[1] G. W. Gundersen, M. M. Zhang, and B. E. Engelhardt. Latent variable modeling with random features. Artificial Intelligence and Statistics, 130:1333-1341, 2021. arxiv:2006.11145. Joint first author. [ arXiv | .html ]
[2] M. M. Zhang and B. M. Stewart. Uncertainty quantification for nonconjugate topic models. 2020. In review.
[3] L. Lin, B. Saparbayeva, M. M. Zhang, and D. B. Dunson. Accelerated algorithms for convex and non-convex optimization on manifolds. 2020. arxiv:2010.08908. In review. [ arXiv ]
[4] L.-F. Cheng, B. Dumitrascu, M. M. Zhang, C. Chivers, K. Li, and B. E. Engelhardt. Personalized effects of medication on patients using latent force models with Gaussian processes. Artificial Intelligence and Statistics, 108:4045-4055, 2020. arXiv:1906.00226. [ arXiv | .html ]
[5] S. A. Williamson, M. M. Zhang, and P. Damien. A new class of time dependent latent factor models with applications. Journal of Machine Learning Research, 21(27):1-24, 2020. [ arXiv | .html ]
[6] A. Dubey, M. M. Zhang, E. P. Xing, and S. A. Williamson. Distributed, partially collapsed MCMC for Bayesian nonparametrics. Artificial Intelligence and Statistics, 108:3685-3695, 2020. arXiv:2001.05591. Joint first author. [ arXiv | .html ]
[7] M. M. Zhang, B. Dumitrascu, S. A. Williamson, and B. E. Engelhardt. Sequential Gaussian processes for online learning of nonstationary functions. 2019. arxiv:1905.10003. In review. [ arXiv ]
[8] M. M. Zhang and S. A. Williamson. Embarrassingly parallel inference for Gaussian processes. Journal of Machine Learning Research, 20(169):1-26, 2019. [ arXiv | .html ]
[9] F. Pérez-Cruz, P. M. Olmos, M. M. Zhang, and H. Huang. Probabilistic time of arrival localization. IEEE Signal Processing Letters, 26(11):1683-1687, 2019. arXiv:1910.06569. [ DOI | arXiv | http ]
[10] M. M. Zhang, S. A. Williamson, and F. Pérez-Cruz. Accelerated parallel non-conjugate sampling for Bayesian non-parametric models. 2019. arXiv:1705.07178. In review, revise and resubmit. Appeared in “BNP@NeurIPS 2018” as workshop paper. Previously known as “Accelerated Inference for Latent Variable Models”. [ arXiv ]
[11] B. Saparbayeva, M. M. Zhang, and L. Lin. Communication efficient parallel algorithms for optimization on manifolds. Advances in Neural Information Processing Systems 31, pages 3578-3588, 2018. Accepted as poster. [ arXiv | .pdf ]
[12] Z. I. Phillips, M. M. Zhang, and L. Reding. Social immune tolerance as a special protection of the queen. 2018. In review.
[13] M. M. Zhang, H. Lam, and L. Lin. Robust and parallel Bayesian model selection. Computational Statistics and Data Analysis, 127:229 - 247, 2018. [ DOI | arXiv | http ]
[14] M. M. Zhang, D. E. Schiavazzi, and L. Lin. Recombination of parallel Markov chains using local regression and Dirichlet process mixture models. 2017. Working paper.
[15] Z. I. Phillips, M. M. Zhang, and U. G. Müller. Dispersal of Attaphila fungicola (Blattodea: Ectobiidae), a symbiotic cockroach of leafcutter ants (Hymenoptera: Formicidae). Insectes Sociaux, 64(2):277-284, 2017. [ DOI | http ]
[16] M. M. Zhang, A. Dubey, and S. A. Williamson. Parallel Markov chain Monte Carlo for the Indian buffet process. 2015. “Bayesian Nonparametrics: The Next Generation” workshop paper. [ arXiv | http ]

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