Selected Publications on Theory (Hierarchical and Mixture Models, Bayesian Nonparametrics, Optimal Transport, Deep Learning, (Approximate) Bayesian Inference, (Non)-Convex Optimization, etc.)
* = equal contribution
** = alphabetical order
† = co-last author
- Multivariate smoothing via the Fourier integral theorem and Fourier kernel . Under revision.
Nhat Ho**, Stephen G. Walker**.
- Bayesian consistency with the supremum metric . Statistica Sinica, 2022.
Nhat Ho**, Stephen G. Walker**
- An exponentially increasing step-size for parameter estimation in statistical models. Under review.
Nhat Ho**, Tongzheng Ren**, Purnamrita Sarkar**, Sujay Sanghavi**, Rachel Ward**.
- Beyond black box densities: Parameter learning for the deviated components. Advances in NeurIPS, 2022.
Dat Do*, Nhat Ho*, Long Nguyen.
- Refined convergence rates for maximum likelihood estimation under finite mixture models. Proceedings of the ICML, 2022 (Long Presentation).
Tudor Manole, Nhat Ho.
- Entropic Gromov-Wasserstein between Gaussian distributions. Proceedings of the ICML, 2022.
Khang Le*, Dung Le*, Huy Nguyen*, Dat Do, Tung Pham, Nhat Ho.
- Towards statistical and computational complexities of Polyak step size gradient descent . AISTATS, 2022.
Tongzheng Ren*, Fuheng Cui*, Alexia Atsidakou*, Sujay Sanghavi, Nhat Ho.
- On the minimax optimality of the EM algorithm for learning two-component mixed linear regression. AISTATS, 2021.
Jeong Y. Kwon, Nhat Ho, Constantine Caramanis.
- Beyond EM algorithm on over-specified two-component location-scale Gaussian mixtures. Under review.
Tongzheng Ren*, Fuheng Cui*, Sujay Sanghavi, Nhat Ho.
- On the efficiency of entropic regularized algorithms for optimal transport . Journal of Machine Learning Research (JMLR), 2022.
Tianyi Lin, Nhat Ho, Michael I. Jordan.
- Convergence rates for Gaussian mixtures of experts. Journal of Machine Learning Research (JMLR), 2022.
Nhat Ho, Chiao-Yu Yang, Michael I. Jordan.
- Projection robust Wasserstein distance and Riemannian optimization . Advances in NeurIPS, 2020 (Spotlight).
Tianyi Lin*, Chenyou Fan*, Nhat Ho, Marco Cuturi, Michael I. Jordan.
- On posterior contraction of parameters and interpretability in Bayesian mixture modeling . Bernoulli 27 (4), 2159-2188, 2021.
Aritra Guha, Nhat Ho, XuanLong Nguyen.
- Singularity, misspecification, and the convergence rate of EM. Annals of Statistics, 48(6), 3161-3182, 2020.
Raaz Dwivedi*, Nhat Ho*, Koulik Khamaru*, Martin J. Wainwright, Michael I. Jordan, Bin Yu.
- Singularity structures and impacts on parameter estimation behavior in finite mixtures of distributions. SIAM Journal on Mathematics of Data Science (SIMODS), 1(4), 730–758, 2019.
Nhat Ho and XuanLong Nguyen.
- Convergence rates of parameter estimation for some weakly identifiable finite mixtures. Annals of Statistics, 44(6), 2726-2755, 2016.
Nhat Ho and XuanLong Nguyen
