Publications

cv
[1] Y. Li, Z. Lin, F. Yin, and M. M. Zhang. Preventing model collapse in Gaussian process latent variable models. 2024. In review.
[2] L. Lin, B. Saparbayeva, M. M. Zhang, and D. B. Dunson. Accelerated algorithms for convex and non-convex optimization on manifolds. 2024. To appear in Machine Learning. [ arXiv ]
[3] M. M. Zhang, B. Dumitrascu, S. A. Williamson, and B. E. Engelhardt. Sequential Gaussian processes for online learning of nonstationary functions. IEEE Transactions on Signal Processing, 71:1539--1550, 2023. [ arXiv | http ]
[4] Y. Li, L. Cheng, F. Yin, M. M. Zhang, and S. Theodoridis. Overcoming posterior collapse in variational autoencoders via EM-style training. IEEE International Conference on Acoustics, Speech and Signal Processing, 2023. Accepted for oral presentation. [ http ]
[5] M. M. Zhang, G. W. Gundersen, and B. E. Engelhardt. Bayesian non-linear latent variable modeling via random Fourier features. 2023. Joint first author. In review, revise and resubmit. [ arXiv ]
[6] F. Fazeli-Asl, M. M. Zhang, and L. Lin. A semi-Bayesian nonparametric estimator of the maximum mean discrepancy measure: Applications in goodness-of-fit testing and generative adversarial networks. 2023. In review. [ arXiv ]
[7] F. Fazeli-Asl and M. M. Zhang. A Bayesian non-parametric approach to generative models: Integrating variational autoencoder and generative adversarial networks using Wasserstein and maximum mean discrepancy. 2023. In review. [ arXiv ]
[8] Y. Li, Z. Lin, K. Li, and M. M. Zhang. Online/offline learning to enable robust beamforming: Limited feedback meets deep generative models. 2023. In review.
[9] T. Sha and M. M. Zhang. Online student-t processes with an overall-local scale structure for modelling non-stationary data. 2023. In review. [ arXiv ]
[10] M. M. Zhang, S. A. Williamson, and F. Pérez-Cruz. Accelerated parallel non-conjugate sampling for Bayesian non-parametric models. Statistics & Computing, 32(50):1--25, 2022. [ arXiv | http ]
[11] M. M. Zhang. Sparse infinite random feature latent variable modeling. 2022. In review. [ arXiv ]
[12] G. W. Gundersen, M. M. Zhang, and B. E. Engelhardt. Latent variable modeling with random features. Artificial Intelligence and Statistics, 130:1333--1341, 2021. Joint first author. [ arXiv | .html ]
[13] 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. Joint first author. [ arXiv | .html ]
[14] 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 ]
[15] 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 | .html ]
[16] 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 | http ]
[17] 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 ]
[18] M. M. Zhang, H. Lam, and L. Lin. Robust and parallel Bayesian model selection. Computational Statistics and Data Analysis, 127:229 -- 247, 2018. [ arXiv | http ]
[19] B. Saparbayeva, M. M. Zhang, and L. Lin. Communication efficient parallel algorithms for optimization on manifolds. Advances in Neural Information Processing Systems, 31:3578--3588, 2018. Accepted as poster. [ arXiv | .pdf ]
[20] 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. [ http ]
[21] 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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