Xingye Qiao

Manuscripts

• Cohen, A., Yu, L., Qiao, X., and Tong, X., “Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning”.
  [PDF] [arXiv]

• Barrett, J., Meng, H., Zhang, Z., Chen, S., Zhao, L., Alsop, D., Qiao, X., and Dai, W., ‘‘Improving spectral clustering method for accurate detection of brain resting-state networks.’’
  [PDF]

Publications

• Wang, W. and Qiao, X. (2022+), “Set-Valued Support Vector Machine with Bounded Error Rates,” Journal of the American Statistical Association, accepted.

• Meng, H. and Qiao, X. (2022), “Augmented Direct Learning for Conditional Average Treatment Effect Estimation with Double Robustness,” Electronic Journal of Statistics, 16(1), pp. 3523–3560. Earlier version: [PDF] [arXiv]

• Khosravi-Kamrani, P., Qiao, X., Zanardi, G., Wiesen, C., Slade, G., and Frazier-Bowers, S. (2022), “A Machine Learning Approach to Determine Prognosis of Class III Malocclusion Patients,” American Journal of Orthodontics & Dentofacial Orthopedics, 161, 1, pp. e1–e11.
  [PDF]

• Ren, Z., Jung, S. and Qiao, X. (2022), “Covariance-engaged Classification of Sets via Linear Programming,” Statistica Sinica, 32, pp. 1515–1540.
  [PDF] [arXiv]

• Dai, W., Wagh, S., Chettiar, S., Zhou, G., Roy, R., Qiao, X., Visich, P., and Hoffman, E. (2021), “Blunted circadian cortisol in children is associated with poor cardiovascular health and may reflect circadian misalignment,” Psychoneuroendocrinology, 129, 105252.
  [PDF]

• Duan, J., Qiao, X., and Cheng, G. (2020), “Statistical Guarantees of Distributed Nearest Neighbor Classification,” In Advances in Neural Information Processing Systems, 33, pp. 229–240..
  [PDF] [arXiv]

• Meng, H., Zhao, Y., Fu, H., and Qiao, X. (2020), “Near-optimal Individualized Treatment Recommendations”, Journal of Machine Learning Research, 21, 183, pp. 1–28.
  [PDF] [arXiv]

• Randall, H., Artemiou, A., and Qiao, X. (2020), “Sufficient Dimension Reduction based on Distance Weighted Discrimination,” Scandinavian Journal of Statistics, 2020, pp. 1–26.
  [PDF]

• Cheng, C.W., Qiao, X., and Cheng, G. (2020), “Mutual Transfer Learning for Massive Data,” In Proceedings of the Thirty-seventh International Conference on Machine Learning, 119, pp. 1800–1809.
  [PDF]

• Curtis, T., Shi, M., and Qiao, X. (2020), “Patience is not always a virtue: effects of terrain complexity on the host-seeking behaviour of adult blacklegged ticks, Ixodes scapularis, in the presence of a stationary host,” Medical and Veterinary Entomology, 34, 3, pp. 309–315.
  [PDF]

• Beltre, A., Zaman, S., Chiu, K., Pamidighantam, S., Qiao, X., and Govindaraju, M. (2019), “Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway,” In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), pp. 1–8.
  [PDF]

• Qiao, X., Duan, J. and Cheng, G. (2019), “Rates of Convergence for Large-scale Nearest Neighbor Classification,” In Advances in Neural Information Processing Systems, 32, pp. 10768–10779.
  [PDF] [arXiv]

• Cohen, A., Qiao, X., Yu, L., Way, E. and Tong, X. (2019), “Diverse Exploration via Conjugate Policies for Policy Gradient Methods,” In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 33, pp. 3404–3411.
  [PDF] [arXiv]

• Weng, H., Feng, Y. and Qiao, X. (2019), “Regularization after retention in ultrahigh dimensional linear regression models,” Statistica Sinica, 29, pp. 387-407.
  [PDF] [arXiv]

• Chen, Y., Duan, W., Sehrawat, P., Chauhan, V., Alfaro, F., Gavrieli, A., Qiao, X., Novak, V., Dai, W. (2019), “Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study,” Journal of Magnetic Resonance in Imaging, 49, pp. 834–844.
  [PDF]

• Wang, W. and Qiao, X. (2018), “Learning Confidence Sets using Support Vector Machines,” In Advances in Neural Information Processing Systems, 31, pp. 4929-4938.
  [PDF] [arXiv]

• Barrett, J., Meng, H., Chen, S., Zhao, L., Alsop, D., Qiao, X., Dai, W. (2018), “Resting-state Brain Networks using Spectral Clustering Analysis,” Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, France, June, 2018, In Proceedings of the International Society for Magnetic Resonance in Medicine, 26, 2746.
  [PDF]

• Zhang, C., Wang, W. and Qiao, X. (2018), “On Reject and Refine Options in Multicategory Classification,” Journal of the American Statistical Association, 113 (522), pp. 730–745.
  [PDF] [arXiv]

• Lu, Q. and Qiao, X. (2018), “Sparse Fisher's Linear Discriminant Analysis for Partially Labeled Data,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 11(1), pp. 17-31.
  [PDF] [arXiv]

• Sangrody, H., Zhou, N., Qiao, X. (2017), “Probabilistic Models for Daily Peak Loads at Distribution Feeders,” 2017 IEEE PES General Meeting, July 16-20, 2017, Chicago, IL, USA, pp. 1–5.
  [PDF]

• Qiao, X. (2017), “Noncrossing Ordinal Classification,” Statistics and Its Interface, 10, 2, pp.187–198.
  [PDF] [arXiv]

• Sun, W., Qiao, X. and Cheng, G. (2016), “Stabilized Nearest Neighbor Classifier and Its Statistical Properties,” Journal of the American Statistical Association, 111, 515, pp. 1254–1265. R packpage
  [PDF] [arXiv]

• Vargason, T.J., Cohn, J., Rios, D., Schultz, O., Cleary, J., Lau, D., Land, W., Schaffer, D., Li, Y., Chou, C.A., Syouri, S., Sidhu, J., Nelson, M., and Qiao, X. (2016), “A Clinical Decision Support System for Malignant Pleural Effusion Analysis,” 2016 Industrial and Systems Engineering Research Conference/IIE Annual Conference & Expo 2016, At Anaheim, CA, USA.
  [PDF] [ORB]

• Lu, Q. and Qiao, X. (2016), “Significance Analysis of High-Dimensional, Low-Sample Size Partially Labeled Data,” Journal of Statistical Planning and Inference, 176, pp. 78–94.
  [PDF] [arXiv]

• Qiao, X., Zhang, L. (2015), “Flexible High-dimensional Classification Machines and Their Asymptotic Properties,” Journal of Machine Learning Research, 16, pp. 1547–1572. Online Appendix
  [PDF] [arXiv]

• Zhang, X., Zhang, D., Jia, H., Feng, Q., Wang, D. Liang, D, Wu, X., Li, J., Tang, L., Li, Y., Lan, Z., Chen, B., Li, Y., Zhong, H., Xie, H., Jie, Z., Chen, W., Tang, S., Xu, X., Wang, X., Cai, X., Liu, S., Xia, Y., Li, J., Qiao, X., Al-Aama, J. Y., Chen, H., Wang, L., Wu, Q., Zhang, F., Zheng, W., Li, Y., Zhang, M., Luo, G., Xue, W., Xiao, L., Li, J., Chen, W., Xu, X., Yin, Y., Yang, H., Wang, J., Kristiansen, K., Liu, L., Li, T., Huang, Q., Li, Y. and Wang, J. (2015), “The Oral and Gut Microbiomes are Perturbed in Rheumatoid Arthritis and Partly Normalized after Treatment,” Nature Medicine, 21, pp. 895–905.
  [PDF]

• Qiao, X., Zhang, L. (2015), “Distance-weighted Support Vector Machine,” Statistics and Its Interface, 8, 3, pp. 331–345.
  [PDF] [arXiv]

• Wright, R., Qiao, X., Loscalzo, S., and Yu, L. (2015), “CFQI: Fitted Q-Iteration with Complex Returns,” in Proceedings of the Fourteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-15), pp. 163–170 (full paper), Istanbul, Turkey, May 2015.
  [PDF]

• Qiao, X. (2015), “Learning Ordinal Data,” Wiley Interdisciplinary Reviews: Computational Statistics (invited), 7, 5, pp. 341–346.
  [PDF]

• Wright, R., Qiao, X., Loscalzo, S. and Yu, L. (2015), “Improving Approximate Value Iteration with Complex Returns by Bounding,” in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), pp. 3087–3093 (full paper, plenary presentation), Austin, TX, January 2015.
  [PDF]

• Qiao, X., Liu, Y. and Marron, J. S. (2014), “Significance Analysis for Pairwise Variable Selection in Classification,” Statistics and Its Interface, 7, 2, pp. 263–274.
  [PDF] [arXiv]

• Jung, S. and Qiao, X. (2014), “A statistical approach to set classification by feature selection with applications to classification of histopathology images,” Biometrics, 70(3), pp. 536-545.
  [PDF] [arXiv]

• Qiao, X. (2014), “Variable selection using \(L_q\) penalties,” Wiley Interdisciplinary Reviews: Computational Statistics (invited), 6, 3, pp. 177–184.
  [PDF]

• Land, W.H., Ma, X., Barnes, E., Qiao, X., Heine, J., Masters, T. and Park, J.W. (2012), “PNN/GRNN Ensemble Processor Design for Early Screening of Breast Cancer,” Procedia Computer Science, Elsevier, 2012, Vol. 12, pp. 438–443.
  [PDF]

• Schaffer, J.D., Park, J.W, Barnes, E., Lu, Q., Qiao, X., Deng, Y., Li, Y. and Land, W.H. (2012), “GRNN Ensemble Classifier for Lung Cancer Prognosis Using Only Demographic and TNM features,” Procedia Computer Science, Elsevier, 2012, Vol. 12, pp. 450–455.
  [PDF]

• Land, W.H., Jr., Qiao, X., Margolis, D.E., Ford, W.S., Paquette, C.T., Perez-Rogers, J.F., Borgia, J.A. and Deng, Y. (2011), “Kernelized partial least squares for feature reduction and classification of gene microarray data,” BMC Systems Biology, 5(Suppl 3):S13.
  [PDF]

• Land, W.H., Jr., Ford, W., Park, J.W., Mathur, R., Hotchkiss, N., Heine, J., Eschrich, S., Qiao, X. and Yeatman, T. (2011), “Partial Least Squares (PLS) Applied to Medical Bioinformatics,” In Cihan H. Dagli, Sue Turner, and Latesha Zach, editors, Procedia Computer Science, volume 6 of Complex Adaptive Systems, pp. 273–278.
  [PDF]

• Land, W.H., Jr., Qiao, X., Margolis, D. and Gottlieb, R. (2011), “A new tool for survival analysis: evolutionary programmingevolutionary strategies (EPES) support vector regression hybrid using both censored non-censored (event) data”, In Cihan H. Dagli, Sue Turner, and Latesha Zach, editors, Procedia Computer Science/, volume 6 of Complex Adaptive Systems, pp. 267–272. (This paper won the best application paper award in CAS 2011.)
  [PDF]

• Margolis, D., Land, W.H., Jr., Gottlieb, R. and Qiao, X. (2011), “A complex adaptive system using statistical learning theory as an inline preprocess for clinical survival analysis”, In Cihan H. Dagli, Sue Turner, and Latesha Zach, editors, Procedia Computer Science, volume 6 of Complex Adaptive Systems, pp. 279–284.
  [PDF]

• Qiao, X., Zhang, H.H., Liu, Y., Todd, M.J. and Marron, J. S. (2010), “Weighted distance weighted discrimination and its asymptotic properties,” Journal of the American Statistical Association, 105, 489, pp. 401–414.
  [PDF]

• Qiao, X. and Liu, Y. (2009), “Adaptive weighted learning for unbalanced multicategory classification,” Biometrics, 65(1), 1, pp. 159–168.
  [PDF]