Publications

Journal Papers

• J67. Tian-Yi Zhou and Xiaoming Huo (2024). Classification of data generated by Gaussian mixture models using deep ReLU networks. Journal of Machine Learning Research, 25(190):1-54.
• J66. Yiling Xie and Xiaoming Huo (2024). Adjusted Wasserstein distributionally robust estimator in statistical learning. Journal of Machine Learning Research, 25(148):1-40, 2024.
• J65. Namjoon Suh, Li-Hsiang Lin, and Xiaoming Huo (2024). High-dimensional multivariate linear regression with weighted nuclear norm regularization. Journal of Computational and Graphical Statistics, accepted.
• J64. Tian-Yi Zhou, Xiaoming Huo (2024). Learning ability of interpolating deep convolutional neural networks. Applied and Computational Harmonic Analysis, Volume 68, January 2024, 101582.
• J63. Fangquan Shi, Lianjie Shu, Yiling Luo, Xiaoming Huo (2023). High-dimensional sparse index tracking based on a multi-step convex optimization approach. Quantitative Finance, Volume 23, Issue 9, Pages 1361-1372.
• J62. Yujie Zhao and Xiaoming Huo (2023). Accelerate the warm-up stage in the Lasso computation via a homotopic approach. Computational Statistics & Data Analysis, Volume 184, August, 107747.
• J61. Yiling Xie, Yiling Luo, Xiaoming Huo (2023). Solving a special type of optimal transport problem by a modified Hungarian algorithm. Transactions on Machine Learning Research (TMLR), Published.
• J60. Yujie Zhao, Xiaoming Huo, and Yajun Mei (2022). Identification of partial-differential-equations-based models from noisy data via splines. Statistica Sinica, Accepted.
• J59. Yujie Zhao and Xiaoming Huo (2022). A survey of numerical algorithms that can solve the lasso problems. WIREs Computational Statistics, Published.
• J58. Yujie Zhao, Xiaoming Huo, and Yajun Mei (2022). Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition. Journal of Applied Statistics, Accepted.
• J57. Yuchen He, Namjoon Suh, Xiaoming Huo, Sungha Kang, Yajun Mei (2022). Asymptotic theory of L1-regularized PDE identification from a single noisy trajectory. SIAM/ASA Journal on Uncertainty Quantification, Vol. 10, Iss. 3. 10.1137/21M1398884.
• J56. Juan Du, Shanshan Cao, Jeffery Hunt, Xiaoming Huo, and Jianjun Shi (2022). A new sparse-learning model for maximum gap reduction of composite fuselage assembly. Technometrics, Accepted.
• J55. Cheng Huang and Xiaoming Huo (2021). A statistically and numerically efficient independence test based on random projections and distance covariance. Front. Appl. Math. Stat. – Statistics, Accepted.
• J54. Cai Yi, Yiqun Li, Xiaoming Huo, and Kwok-Leung Tsui (2021). A promising new tool for fault diagnosis of railway wheelset bearings: SSO-based Kurtogram. ISA Transactions, In press.
• J53. Kai Ni, Shanshan Cao, and Xiaoming Huo (2021). Asymptotic Convergence Rates of the Length of the Longest Run(s) in an Inflating Bernoulli Net, IEEE Transactions on Information Theory, volume 67, issue 9, pages 5922-5941, September. doi: 10.1109/TIT.2021.3097886.
• J52. Shanshan Cao, Xiaoming Huo, and Jong-Shi Pang (2022). A unifying framework of high-dimensional sparse estimation with difference-of-convex (DC) regularization. Statistical Science Sci. 37(3): 411-424, August.
• J51. Namjoon Suh, Xiaoming Huo, Eric Heim, Lee Seversky (2021) A network model that combines latent factors and sparse graphs. Statistical Analysis and Data Mining: The ASA Data Sci Journal 14(2): 97-115, April.
• J50. Sim M.K., Deng S. & Huo X. (2020) What can cluster analysis offer in investing?—Measuring structural changes in the investment universe. International Review of Economics and Finance.
• J49. Yuanyuan Zhang, Renfu Li, and Xiaoming Huo (2019). Switching-dominated stability of numerical solutions for hybrid neutral stochastic differential delay equations. Nonlinear Analysis: Hybrid Systems 33: 76-92.
• J48. Cheng Huang and Xiaoming Huo (2019). A distributed one-step estimator. Mathematical Programming, Series B, 174(1), 41-76. DOI 10.1007/s10107-019-01369-0
• J47. Xiaoming Huo and Shanshan Cao (2019). Aggregated inference. Wiley Interdisciplinary Reviews: Computational Statistics 11:1, January/February. Edited by Edward J. Wegman, Yasmin H. Said and David W. Scott.
• J46. Xiaoming Huo and Shanshan Cao (2019). Manifold-based learning: nonlinear methods. Wiley StatsRef: Statistics Reference Online. Published Online: 18 February 2019.
• J45. Xiaoming Huo and Shanshan Cao (2019). Manifold-based learning: linear methods. Wiley StatsRef: Statistics Reference Online. Published Online: 18 February 2019.
• J44. Yuanyuan Zhang, Renfu Li, Wei Zhao, and Xiaoming Huo (2018). Stochastic leader-following consensus of multi-agent systems with measurement noises and communication time-delays. Neurocomputing, 282, 136-145.
• J43. Zhijun Fang, Jenq-Neng Hwang, Xiaoming Huo, Hyo-Jong Lee, and Joachim Denzler (2017). Editorial of `emergent techniques and applications for big visual data’. Special Issue in International Journal of Digital Multimedia Broadcasting, Article ID 6468502, doi:10.1155/2017/6468502. Hindawi.
• J42a. Xiaoming Huo and Gabor J. Szekely (2017). Clarifications for “Fast computing for distance covariance” by Xiaoming Huo and Gabor J. Szekely. Technometrics, 59:1, 134-135, February.
• J42. Xiaoming Huo and Gabor J. Szekely (2016). Fast computing for distance covariance. Technometrics, 58:4, 435-447.
• J41. Fang Wang, Renfu Li, Zhikun Lei, Xuelei Sherry Ni, Xiaoming Huo, Ming Chen (2015). Kernel fusion-refinement for semi-supervised nonlinear dimension reduction. Pattern Recognition Letters, 63, 16-22, October.
• J40. Zhikun Lei, Renfu Li, Xuelei Sherry Ni, and Xiaoming Huo (2015). High-dimensional semisupervised learning via a fusion-refinement procedure. Signal Processing, 114, 171-182, September.
• J39. Huizhu Wang, Seong-Hee Kim, Xiaoming Huo, Youngmi Hur, and James Wilson (2015). Monitoring nonlinear profiles adaptively with a wavelet-based distribution-free CUSUM chart. International Journal of Production Research, 53 (15), 4648-4667, August.
• J38. JianZhou Feng, Li Song, Xiaoming Huo, Xiaokang Yang, Wenjun Zhang (2015). An optimized pixel-wise weighting approach for patch-based image denoising. IEEE Signal Processing Letters, 22 (1), Article number 6880752, Pages 115-119, January.
• J37. Yuanyuan Zhang, Renfu Li, Dinggen Li, Yang Hu, Xiaoming Huo (2014). Stabilization of the stochastic jump diffusion systems by state-feedback control. Journal of the Franklin Institute, 351 (3), 1596-1614, March.
• J36. Jianzhou Feng, Xiaoming Huo, Li Song, Xiaokang Yang, and Wenjun Zhang (2014). Evaluation of different algorithms of nonnegative matrix factorization in Temporal PsychoVisual Modulation. IEEE Transactions on Circuits and Systems for Video Technology, Vol 24 (4), 553-565, April.
• J35. Heeyoung Kim, Xiaoming Huo, Meghan Shilling, and Hy D. Tran (2014). A Lipschitz regularity-based statistical model, with applications in coordinate metrology. IEEE Transactions on Automation Science and Engineering, Vol 11 (2, ITASC7), 327-337, April.
• J34. Heeyoung Kim and Xiaoming Huo (2014). Asymptotic optimality of a multivariate version of the generalized cross validation in adaptive smoothing splines. Electronic Journal of Statistics, Vol. 8 (0), 159-183.
• J33. Heeyoung Kim, Xiaoming Huo, and Jianjun Shi (2014). A single interval-based classifier. Annals of Operations Research, 216 (1): 307-325, May.
• J32. Heeyoung Kim and Xiaoming Huo (2013). Optimal sampling and curve interpolation via wavelets. Applied Mathematics Letters 26 (7), 774-779, July.
• J31. Chengliang Wang and Xiaoming Huo (2013). Object tracking under low signal-to-noise-ratio with the instantaneous-possible-moving-position model. Signal Processing, 93 (5): 1044-1055, May.
• J30. Kaveh Bastani, Zhenyu (James) Kong, Wenzhen Huang, Xiaoming Huo, and Yingqing Zhou (2013). Fault diagnosis using an enhanced relevance vector machine (RVM) for partially diagnosable multi-station assembly processes. IEEE Transactions on Automation Science and Engineering, 10 (1): 124-136. January.
• J29. Heeyoung Kim and Xiaoming Huo (2012). Locally optimal adaptive smoothing splines. Journal of Nonparametric Statistics, 24 (3):665-680, September.
• J28. Yibiao Lu, Xiaoming Huo, and Panagiotis Tsiotras (2012). Beamlet-based graph structure for path planning using multiscale information. IEEE Trans. Automatic Control, 57 (5): 1166-1178.
• J27. Yibiao Lu, Xiaoming Huo, Oktay Arslan, and Panagiotis Tsiotras (2011). An incremental, multi-scale search algorithm for dynamic path planning with low worst case complexity. IEEE Transactions on Systems, Man, and Cybernetics, Part B Cybernetics, 41 (6): 1556-1570.
• J26. X. Huo and J. Chen (2010). Complexity of penalized likelihood estimation. Journal of Statistical Computation and Simulations. To appear.
• J25. S. B. Kim , Xiaoming Huo , and Kwok L. Tsui (2009). A finite-sample simulation study of cross validation in tree-based models. Information Technology and Management, 10 (4):223-233, December.
• J24. J. Chen and X. Huo (2009). A Hessian regularized nonlinear time series model. Journal of Computational and Graphical Statistics, 18 (3): 694-716, September.
• J23. X. Huo and X. S. Ni (2009). Detectability of convex-shaped objects in digital images, its fundamental limit and multiscale analysis. Statistica Sinica, 19 (4): 1439-1462, October.
• J22. X. Huo and A. K. Smith (2009). Matrix perturbation analysis of local tangent space alignment. Linear Algebra & Its Applications, 430: 732-746, January.
• J21. X. S. Ni and X. Huo (2009). Another look at Huber’s estimator: a new minimax estimator in regression with stochastically bounded noise. Journal of Statistical Planning & Inference, 139 (2): 503-515, February.
• J20. J. Chen, S. Deng, and X. Huo (2008). Electricity price curve modeling and forecasting by manifold learning. IEEE Trans. on Power Systems, 23 (3): 877-888, August.
• J19. X. Huo and X. S. Ni (2007). When do stepwise algorithms meet subset selection criteria? Annals of Statistics, 35 (2): 870-887, April.
• J18. X. S. Ni and X. Huo (2007). Statistical interpretation of the importance of phase information in signal and image reconstruction. Statistics and Probability Letters, 77 (4): 447-454, February.
• J17. Jie Chen and X. Huo (2006). Theoretical results on sparse representations of Multiple Measurement Vectors. IEEE Trans. Signal Processing, 54 (12): 4634-4643, December.
• J16. X. Huo, S. B. Kim, K. L. Tsui, and S. Wang (2006). FBP: A frontier-based tree-pruning algorithm. INFORMS Journal on Computing, 18 (4): 494-505, Fall.
• J15. Jihong Chen and X. Huo (2006). Distribution of the length of the longest significance run on a Bernoulli net, and its applications. Journal of the American Statistical Association, 101 (473), 321-331, March.
• J14. M. K. Jeong, J.C. Lu, X. Huo, B. Vidakovic, and D. Chen (2006). Wavelet-based data reduction techniques for process fault detection. Technometrics, 48 (1), 26-40, February. (Invited presentation in Technometrics session, QSR cluster, in 2005 Informs Annual Meeting at San Francisco, CA
• J13. E. Arias-Castro, D. L. Donoho and X. Huo (2006). Adaptive multiscale detection of filamentary structures embedded in a background of Uniform random points. Annals of Statistics, 34 (1), 326-349, February.
• J12. X. Huo and Jihong Chen (2005). JBEAM: multiscale curve coding via beamlets. IEEE Trans. Image Processing, 14 (11), 1665-1677, November.
• J11a. Ery Arias-Castro, David L. Donoho, Xiaoming Huo, and Craig A. Tovey (2006). Correction for “Connect-the-dots: How many random points can a regular curve pass through?” Advances in Applied Probability, 38 (2), 579, June.
• J11. E. Arias-Castro, D. L. Donoho, X. Huo, and C. Tovey (2005). Connect-the-dots: how many random points can a regular curve pass through? Advances in Applied Probability, 37 (3), 571-603, September.
• J10. E. Arias-Castro, D. L. Donoho and X. Huo (2005). Near-optimal detection of geometric objects by fast multiscale methods. IEEE Trans. Information Theory, 51 (7): 2402-2425, July.
• J9. X. Huo (2005). Exact lower bound for proportion of maximally embedded beamlet. Applied Mathematics Letters, 18 (5): 529-534, May.
• J8. X. Huo (2005). Minimax correlation between a line segment and a beamlet. Statistics & Probability Letters, 72 (1): 71-81, April.
• J7. D. L. Donoho and X. Huo (2004). BeamLab and reproducible research. International Journal of Wavelets, Multiresolution and Information Processing (IJWMIP), 2 (4): 391-414, December.
• J6. X. Huo and J. Lu (2004). A network flow approach in finding maximum likelihood estimate of high concentration regions. Computational Statistics and Data Analysis, 46 (1): 33-56, May.
• J5. X. Huo and Jihong Chen (2004). Building a cascade detector and its applications in automatic target recognition. Applied Optics: Information Processing (IP), 43 (2): 293-303, January.
• J4. X. Huo (2004). A statistical analysis of Fukunaga Koontz transform. IEEE Signal Processing Letters, 11 (2): 123-126, February.
• J3. X. Huo (2002). Multiscale Approximation MEthods (MAME) to locate embedded consecutive subsequences–its applications in statistical data mining and spatial statistics. Computers & Industrial Engineering, 43 (4): 703-720, September.
• J2. D. Chen, J. C. Lu, X. Huo, and M. Yin (2001). Optimum percentile estimating equations for nonlinear random coefficient models. Journal of Statistical Planning and Inference, 97 (2): 275-292, September.
• J1. D. Donoho and X. Huo (2001). Uncertainty principles and ideal atomic decomposition. IEEE Transactions on Information Theory, 47 (7): 2845-2862, November.

Refereed Conference Papers

• C36. Hyunouk Ko and Xiaoming Huo (2024). Universal consistency of wide and deep ReLU neural
  networks and minimax optimal convergence rates for Kolmogorov-Donoho optimal function
  classes. International Conference on Machine Learning (ICML), Vienna Austria, July 21-27, 2024.
• C35. Etash Kumar Guha, Prasanjit Dubey, Xiaoming Huo (2024). Generalization bounds for
  magnitude based pruning. The International Conference on Learning Representations (ICLR)
  Workshop Bridging the Gap Between Practice and Theory in Deep Learning (BGPT), Vienna
  Austria, May 11, 2024.
• C34. Etash Kumar Guha, Eugene Ndiaye, Xiaoming Huo (2023). Conformalization of sparse generalized linear models. International Conference on Machine Learning (ICML), Honolulu, Hawaii on July 23-29, 2023. (acceptance rate: 1,827 out of 6,538 submissions).
• C33. Namjoon Suh, Tian-Yi Zhou, Xiaoming Huo (2023). Approximation and non-parametric estimation of functions over high-dimensional spheres via deep ReLU networks. International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 1 – 5, 2023. (acceptance rate: 31.8%)
• C32. Yiling Luo, Yiling Xie, Xiaoming Huo (2023). Improved rate of first order algorithms for entropic optimal transport. The 26th International Conference on Artificial Intelligence and Statistics (AISTAT), Valencia, Spain on April 25 – 27, 2023. (acceptance rate: 29%)
• C31. Yiling Xie, Yiling Luo, Xiaoming Huo (2022). Solving a special type of optimal transport problem by a modified Hungarian algorithm. NeurIPS 2022 Workshop OPT.
• C30. Yiling Luo, Xiaoming Huo, Yajun Mei (2022). The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models, IEEE International Symposium on Information Theory (ISIT), pp.678-683.
• C29. Yiling Luo, Xiaoming Huo, Yajun Mei (2022). Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent. IEEE International Symposium on Information Theory (ISIT), pp.696-701.
• C28. Namjoon Suh, Hyunouk Ko, Xiaoming Huo (2022). Generalization of overparametrized deep neural network under noisy observations. International Conference on Learning Representations (ICLR). Virtual.
• C27. Jianzhou Feng, Li Song, Xiaoming Huo, Xiaokang Yang, Wenjun Zhang (2015). An optimized pixel-wise weighting approach for patch-based image denoising. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, April 19-24.
• C26. Jianzhou Feng, Li Song, Xiaoming Huo, Xiaokang Yang, Wenjun Zhang (2013). Image restoration via efficient Gaussian mixture model learning. International Conference on Image Processing (ICIP), Melbourne, Australia, September 15-18.
• C25. Hongteng Xu, Dixin Luo, Xiaoming Huo and Xiaokang Yang (2013). World expo problem and its mixed integer programming based solution. Workshop on Behavior and Social Informatics (BSI-UCBCN2013), in conjunction with the 2013 Pacific-Asia Conference on Data Mining and Knowledge Discovery (PAKDD2013), Gold Coast, Australia, April 14. (acceptance ratio 44%: 16 out of 36)
• C24. Chengliang Wang, Xiaoming Huo and W.-Z. Song (2013). Integer programming based approach for multiple-targets trajectory identification in WSNs. 2013 IEEE International Conference on Networking Sensing and Control, Paris-Evry, France, April 10-12.
• C23. Jianzhou Feng, Li Song, Xiaoming Huo, Xiaokang Yang, and Wenjun Zhang (2012). New bounds on image denoising: viewpoint of sparse representation and non-local averaging. Visual Communications and Image Processing (VCIP), 27-30 November, San Diego, USA.
• C22. Debraj De, Wen-Zhan Song, Mingsen Xu, Cheng-Liang Wang, Diane Cook, and Xiaoming Huo (2012). FindingHuMo: real-time user tracking in smart environments with anonymous binary sensing. INFOCOM{Demo/Poster Session.
• C21. Debraj De, Wen-Zhan Song, Mingsen Xu, Diane Cook, and Xiaoming Huo (2012). FindingHuMo: real-time tracking of motion trajectories from anonymous binary sensing in smart environments. The 32nd International Conference on Distributed Computing Systems (ICDCS’12). (acceptance ratio 13%: 71 out of 515)
• C20. Oktay Arslan, Panagiotis Tsiotras and Xiaoming Huo (2011). Solving shortest path problems with curvature constraints using Beamlets. IEEE/RSJ International Conference on Intelligent Robots and Systems. September 25-30, San Francisco, CA.
• C19. Yibiao Lu, Xiaoming Huo, Oktay Arslan, and Panagiotis Tsiotras (2011). Multi-scale LPA* with low worst-case complexity guarantees. IEEE/RSJ International Conference on Intelligent Robots and Systems. September 25-30, San Francisco, CA.
• C18. G. Deshpande, C. Kerssens, Xiaoming Huo, and Xiaoping Hu (2011). Simultaneous Investigation of Local and Distributed Functional Brain Connectivity from fMRI Data. 5th IEEE EMBS conference on Neural Engineering, Cancun, Mexico, April 27 – May 1.
• C17. Jianzhou Feng, Li Song, Xiaoming Huo, Xiaokang Yang, and Wenjun Zhang (2011). Learning sparse dictionaries with a popularity-based model. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 22-27.
• C16. Yibiao Lu, Xiaoming Huo, and Panagiotis Tsiotras (2010). Beamlet-like data processing for accelerated path-planning using multiscale information of the environment. 49th IEEE Conference on Decision and Control, Atlanta, GA, December.
• C15. Jianzhou Feng, Li Song, Xiaoming Huo, Xiaokang Yang, and Wenjun Zhang (2010). Image denoising using local tangent space alignment. Visual Communications and Image Processing (VCIP), 11-14 July, 2010, Huang Shan, An Hui, China.
• C14. A. K. Smith, X. Huo, and H. Zha (2008). Convergence and rate of convergence of a manifold-based dimension reduction algorithm. NIPS (a prestigious conference in computer science). Vancouver, Canada, December.
• C13a. X. Huo (2006). Some recent results on the performance and implementation of manifold learning algorithms. Proceedings of AI/DM workshop prior to the INFORMS Annual Meeting, Pittsburgh, PA, November. http://ieweb.uta.edu/vchen/AIDM/AIDM-Huo.pdf.
• C13. X. S. Ni and X. Huo (2005). Enhanced leaps-and-bounds methods in subset selections with additional optimality tests. (One of four finalists in the INFORMS QSR Best Student Paper Competition; http://qsr.section.informs.org/qsr activities.htm.)
• C12. Jie Chen and Xiaoming Huo (2005). Sparse representations for Multiple Measurement Vectors (MMV) in an over-complete dictionary. ICASSP, Philadelphia, PA, March.
• C11. X. Huo, Jihong Chen, and D. L. Donoho (2004). Coding lines and curves via digital beamlets. Data Compression Conference (DCC), Snowbird, UT, March. (DCC is a top international conference on data compression.)
• C10. X. Huo and Jihong Chen (2004). Detecting the presence of an inhomogeneous region in a homogeneous background: taking advantages of the underlying geometry via manifolds. ICASSP, Montreal, Quebec, Canada, May.
• C9. X. Huo, Jihong Chen and D. L. Donoho (2003). Multiscale significance run: realizing the `most powerful’ detection in noisy images. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November.
• C8. X. Huo (2003). A geodesic distance and local smoothing based clustering algorithm to utilize embedded geometric structures in high dimensional noisy data. SIAM International Conference on Data Mining, Workshop on Clustering High Dimensional Data and its Applications, San Francisco, CA, May.
• C7c. X. Huo, M. Elad, A. G. Flesia, B. Muise, R. Stanfill, J. Friedman, B. Popescu, Jihong Chen, A. Mahalanobis, and D. L. Donoho (2003). Optimal reduced-rank quadratic classifiers using the Fukunaga-Koontz transform, with applications to automated target recognition. SPIE’s 7th Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Controls (AeroSense), Orlando, FL, April.
• C7b. X. Huo, Jihong Chen, and D.L. Donoho (2003). Multiscale detection of filamentary features in image data. SPIE Wavelet-X, San Diego, CA, August.
• C7a. Jihong Chen and X. Huo (2002). Beamlet coder: a tree-based, hierarchical contour representation and coding method. ICASSP, Orlando, FL, May.
• C7. X. Huo and Jihong Chen (2002). Local linear projection (LLP). First IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS), Raleigh, NC, October. http://www.gensips.gatech.edu/proceedings/.
• C6. X. Huo and D. Donoho (2002). Recovering filamentary objects in severely degraded binary images using beamlet-decorated partitioning. International Conference on Acoustic Speech and Signal Processing, (ICASSP), Orlando, FL, May.
• C5a. D. Donoho and X. Huo (2000). Beamlet pyramids: a new form of multiresolution analysis, suited for extracting lines, curves and objects from very noisy image data. Published in Wavelet applications in signal and image processing VIII. Presented in SPIE, San Diego, CA.
• C5. D. Donoho and X. Huo (2001). Applications of beamlets to detection and extraction of lines, curves and objects in very noisy images. Nonlinear Signal and Image Processing (NSIP), Baltimore, MD, June.
• C4a. D. Donoho and X. Huo (1999). Combined image representation using edgelets and wavelets. Published in Wavelet Applications in Signal and Image Processing VII. Presented in SPIE, Denver, CO.
• C4. X. Huo and A. Stoschek (1999). Experiments with combined image transforms and its implications in biomedical image analysis. First USF International Workshop on Digital and Computational Video (DCV), Tampa, FL.
• C3. X. Huo and D. Donoho (1998). A simple and robust modulation classification method via counting. International Conference on Acoustic Speech and Signal Processing (ICASSP), Seattle, WA. (ICASSP is a top international conference on signal processing.)
• C2. X. Huo and S. Liu (1998). Stochastic behavior of inter-drop time in an M-buffer video decoding scenario. International Conference on Image Processing (ICIP), Chicago, IL. (ICIP is a top international conference on image processing.)
• C1. D. Donoho and X. Huo (1997). Large-sample modulation classification using Hellinger representation. Proc. Signal Processing Advances on Wireless Communication (SPAWC), Paris, France.

Refereed Book Chapters

• B10. Chuanping Yu and Xiaoming Huo (2019). Optimal projections in the distance-based statistical methods. In Statistical Modeling in Biomedical Research – Contemporary Topics and Voices in the Field. Editors: Yichuan Zhao and Ding-Geng Chen. Publisher: Springer. Series: Emerging Topics in Statistics and Biostatistics.
• B9. Jianzhou Feng, Xiaoming Huo, Li Song, Xiaokang Yang, and Wenjun Zhang (2017). Image nonnegative factorization: formulation and numerical strategies. In Tsinghua Lectures in Mathematics, Publisher: the Higher Education Press (in China) and International Press (in USA).
• B8. Deng, Shijie, Min Sim, and Xiaoming Huo (2017). Empirical analysis of market connectedness as a risk factor for explaining expected stock returns. In Portfolio Construction, Measurement, and Efficiency, Series: Springer International Publishing. pp. 275-289.
• B7. Xiaoming Huo, Cheng Huang, and Xuelei Sherry Ni (2018). Scattered data and aggregated inference. In Handbook of Big Data Analytics, Series: Springer Handbooks of Computational Statistics. Editors: Wolfgang Hardle, Henry Horng-Shing Lu, and Xiaotong Shen. Chapter 4, Springer.
• B6. Zhouwang Yang, Huizhi Xie, and Xiaoming Huo (2014). Data-driven smoothing can preserve good asymptotic properties. In Perspectives on Big Data Analysis Contemporary Mathematics, vol. 622, American Mathematical Society, Providence, RI, pp. 125-139.
• B5. Xiaoming Huo (2010). Beamlets. Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, No. 1 (Jan./Feb.), Eds. Edward J. Wegman, Yasmin H. Said, and David W. Scott, Wiley & Sons, NJ, pp 116-119.
• B4. Xiaoming Huo, Xuelei S. Ni, and Andrew K. Smith (2008). A survey of manifold-based learning methods. In Recent Advances in Data Mining of Enterprise Data, T. W. Liao and E. Triantaphyllou (Eds.) World Scientific, Singapore, pp 691-745, January.
• B3. X. Huo and X. S. Ni (2007). Some recent results in model selection. In Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques, Eds. D. Hong and Y. Shyr. World Scientific Publication, Singapore. Page 25-42.
• B2. X. Huo (2005). Beamlets and multiscale modeling. Entry for the 2nd Edition of Encyclopedia of Statistical Sciences, Eds. C. B. Read, N. Balakrishnan, and B. Vidakovic, Wiley & Sons, NJ.
• B1. D. Donoho and X. Huo (2002). Beamlets and multiscale image analysis. In Multiscale and Multiresolution Methods. Eds. T. J. Barth, T. Chan, and R. Haimes, Springer Lecture Notes in Computational Science and Engineering, 20: 149-196.

Edited Volume(s)

• E1. David Glickenstein, Keaton Hamm, Xiaoming Huo, Yajun Mei, Martin Stoll (2021). Mathematical Fundamentals of Machine Learning. Frontiers in Applied Mathematics and Statistics, section Mathematics of Computation and Data Science, Editorial.

Thesis

• X. Huo (1999). Sparse Image Representation via Combined Transforms. Ph.D. thesis, Stanford University, August; Tech. Report no. 1999-18.