Jocelyn Chi

Chi, J. T., & Ipsen, I. C. F. (2021, August 11). A projector-based approach to quantifying total and excess uncertainties for sketched linear regression. INFORMATION AND INFERENCE-A JOURNAL OF THE IMA, Vol. 8. https://doi.org/10.1093/imaiai/iaab016

Qin, J., Lee, H., Chi, J. T., Drumetz, L., Chanussot, J., Lou, Y., & Bertozzi, A. L. (2021). Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 59(4), 3338–3351. https://doi.org/10.1109/TGRS.2020.3020810

Chi, J. T., & Ipsen, I. C. F. (2021). Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms. LINEAR ALGEBRA AND ITS APPLICATIONS, 624, 87–102. https://doi.org/10.1016/j.laa.2021.03.039

Chi, J. T., Ipsen, I. C. F., Hsiao, T.-H., Lin, C.-H., Wang, L.-S., Lee, W.-P., … Tzeng, J.-Y. (2021). SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data. FRONTIERS IN GENETICS, 12. https://doi.org/10.3389/fgene.2021.710055

Chi, J. T., Chi, E. C., & Baraniuk, R. G. (2016). k-POD: A Method for k-Means Clustering of Missing Data. AMERICAN STATISTICIAN, 70(1), 91–99. https://doi.org/10.1080/00031305.2015.1086685