J. and Zhou, X. (2019). A General Framework for Quantile Estimation with Incomplete Data. Journal of Royal Statistical Society: Series B. Vol. 81, P. 2, 305-333. [PDF]
  53. Wang, Y.-, Kong, L.*, Jiang, B., Zhou, X., Yu, S.-, Zhang, L., and Heo, G. (2019). Wavelet-based Lasso in Functional Linear Quantile Regression. Journal of Statistical Computation and Simulation, Vol. 89, No. 6, 1111-1130. [PDF]
  54. Nathoo, F., Kong, L., and Zhu, H. (2019). A Review of Statistical Methods in Imaging Genetics. Canadian Journal of Statistics, Vol. 47, No. 1, 108-131. [PDF]
  55. Asahchop, E., Branton, W., Krishnan,A., Chen, P., Yang, D.-, Kong, L., Zochodne, D., Brew, B., Gill, J., and Power, C. (2018).microRNA-455-3p predicts HIV-associated symptomatic distal sensory polyneuropathy and suppresses NGF expression in human neurons. JCI insight, 3(23): e122450. [PDF]
  56. Zhang, L., Cobza, B., Wilman, A. And Kong, L. (2018). Significant Anatomy Detection through Sparse Classification: A Comparative Study. IEEE Transition in Medical Imaging, Vol. 37, No. 1, 128-137. [PDF]
  57. Che, M.-, Kong, L., Bell, R. and Yuan, Y. (2017). Trajectory Modeling of Gestational Weight: a Functional Principal Component Analysis Approach. PLoS ONE, Vol. 12, No. 10, e0186761. [PDF]
  58. Tang, Q. and Kong, L. (2017). Quantile regression in functional linear semiparametric model. Statistics, Vol. 51, No. 6, 1342-1358. [PDF]
  59. Yu, D.-, Kong, L.* and Mizera, I. (2016). Partial Functional Linear Quantile Regression for Neuroimaging Data Analysis. Neurocomputing, Vol. 195, 74-87. [PDF]
  60. He, Q.+, Kong, L.+, Wang, Y. Wang, S. Chan, T. and Holland, E. (2016). Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits. Computational Statistics & Data Analysis, Vol. 95, 222-239. [PDF]
  61. Kong, L. and Wiens, P. D. (2015). Model-Robust Designs for Quantile Regression. Journal of the American Statistical Association, Vol. 110, No. 509, 233-245. [PDF]
  62. Zhu, H., Fan, J. and Kong, L. (2014). Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities. Journal of the American Statistical Association, Vol. 109, No. 507, 1084-1098. [PDF]
  63. Ford, A., An, H., Kong, L., Zhu, H., Vo, K., Powers, W., and Lin, W. (2014). Clinically-relevant reperfusion in acute ischemic stroke: MTT performs better than Tmax and TTP. Translational Stroke Research, Vol. 5, 415-421. [PDF]
  64. Calderon-Garciduenas, L., Mora-Tiscareno, A., Torres-Jardon, R., Pean-Cruz, B., Palacios-Lopez, C., Zhu, H., Kong, L., Mendoza-Mendoza, N., Montesinos-Correa, H., Romero, L., Valencia-Salazar, G., Cross, J., Kavanaugh, M., Medina-Cortina, H., Frenk, S. (2013). Exposure to Urban Air Pollution and Bone Health in Clinically Healthy Six Years Old Children. Archives of Industrial Hygiene and Toxicology, Vol. 64(1), 23-34. [PDF]
  65. Zhu, H., Li, R. and Kong, L. (2012). Multivariate Varying Coefficient Models for Functional Responses. Annals of Statistics, Vol. 40, No. 5, 2634-2666. [PDF]
  66. Kong, L. and Mizera, I. (2012). Quantile Tomography: Using Quantiles with Multivariate Data. Statsitica Sinica, Vol. 22, No. 4. 1589-1610. [PDF]
  67. Zhu, H., Kong, L., Li, R., Styner, M., Gerig, G., Li, Y. and Gilmore, JH. (2011). FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics. Neuroimage, Vol. 56(3), 1412-1425. [PDF]
  68. Kong, L. and Zuo, Y. (2010). Depth Contours Characterize the Underlying Distribution. Journal of Multivariate Analysis, Vol. 101(9), 2222-2226. [PDF]
  69. Kong, L. and Mizera, I. (2010). Discussion of "Multivariate Quantiles and Multiple-Output Regression Quantiles: From L1 Optimization to Halfspace Depth". Annals of Statistics, Vol. 38, No. 2, 685-693. [PDF]

Peer-Reviewed Proceedings

  1. Zhao, S., Cui, W., iang, B., Kong, L., and Yan, X. (2024). Optimal Smooth Approximation for Quantile Matrix Factorization, Proceedings of the 38th AAAI Conference on Artificial Intelligence 2024 (acceptance rate: 21.3%).
  2. Jiang, Y., Liu, Y.-, Yan, X., Charest, A-S., Kong, L., Jiang, B. (2024). Analysis of Differentially Private Synthetic Data: A Measurement Error Approach, Proceedings of the 38th AAAI Conference on Artificial Intelligence 2024 (acceptance rate: 21.3%).
  3. Jiang, Y., Chang, X., Liu, Y.-, Ding, L.-, Kong, L., and Jiang, B.(2023). Gaussian Differential Privacy on Riemannian Manifolds, Proceeding of the 37th Conference on Neural Information Processing Systems (NeurIPS) (acceptance rate: 26.1%).
  4. Feng, W., Li, X., Kong, L., Jiang, B. and Yan, X. (2023). P-learning for Two-sided Markets, KDD-23 Workshop on Decision Intelligence and Analytics for Online Marketplaces.
  5. Sun, K.-, Zhao, Y.-, Jui, S. and Kong, L.* (2023).Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023). (acceptance rate: 24%)
  6. Liu, Y.-, Hu, Q.-, Ding, L.-, Jiang, B. and Kong, L.* (2023). Online Local Differential Private Quantile Inference via Self-normalization, 2023 Proceedings of the Fortieth International Conference on Machine Learning (ICML 2023). (acceptance rate: 21.5%)
  7. Kiechie, J., Slessor, J., Cobzas, D., Pietrosanu., Beaulieu, C., and Kong, L. (2023). Explaining anatomical shape variability: Supervised disentangling with a variational graph autoencoder, IEEE International Symposium on Biomedical Imaging (ISBI 2023)
  8. Liu, P., Liu, Y., Zhu, R., Kong, L., Jiang, B., and Niu, D. (2023). Optimal Smooth Approximation for Quantile Matrix Factorization. SIAM International Conference on Data Mining (SDM 2023), 595-603, (acceptance rate: 27.4%)
  9. Cui, W., Ji, X., Kong, L., and Yan, X. (2023). Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators. The 2023 AAAI Conference on Artificial Intelligence (AAAI 2023) (acceptance rate: 19.7%)
  10. Liu, M.-, Ding, L.-, Yu, D., Liu, W., Kong, L.* and Jiang, B. (2022). Conformalized Fairness via Quantile Regression, Proceeding of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), (acceptance rate: 25.6%).
  11. Liu, Y.-, Sun, K.-, Jiang, B., and Kong, L.* (2022). Identification, Amplification, and Measurement: A bridge to Gaussian Differential Privacy, Proceeding of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), (acceptance rate: 25.6%).
  12. Yang, J., Guo, W., Liu, B., Yu, Y., Wang, C., Kong, L., Niu, D., Wen, Z. (2022). TAG: Toward Accurate Social Media Content Tagging with a Concept Graph, The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), (acceptance rate: 15%).
  13. Lu, D., Chen, R., Sui, S., Han, Q., Kong, L., and Wang, Y. (2022). MTGnet: Multi-Task Spatiotemporal Graph Convolutional Networks for Air Quality Prediction, 2022 International Joint Conference on Neural Networks (IJCNN 2022).
  14. Wang, Y.-, Pan, B.-, Tu, W., Liu, P., Jiang, B., Gao, C., Lu, W. Jiu, S. and Kong, L.* (2022). Sample Average Approximation for Dependent Data: Performance Guarantee and Tractability, The 2022 AAAI Conference on Artificial Intelligence (AAAI 2022), (acceptance rate: 15%). [PDF]
  15. Ding, L.-, Yu, D., Xie, J.-, Guo, W.-, Hu, S., Liu, M.-, Kong, L.*, Dai, H., Bao, Y., and Jiang, B. (2022). Gender Debiasing Word Embedding with Oracle Semantic Information Retained Using Causal Inference, The 2022 AAAI Conference on Artificial Intelligence (AAAI 2022), (acceptance rate: 15%). [PDF]
  16. Wang, Y.+-, Sun, K.+-, Liu, Y.-, Zhao, Y.-, Pan, B.-, Jui, S., Jiang, B., and Kong, L.* (2021). Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization, Proceeding of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021), (acceptance rate: 26%). [PDF]
  17. Mills, K., Han, F., Niu, D., Lian S., Jui, S., Salameh, M., Rezaei, S. and Kong, L. (2021). L2-NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning, Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021). (acceptance rate: 21.7%)[PDF]
  18. Tu, W.-, Liu, P.-, Liu, Y.-, Kong, L.*, Li, G., Jiang, B., Yao, H., and Jui, S. (2021). Nonsmooth Low-rank Matrix Recovery: Methodology, Theory and Algorithm, Proceedings of the Future Technologies Conference (FTC 2021), 848-862.[PDF]
  19. Hu, Y., Liu, P.-, Ge, K. Kong, L., Jiang, B., and Niu, D. (2020). Learning Privately over Distributed Features: An ADMM Sharing Approach. 2020 34th Conference on Neural Information Processing Systems, Workshop on Scalability, Privacy, and Security in Federated Learning (NeurIPS-SpicyFL 2020). [PDF]
  20. Liu, P.+- , Tu, W.+- , Zhao, J., Liu, Y., Kong, L.* , Li, G., Jiang, B., Tian, G., and Yao, H. (2019). M-estimation in Low-rank Matrix Factorization: a General Framework. 2019 IEEE 19th International Conference on Data Mining (ICDM 2019). (acceptance rate: 9.08%) [PDF]
  21. Tu, W.+-, Yang, D+-., Che, M.-, Shi, Q.-, Li, G., Tian, G., and Kong, L.*. (2019). Ensemble-based Ultrahigh-dimensional Variable Screening, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19). (acceptance rate: 17.9%) [PDF]
  22. Mavrin, B.-, Zhang, S., Yao, H., Kong, L.., Wu, K., and Yu, Y. (2019). Distributional Reinforcement Learning for Efficient Exploration, Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML-19). (acceptance rate: 22.6%) [PDF]
  23. Mavrin, B.-, Zhang, S., Yao, H., and Kong, L.. (2019). Exploration in the face of Parametric and Intrinsic Uncertainties, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19). [PDF]
  24. Zhang, S., Mavrin, B.-, Kong, L. Liu, B. and Yao, H. (2019). QUOTA: The Quantile Option Architecture for Reinforcement Learning, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-19). (acceptance rate: 16.2%) [PDF]
  25. Liu, B.-, Mavrin, B.-, Niu, D., and Kong, L. (2017). Recover Fine-Grained Spatial Data from Coarse Aggregation. 2017 IEEE 17th International Conference on Data Mining (ICDM 2017). (acceptance rate: 19.9%) [PDF]
  26. Liu, B.-, Niu, D., Lai, K., Kong, L. and Xu, Y. (2017). Growing Story Forest Online from Massive Breaking News. Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017). [PDF]
  27. Zhang, L., Cobza, D., Wilman, A., and Kong, L. (2017). An unbiased penalty for sparse classification with application to neuroimaging data. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017), Lecture Notes in Computer Science, 2017, Springer Berlin/Heidelberg, Vol. 10435, 55-63. (acceptance rate: 32.2%) [PDF]
  28. Zhu, R., Niu, D., Kong, L., and Li, Z. (2017). Expectile Matrix Factorization for Extreme Data Analysis. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 259-265. (acceptance rate: 24.6%) [PDF]
  29. Liu, B.+-, Mavrin, B.+-, Niu, D., and Kong, L. (2016). House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. 2016 IEEE 16th International Conference on Data Mining (ICDM 2016), 2047-2052. (acceptance rate: 19.6%) [PDF]
  30. Luo, X., Zhu, L., Kong, L., and Zhu, H. (2015). Multivariate Varying Coefficient Models for DTI Tract Statistics. Functional Nonlinear Mixed Effects Models for Longitudinal Image Data, Information Processing in Medical Imaging (IPMI 2015), Lecture Notes in Computer Science}, Springer Berlin/Heidelberg, Vol. 9123, 794-805. (acceptance rate: 32.3%) [PDF]
  31. Zhu, H., Styner, M., Li, Y., Kong, L., Shi, W., Lin, W., Coe, C. and Gilmore, JH.(2010). Multivariate Varying Coefficient Models for DTI Tract Statistics. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010), Lecture Notes in Computer Science, 2010, Springer Berlin/Heidelberg, Vol. 6361, 690-697. (acceptance rate: 31.9%) [PDF]

Peer-Reviewed Book Chapters

  1. Konnikov, A., Rets, I., Hughes, K., Alshehabi Al-Ani, J., Denier, N., Ding, L., Hu, S., Hu, Y., Jiang, B., Kong, L. , Tarafdar, M. and Yu, D. (2022). Responsible AI for labour market equality (BIAS), In: How to Manage International Multidisciplinary Research Projects. Edward Elgar, Cheltenham. 75-87.
  2. Wang, Y.-, Lai, T., Jiang, B., Kong, L. , and Zhang Z. (2022). Functional linear regression for partially observed functional data , Advances and Innovations in Statistics and Data Science, Editors: He, W., Wang, L., Chen, J. and Lin, C., Springer, 137-158.

Patent

  1. Jiang, Y., Jiang, B., Kong, L. , and Emam, K el. (2022). Re-identification risk assessment using a synthetic estimator, US Patent App. 17/400,484. [PDF]

Non Peer-Reviewed Proceedings

  1. Kong, L. , and Wiens, D. (2016). Nonlinear Quantile Regression Design, Joint Statistical Meeting Proceedings, 3602-3609. [PDF]

Technical Reports

  1. Squires, J., Kong, L., Brooker, S.Mitchell, A. Sales, A. and Estabrooks, C. (2009). Examining the Role of Context in Alzheimer Care Centers: A Pilot Study Technical Report. (Report No. 08-04-TR). Edmonton, AB, Faculty of Nursing, University of Alberta. (ISBN:978-1-55195-237-6). [PDF]
  2. Estabrooks, C., Squires, J., Adachi, A., Kong, L. and Norton, P. (2008). Utilization of Health Research in Acute Care Settings in Alberta Technical Report. (Report No. 08-01-TR). Edmonton, AB, Faculty of Nursing, University of Alberta. (ISBN: 978-1-55195-231-4). [PDF]
  3. Hutchinson, A., Adachi, A., Kong, L., Estabrooks, C. and Steves, B. (2008). Context and Research Use in the Care of Children: A Pilot Study Project 2 CIHR Team in Children's Pain Technical Report. (Report No. 08-03-TR). Edmonton, AB, Faculty of Nursing, University of Alberta. (ISBN: 978-1-55195-236-9). [PDF]

PhD Thesis

  1. Kong, L. (2009). On multivariate quantile regression: directional approach and application with growth charts. Ph.D. thesis, Advisor: Ivan Mizera, University of Alberta. [PDF]