Publications

Journal Papers

Refereed Conference Papers

  • 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