Selected Publications [bibtex]

  1. Y. Cong, B. Chen, H. Liu, and M. Zhou, "Deep latent Dirichlet allocation with topic-layer-adaptive stochastic gradient Riemannian MCMC," to appear in International Conference on Machine Learning (ICML2017), Sydney, Australia, August 2017.
  2. S. Z. Dadaneh, X. Qian, and M. Zhou, "BNP-Seq: Bayesian nonparametric differential expression analysis of sequencing count data," to appear in Journal of the American Statistical Association, 2017+. PDF / arXiv:1608.03991 / [R Code]
  3. F. Xie, M. Zhou, and Y. Xu, "BayCount: A Bayesian decomposition method for inferring tumor heterogeneity using RNA-Seq counts," preprint, Feb. 2017. arXiv:1702.07981
  4. Y. Cong, B. Chen, and M. Zhou, "Fast simulation of hyperplane-truncated multivariate normal distributions," to appear in Bayesian Analysis, 2017+. BA / PDF / arXiv:1607.04751
  5. Q. Zhang and M. Zhou, "Permuted and augmented stick-breaking Bayesian multinomial regression," preprint, Dec. 2016. PDF / arXiv:1612.09413 / [R Code]
  6. A. Schein, M. Zhou, and H. Wallach, "Poisson–gamma dynamical systems," Neural Information Processing Systems (NIPS2016), Barcelona, Spain, Dec. 2016. PDF / Slides /Poster / Python Code in Github (Oral presentation)
  7. M. Zhou, Y. Cong, and B. Chen, "Augmentable gamma belief networks," Journal of Machine Learning Research, vol. 17, pp. 1-44, Sept. 2016. JMLR / PDF / arXiv:1512.03081 / Slides for CFE2015 / Slides for ISBA2016 / Matlab Code in Github
  8. M. Zhou, "Softplus regressions and convex polytopes," preprint, August 2016. PDF / arXiv:1608.06383 / [Matlab Code]
  9. M. Zhou, S. Favaro, S. G. Walker, "Frequency of frequencies distributions and size dependent exchangeable random partitions," to appear in Journal of the American Statistical Association (Theory and Methods). JASA / PDF / arXiv:1608.00264 / [Code]
  10. A. Schein, M. Zhou, D. M. Blei, H. Wallach, "Bayesian Poisson Tucker decomposition for learning the structure of international relations," International Conference on Machine Learning (ICML2016), New York City, NY, June 2016. PDF / arXiv:1606.01855
  11. M. Zhou, "Nonparametric Bayesian negative binomial factor analysis," Jan. 2016. PDF / arXiv:1604.07464
  12. M. Zhou, Y. Cong, and B. Chen, "The Poisson gamma belief network," Neural Information Processing Systems (NIPS2015), Montreal, Canada, Dec. 2015. NIPS / PDF / arXiv:1511.02199 / Poster / Matlab Code in Github
  13. M. Zhou, O. H. M. Padilla, and J. G. Scott, "Priors for random count matrices derived from a family of negative binomial processes," Journal of the American Statistical Association (Theory and Methods), vol. 111, pp. 1144-1156, 2016. JASA / Supplemental / PDF / arXiv:1404.3331 / Slides for BNP10 / Matlab Code in Github
  14. A. Acharya, D. Teffer, J. Henderson, M. Tyler, M. Zhou, and J. Ghosh, "Gamma process Poisson factorization for joint modeling of network and documents," European Conference on Machine Learning (ECML 2015), Porto, Portugal, Sept. 2015. PDF
  15. M. Zhou, "Nonparametric Bayesian matrix factorization for assortative networks," European Signal Processing Conference (EUSIPCO), Sept. 2015. (Invited special session paper) PDF / Slides
  16. M. Zhou, "Infinite edge partition models for overlapping community detection and link prediction," Artificial Intelligence and Statistics (AISTATS2015), JMLR W&CP, vol. 38, San Diego, CA, May 2015. PDF / arXiv:1501.06218 / Poster / Matlab Code in Github
  17. A. Acharya, J. Ghosh, and M. Zhou, "Nonparametric Bayesian factor analysis for dynamic count matrices," Artificial Intelligence and Statistics (AISTATS2015), JMLR W&CP, vol. 38, San Diego, CA, May 2015. PDF / arXiv:1512.08996
  18. M. Zhou, "Beta-negative binomial process and exchangeable random partitions for mixed-membership modeling," Neural Information Processing Systems (NIPS2014), Montreal, Canada, Dec. 2014. NIPS / PDF / arXiv:1410.7812 / Poster / Matlab Code in Github
  19. G. Polatkan, M. Zhou, L. Carin, D. Blei, and I. Daubechies, "A Bayesian nonparametric approach to image super-resolution," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 37, pp. 346-358, Feb. 2015. PAMI / arXiv:1209.5019
  20. M. Zhou and L. Carin, "Negative binomial process count and mixture modeling," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 37, pp. 307-320, Feb. 2015. PAMI / PDF / arXiv:1209.3442 / Matlab Code
  21. M. Zhou and L. Carin, "Augment-and-conquer negative binomial processes," in Neural Information Processing Systems (NIPS2012), Lake Tahoe, NV, Dec. 2012. PDF / Slides / Poster / Matlab Code (Spotlight oral presentation)
  22. M. Zhou, L. Li, D. Dunson and L. Carin, "Lognormal and gamma mixed negative binomial regression," International Conference on Machine Learning (ICML2012), Edinburgh, Scotland, Jun. 2012. PDF / Matlab Code / Appendix / Slides / Poster / Video
  23. M. Zhou, L. Hannah, D. Dunson and L. Carin, "Beta-negative binomial process and Poisson factor analysis," Artificial Intelligence and Statistics (AISTATS2012), JMLR W&CP, vol. 22, pp. 1462-1471, La Palma, Canary Islands, Spain, Apr. 2012. PDF / Poster / Matlab Code
  24. L. Li, M. Zhou, G. Sapiro and L. Carin, "On the integration of topic modeling and dictionary learning," International Conference on Machine Learning (ICML2011), Bellevue, WA, Jun. 2011. PDF
  25. Z. Xing, M. Zhou, A. Castrodad, G. Sapiro and L. Carin, "Dictionary learning for noisy and incomplete hyperspectral images," SIAM Journal on Imaging Sciences, vol. 5, pp. 33-56, Jan. 2012. SIAM / PDF
  26. M. Zhou, H. Chen, J. Paisley, L. Ren, L. Li, Z. Xing, D. Dunson, G. Sapiro and L. Carin, "Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images," IEEE Trans. Image Processing, vol. 21, pp. 130-144, Jan. 2012. TIP / PDF / Matlab code and test results
  27. M. Zhou, H. Yang, G. Sapiro, D. Dunson and L. Carin, "Dependent hierarchical beta process for image interpolation and denoising," Artificial Intelligence and Statistics (AISTATS2011), JMLR W&CP, vol. 15, pp. 883-891, Ft. Lauderdale, FL, 2011. PDF / Slides / Video (Oral presentation)
  28. M. Zhou, H. Chen, J. Paisley, L. Ren, G. Sapiro and L. Carin, "Non-parametric Bayesian dictionary learning for sparse image representations," Neural Information Processing Systems (NIPS2009), Vancouver, Canada, Dec. 2009. NIPS / PDF / Matlab code and Inference equations / Slides / Poster / Video (Oral presentation)
  29. © February 2017 Mingyuan Zhou