Publications


Google Scholar Profile

Pre-prints

  1. Heng, Q., Chi, E. C. and Liu, Y. (2022). Tucker-L_2E: Robust Low-rank Tensor Decomposition with the L_2 Criterion. arXiv:2208.11806 [stat.ME].
  2. Zhou, X., Chi, E. C. and Zhou, H. (2022). Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation. arXiv:2205.07378 [stat.ME].
  3. Heng, Q., Zhou, H. and Chi, E. C. (2022). Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo. arXiv:2201.00092 [stat.CO].
  4. Liu, X., Molstad, A. J. and Chi, E. C. (2022). A Convex-Nonconvex Strategy for Grouped Variable Selection. arXiv:2111.15075 [stat.ME].


Peer Reviewed Publications

  1. Liu, X., Chi, E. C. and Lange, K. (2022). A Sharper Computational Tool for L_2E Regression. Technometrics, In press. doi:10.1080/00401706.2022.2118172
  2. Chi, J. T. and Chi, E. C. (2022). A User-Friendly Computational Framework for Robust Structured Regression with the L_2 Criterion. Journal of Computational and Graphical Statistics, In press. doi:10.1080/10618600.2022.2035232
  3. Zhang, M., Mishne, G. and Chi, E. C. (2022). Multi-scale Affinities with Missing Data: Estimation and Applications. Statistical Analysis and Data Mining, 15(3), 303–313. doi:10.1002/sam.11561
  4. Liu, X., Vardhan, M., Wen, Q., Das, A., Randles, A. and Chi, E. C. (2021). An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions. Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
  5. Zhou, W., Yi, H., Mishne, G. and Chi, E. C. (2021). Scalable Algorithms for Convex Clustering. Proceedings of the 2021 IEEE Data Science and Learning Workshop (DSLW 2021), 1–6. doi:10.1109/DSLW51110.2021.9523411
  6. Yi, H., Huang, L., Mishne, G. and Chi, E. C. (2021). COBRAC: A fast implementation of convex biclustering with compression. Bioinformatics. doi:10.1093/bioinformatics/btab248
  7. Chi, E. C. (2021). Discovering Geometry in Data Arrays. Computing in Science and Engineering, 23(6), 42–51. doi:10.1109/MCSE.2021.3120039
  8. Vardhan, M., Gounley, J., Chen, S. J., Chi, E. C., Kahn, A. M., Leopold, J. A. and Randles, A. (2021). Non-invasive characterization of complex coronary lesions. Nature Scientific Reports, 11(1), 8145. doi:10.1038/s41598-021-86360-6
  9. Feng, Y., Xiao, L. and Chi, E. C. (2021). Sparse Single Index Models for Multivariate Responses. Journal of Computational and Graphical Statistics, 30(1), 115–124. doi:10.1080/10618600.2020.1779080
  10. Brantley, H. L., Guinness, J. and Chi, E. C. (2020). Baseline drift estimation for air quality data using quantile trend filtering. The Annals of Applied Statistics, 14(2), 585–604. doi:10.1214/19-AOAS1318
  11. Stanley III, J. S., Chi, E. C. and Mishne, G. (2020). Multiway Graph Signal Processing on Tensors: Integrative Analysis of Irregular Geometries. IEEE Signal Processing Magazine, 37(6), 160–173. doi:10.1109/MSP.2020.3013555
  12. Rhyne, J., Jeng, X. J., Chi, E. C. and Tzeng, J.-Y. (2020). FastLORS: Joint modelling for expression quantitative trait loci mapping in R. Stat, 9(1), e265. doi:10.1002/sta4.265
  13. Chi, E. C., Gaines, B. J., Sun, W. W., Zhou, H. and Yang, J. (2020). Provable Convex Co-clustering of Tensors. Journal of Machine Learning Research, 21(214), 1–58.
  14. Min, E. J., Chi, E. C. and Zhou, H. (2019). Tensor Canonical Correlation Analysis. Stat, 8(1), e253. doi:10.1002/sta4.253
  15. Lusch, B., Chi, E. C. and Kutz, J. N. (2019). Shape Constrained Tensor Decompositions. 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA) in (pp. 287–297). doi:10.1109/DSAA.2019.00044
  16. Mishne, G., Chi, E. C. and Coifman, R. R. (2019). Co-manifold learning with missing data. K. Chaudhuri and R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning in , Proceedings of Machine Learning Research (Vol. 97, pp. 4605–4614). Long Beach, California, USA: PMLR.
  17. Chi, E. C. and Li, T. (2019). Matrix Completion from a Computational Statistics Perspective. WIREs Computational Statistics, e1469. doi:10.1002/wics.1469
  18. Chi, E. and Steinerberger, S. (2019). Recovering Trees with Convex Clustering. SIAM Journal on Mathematics of Data Science, 1(3), 383–407. doi:10.1137/18M121099X
  19. Chi, E. C., Hu, L., Saibaba, A. K. and Rao, A. U. K. (2019). Going Off the Grid: Iterative Model Selection for Biclustered Matrix Completion. Journal of Computational and Graphical Statistics, 28(1), 36–47. doi:10.1080/10618600.2018.1482763
  20. Xu, J., Chi, E. C., Yang, M. and Lange, K. (2018). A Majorization-Minimization Algorithm for Split Feasibility Problems. Computational Optimization and Applications, 71(3), 795–828. doi:doi:10.1007/s10589-018-0025-z
  21. Chi, E. C., Allen, G. I. and Baraniuk, R. G. (2017). Convex Biclustering. Biometrics, 73(1), 10–19. doi:10.1111/biom.12540
  22. Xu, J., Chi, E. C. and Lange, K. (2017). Generalized Linear Model Regression under Distance-to-set Penalties. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan and R. Garnett (Eds.), Advances in Neural Information Processing Systems 30 in (pp. 1385–1395). Curran Associates, Inc.
  23. Long, J. P., Chi, E. C. and Baraniuk, R. G. (2016). Estimating a common period for a set of irregularly sampled functions with applications to periodic variable star data. The Annals of Applied Statistics, 10(1), 165–197. doi:10.1214/15-AOAS885
  24. Chi, J. T., Chi, E. C. and Baraniuk, R. G. (2016). k-POD: A Method for k-Means Clustering of Missing Data. The American Statistician, 70(1), 91–99. doi:10.1080/00031305.2015.1086685
  25. Chi, E. C. and Lange, K. (2015). Splitting Methods for Convex Clustering. Journal of Computational and Graphical Statistics, 24(4), 994–1013. doi:10.1080/10618600.2014.948181
  26. Chen, G. K., Chi, E. C., Ranola, J. M. O. and Lange, K. (2015). Convex Clustering: An Attractive Alternative to Hierarchical Clustering. PLoS Computational Biology, 11(5), e1004228. doi:10.1371/journal.pcbi.1004228
  27. Chi, E. C., Zhou, H., Chen, G. K., Ortega-Del-Vecchyo, D. and Lange, K. (2015). Genotype Imputation via Matrix Completion. Genome Research. doi:10.1101/gr.145821.112
  28. Chi, E. C. and Lange, K. (2014). Stable Estimation of a Covariance Matrix Guided by Nuclear Norm Penalties. Computational Statistics & Data Analysis, 80(0), 117–128. doi:10.1016/j.csda.2014.06.018
  29. Chi, E. C., Zhou, H. and Lange, K. (2014). Distance Majorization and Its Applications. Mathematical Programming Series A, 146(1-2), 409–436. doi:10.1007/s10107-013-0697-1
  30. Lange, K., Chi, E. C. and Zhou, H. (2014). A brief survey of optimization for statisticians. International Statistical Review, 82(1), 46–70. doi:10.1111/insr.12022
  31. Lange, K., Chi, E. C. and Zhou, H. (2014). A brief survey of optimization for statisticians: Rejoinder. International Statistical Review, 82(1), 81–89. doi:10.1111/insr.12030
  32. Chi, E. C. and Scott, D. W. (2014). Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion. Journal of Computational and Graphical Statistics, 23(1), 111–128. doi:10.1080/10618600.2012.737296
  33. Chi, E. C. and Lange, K. (2014). A Look at the Generalized Heron Problem through the Lens of Majorization-Minimization. The American Mathematical Monthly, 121(2), 95–108. doi:10.4169/amer.math.monthly.121.02.095
  34. Chi, E. C., Allen, G. I., Zhou, H., Kohannim, O., Lange, K. and Thompson, P. M. (2013). Imaging genetics via sparse canonical correlation analysis. Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on in (pp. 740–743). doi:10.1109/ISBI.2013.6556581
  35. Chi, E. C. and Kolda, T. G. (2012). On Tensors, Sparsity, and Nonnegative Factorizations. SIAM Journal on Matrix Analysis and Applications, 33(4), 1272–1299. doi:10.1137/110859063
  36. Chi, E. C., Mende, S. B., Fok, M.-C. and Reeves, G. D. (2006). Proton auroral intensifications and injections at synchronous altitude. Geophysical Research Letters, 33, 6104. doi:10.1029/2005GL024656
  37. Gupta, R., Chi, E. and Walrand, J. (2005). Different Algorithms for Normal and Protection Paths. Journal of Network System Management, 13(1), 13–33. doi:10.1007/s10922-005-1845-6
  38. Chi, E., Fu, M. and Walrand, J. (2004). Proactive resource provisioning. Computer Communications, 27(12), 1174–1182. doi:10.1016/j.comcom.2004.02.019
  39. Gupta, R., Chi, E. and Walrand, J. (2004). Sharing Normal Bandwidth During a Failure. Proceedings Seventh INFORMS Telecommunications Conference, Boca Raton, Florida in .
  40. Chi, E., Fu, M. and Walrand, J. (2003). Proactive Resource Provisioning for Voice over IP. Proceedings SPECTS 2003, Montreal, Canada in . doi:10.1.1.69.4215
  41. Gupta, R., Chi, E. and Walrand, J. (2003). Different Algorithms for Normal and Protection Paths, Banff, Canada. Proceedings DRCN 2003 in . doi:10.1109/DRCN.2003.1275356
  42. Thomsen, S. L., Baldwin, B., Chi, E., Ellard, J. and Schwartz, J. A. (1997). Histopathology of laser skin resurfacing. Proceedings of SPIE Vol 2970 in . doi:10.1117/12.275056


Refereed Book Chapters and Other Refereed Articles

  1. Chi, E. C. (2018). Proximal Methods for Penalized Regression. doi:10.1002/9781118445112.stat08052
  2. Hu, Y., Chi, E. C. and Allen, G. I. (2016). Splitting Methods in Communication and Imaging, Science and Engineering. W. Y. S. Osher and R. Glowinski (Eds.), . Springer. doi:doi:10.1007/978-3-319-41589-5


Technical Reports and Other Papers

  1. Chi, E. C. and Lange, K. (2012, March). Techniques for Solving Sudoku Puzzles. arXiv:1203.2295v3 [math.OC].
  2. Chi, E. C. and Kolda, T. G. (2011). Making Tensor Factorizations Robust to Non-Gaussian Noise ( No: SAND2011-1877). Sandia National Laboratories, Albuquerque, NM and Livermore, CA.
  3. Chi, E. C. and Kolda, T. G. (2010, October). Making Tensor Factorizations Robust to Non-Gaussian Noise (Contributed paper at the NIPS Workshop on Tensors, Kernels, and Machine Learning, Whistler, BC, Canada, December 10, 2010). arXiv:1010.3043.


Tutorials

  1. Chi, J. T. and Chi, E. C. (2014, March). Getting to the Bottom of Matrix Completion and Nonnegative Least Squares with the MM Algorithm. StatisticsViews.com.
  2. Chi, J. T. and Chi, E. C. (2014, January). Getting to the Bottom of Regression with Gradient Descent. StatisticsViews.com.