Publications


Google Scholar Profile

Pre-prints

  1. Chi, E. C., & Steinerberger, S. (2018, July). Recovering Trees with Convex Clustering. arXiv:1806.11096 [stat.ML].
  2. Rhyne, J., Chi, E. C., Tzeng, J.-Y., & Jeng, X. (2018). Fast-LORS: Joint Modeling for eQTL Mapping in R. arXiv:1805.05170 [stat.AP].
  3. Chi, E. C., Gaines, B. R., Sun, W. W., Zhou, H., & Yang, J. (2018). Provable Convex Co-clustering of Tensors. arXiv:1803.06518 [stat.ME].
  4. Lusch, B., Chi, E. C., & Kutz, J. N. (2016). Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries. arXiv:1608.04674 [stat.ML].


Journal Articles

  1. Chi, E. C., Hu, L., Saibaba, A. K., & Rao, A. U. K. (2018). Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion. Journal of Computational and Graphical Statistics. https://doi.org/doi:10.1080/10618600.2018.1482763
  2. Xu, J., Chi, E. C., Yang, M., & Lange, K. (2018). A Majorization-Minimization Algorithm for Split Feasibility Problems. Computational Optimization and Applications. https://doi.org/doi:10.1007/s10589-018-0025-z
  3. Chi, E. C., Allen, G. I., & Baraniuk, R. G. (2017). Convex Biclustering. Biometrics, 73(1), 10–19. https://doi.org/10.1111/biom.12540
  4. Long, J. P., Chi, E. C., & 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. https://doi.org/10.1214/15-AOAS885
  5. Chi, J. T., Chi, E. C., & Baraniuk, R. G. (2016). k-POD: A Method for k-Means Clustering of Missing Data. The American Statistician, 70(1), 91–99. https://doi.org/10.1080/00031305.2015.1086685
  6. Chi, E. C., & Lange, K. (2015). Splitting Methods for Convex Clustering. Journal of Computational and Graphical Statistics, 24(4), 994–1013. https://doi.org/10.1080/10618600.2014.948181
  7. Chi, E. C., Zhou, H., Chen, G. K., Ortega-Del-Vecchyo, D., & Lange, K. (2015). Genotype Imputation via Matrix Completion. Genome Research. https://doi.org/10.1101/gr.145821.112
  8. Chen, G. K., Chi, E. C., Ranola, J. M. O., & Lange, K. (2015). Convex Clustering: An Attractive Alternative to Hierarchical Clustering. PLoS Computational Biology, 11(5), e1004228. https://doi.org/10.1371/journal.pcbi.1004228
  9. Lange, K., Chi, E. C., & Zhou, H. (2014). A brief survey of optimization for statisticians. International Statistical Review, 82(1), 46–70. https://doi.org/10.1111/insr.12022
  10. Chi, E. C., & Lange, K. (2014). Stable Estimation of a Covariance Matrix Guided by Nuclear Norm Penalties. Computational Statistics & Data Analysis, 80(0), 117–128. https://doi.org/10.1016/j.csda.2014.06.018
  11. Chi, E. C., Zhou, H., & Lange, K. (2014). Distance Majorization and Its Applications. Mathematical Programming Series A, 146(1-2), 409–436. https://doi.org/10.1007/s10107-013-0697-1
  12. Lange, K., Chi, E. C., & Zhou, H. (2014). A brief survey of optimization for statisticians: Rejoinder. International Statistical Review, 82(1), 81–89. https://doi.org/10.1111/insr.12030
  13. Chi, E. C., & 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. https://doi.org/10.1080/10618600.2012.737296
  14. Chi, E. C., & Lange, K. (2014). A Look at the Generalized Heron Problem through the Lens of Majorization-Minimization. The American Mathematical Monthly, 121(2), 95–108. https://doi.org/10.4169/amer.math.monthly.121.02.095
  15. Chi, E. C., & Kolda, T. G. (2012). On Tensors, Sparsity, and Nonnegative Factorizations. SIAM Journal on Matrix Analysis and Applications, 33(4), 1272–1299. https://doi.org/10.1137/110859063
  16. Chi, E. C., Mende, S. B., Fok, M.-C., & Reeves, G. D. (2006). Proton auroral intensifications and injections at synchronous altitude. Geophysical Research Letters, 33, 6104. https://doi.org/10.1029/2005GL024656
  17. Gupta, R., Chi, E., & Walrand, J. (2005). Different Algorithms for Normal and Protection Paths. Journal of Network System Management, 13(1), 13–33. https://doi.org/10.1007/s10922-005-1845-6
  18. Chi, E., Fu, M., & Walrand, J. (2004). Proactive resource provisioning. Computer Communications, 27(12), 1174–1182. https://doi.org/10.1016/j.comcom.2004.02.019


Refereed Conference and Workshop Proceedings

  1. Xu, J., Chi, E. C., & Lange, K. (2017). Generalized Linear Model Regression under Distance-to-set Penalties. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 30 (pp. 1385–1395). Curran Associates, Inc.
  2. Chi, E. C., Allen, G. I., Zhou, H., Kohannim, O., Lange, K., & Thompson, P. M. (2013). Imaging genetics via sparse canonical correlation analysis. In Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on (pp. 740–743). https://doi.org/10.1109/ISBI.2013.6556581
  3. Gupta, R., Chi, E., & Walrand, J. (2004). Sharing Normal Bandwidth During a Failure. In Proceedings Seventh INFORMS Telecommunications Conference, Boca Raton, Florida.
  4. Chi, E., Fu, M., & Walrand, J. (2003). Proactive Resource Provisioning for Voice over IP. In Proceedings SPECTS 2003, Montreal, Canada. https://doi.org/10.1.1.69.4215
  5. Gupta, R., Chi, E., & Walrand, J. (2003). Different Algorithms for Normal and Protection Paths, Banff, Canada. In Proceedings DRCN 2003. https://doi.org/10.1109/DRCN.2003.1275356
  6. Thomsen, S. L., Baldwin, B., Chi, E., Ellard, J., & Schwartz, J. A. (1997). Histopathology of laser skin resurfacing. In Proceedings of SPIE Vol 2970. https://doi.org/10.1117/12.275056


Refereed Book Chapters and Other Refereed Articles

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


Technical Reports and Other Papers

  1. Chi, E. C., & Lange, K. (2012, March). Techniques for Solving Sudoku Puzzles. arXiv:1203.2295v3 [math.OC].
  2. Chi, E. C., & 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., & 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., & 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., & Chi, E. C. (2014, January). Getting to the Bottom of Regression with Gradient Descent. StatisticsViews.com.