Selected Publications [bibtex]

  1. M. Yin and and M. Zhou, "ARM: Augment-REINFORCE-merge gradient for stochastic binary networks," to appear in International Conference on Learning Representations (ICLR2019), New Orleans, LA, May 2019. ICLR / Python (TensorFlow) code in GitHub
  2. R. Panda, A. Pensia, M. Zhou, and P. Rai, "Deep conditioned Poisson factor model for multi-label learning," to appear in International Conference on Artificial Intelligence and Statistics (AISTATS2019), Naha, Okinawa, Japan, April 2019.
  3. M. Yin and and M. Zhou, "ARM: Augment-REINFORCE-merge gradient for discrete latent variable models," July 2018. PDF / arXiv:1807.11143 / Slides / Python (TensorFlow) code in GitHub
  4. M. Zhou, "Parsimonious Bayesian deep networks," Neural Information Processing Systems (NeurIPS2018), Montreal, Canada, Dec. 2018. PDF / arXiv:1805.08719 / Poster / Code in GitHub (Python (Tensorflow) for MAP-SGD, Matlab for Gibbs sampling) / Illustration
  5. Q. Zhang and M. Zhou, "Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks," Neural Information Processing Systems (NeurIPS2018), Montreal, Canada, Dec. 2018. PDF / arXiv:1810.08564 / R Code in GitHub
  6. H. Zhao, L. Du, W. Buntine, and M. Zhou, "Dirichlet belief networks as structured topic prior," Neural Information Processing Systems, (NeurIPS2018), Montreal, Canada, Dec. 2018. PDF / arXiv:1811.00717 / Code in GitHub
  7. D. Guo, B. Chen, H. Zhang, and M. Zhou, "Deep Poisson gamma dynamical systems," Neural Information Processing Systems, (NeurIPS2018), Montreal, Canada, Dec. 2018. PDF / arXiv:1810.11209 / Python (TensorFlow) Code in GitHub
  8. E. Hajiramezanali, S. Z. Dadaneh, A. Karbalayghareh, M. Zhou, and X. Qian, "Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data," Neural Information Processing Systems (NeurIPS2018), Montreal, Canada, Dec. 2018. PDF
  9. B. Han, J. Yao, G. Niu, M. Zhou, I. Tsang, Y. Zhang, M. Sugiyama, "Masking: A new perspective of noisy supervision," Neural Information Processing Systems, (NeurIPS2018), Montreal, Canada, Dec. 2018. PDF / Python (TensorFlow) code in GitHub
  10. A. Schein, Z. S. Wu, A. Schofield, M. Zhou, and H. Wallach, "Locally private Bayesian inference for count models," preprint, Nov. 2018. arXiv:1803.08471
  11. M. Yin and and M. Zhou, "Semi-implicit variational inference," International Conference on Machine Learning (ICML2018), Stockholm, Sweden, July 2018. PDF / arXiv:1805.11183 / Slides / Poster / Related Slides / Python (TensorFlow) code in GitHub
  12. H. Zhao, L. Du, W. Buntine, and M. Zhou, "Inter and intra topic structure learning with word embeddings," International Conference on Machine Learning (ICML2018), Stockholm, Sweden, July 2018. PDF / Matlab code in GitHub
  13. A. Acharya, J. Ghosh, and M. Zhou, "A dual Markov chain topic model for dynamic environments," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2018), London, UK, Aug. 2018. (Long Presentation, Research Track) PDF
  14. S. Z. Dadaneh, M. Zhou, and X. Qian, "Bayesian negative binomial regression for differential expression with confounding factors," to appear in Bioinformatics. Bioinformatics / R Code in GitHub
  15. E. Hajiramezanali, S. Z. Dadaneh, P. de Figueiredo, S.-H. Sze, M. Zhou, and X. Qian, "Differential expression analysis of dynamical sequencing count data with a gamma Markov chain," arXiv:1803.02527, March 2018
  16. S. Z. Dadaneh, M. Zhou, and X. Qian, "Covariate-dependent negative binomial factor analysis of RNA sequencing data," to appear in Bioinformatics. PDF / R Code in GitHub
  17. Q. Zhang and M. Zhou, "Permuted and augmented stick-breaking Bayesian multinomial regression," Journal of Machine Learning Research, vol. 18, pp. 1-33, Apr. 2018. JMLR / PDF / arXiv:1612.09413/ Slides for 39th Annual ISMS Marketing Science Conference / [R Code]
  18. H. Zhang, B. Chen, D. Guo, and M. Zhou, "WHAI: Weibull hybrid autoencoding inference for deep topic modeling," International Conference on Learning Representations (ICLR2018), Vancouver, Canada, May 2018. PDF / arXiv:1803.01328 / Python (Theano) code in GitHub
  19. R. Kalantari, J. Ghosh, and M. Zhou, "Nonparametric Bayesian sparse graph linear dynamical systems," Artificial Intelligence and Statistics (AISTATS2018), Lanzarote, Canary Islands, Spain, April 2018. PDF / arXiv:1802.07434
  20. F. Xie, M. Zhou, and Y. Xu, "BayCount: A Bayesian decomposition method for inferring tumor heterogeneity using RNA-Seq counts," Annals of Applied Statistics, vol. 12, no. 3 pp. 1605-1627, 2018. AOAS / PDF
  21. C. Wang, B. Chen, and M. Zhou, "Multimodal Poisson gamma belief network," AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, Feb. 2018. PDF / Poster / Matlab Code in GitHub
  22. M. Zhou, "Nonparametric Bayesian negative binomial factor analysis," Bayesian Analysis, vol. 13, no. 4, pp. 1061-1089, 2018. BA / PDF / arXiv:1604.07464 / Matlab Code in GitHub
  23. M. Zhou, Discussion on "Sparse graphs using exchangeable random measures" by F. Caron and E. B. Fox, Journal of the Royal Statistical Society, Series B, 2017. PDF / arXiv:1802.07721
  24. Y. Cong, B. Chen, H. Liu, and M. Zhou, "Deep latent Dirichlet allocation with topic-layer-adaptive stochastic gradient Riemannian MCMC," International Conference on Machine Learning (ICML2017), Sydney, Australia, Aug. 2017. PDF / arXiv:1706.01724 / Slides /Poster / Matlab Code in GitHub
  25. S. Z. Dadaneh, X. Qian, and M. Zhou, "BNP-Seq: Bayesian nonparametric differential expression analysis of sequencing count data," Journal of the American Statistical Association, vol. 113, no. 521, pp. 81-94, 2018. JASA / PDF / arXiv:1608.03991 / R Code in GitHub
  26. Y. Cong, B. Chen, and M. Zhou, "Fast simulation of hyperplane-truncated multivariate normal distributions," Bayesian Analysis, vol. 12, pp. 1017-1037, 2017. BA / PDF / arXiv:1607.04751 / Matlab Code in GitHub
  27. 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)
  28. 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
  29. M. Zhou, "Softplus regressions and convex polytopes," preprint, August 2016. PDF / arXiv:1608.06383 / [Matlab Code]
  30. M. Zhou, S. Favaro, S. G. Walker, "Frequency of frequencies distributions and size dependent exchangeable random partitions," Journal of the American Statistical Association (Theory and Methods), vol. 112, no. 520, pp. 1623-1635, 2017. JASA / PDF / arXiv:1608.00264 / Matlab Code in GitHub
  31. 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
  32. 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
  33. 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
  34. 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
  35. M. Zhou, "Nonparametric Bayesian matrix factorization for assortative networks," European Signal Processing Conference (EUSIPCO), Sept. 2015. (Invited special session paper) PDF / Slides
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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)
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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)
  48. 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)
  49. © September 2018 Mingyuan Zhou