Nhat Ho
Cited by
Cited by
Multilevel Clustering via Wasserstein Means
N Ho, XL Nguyen, M Yurochkin, HH Bui, V Huynh, D Phung
Proceedings of the ICML, 2017, 2017
On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms
T Lin*, N Ho*, MI Jordan
Proceedings of the ICML, 2019, 2019
Convergence rates of parameter estimation for some weakly identifiable finite mixtures
N Ho, XL Nguyen
The Annals of Statistics 44 (6), 2726-2755, 2016
On strong identifiability and convergence rates of parameter estimation in finite mixtures
N Ho, XL Nguyen
Electronic Journal of Statistics 10 (1), 271-307, 2016
Fixed-support Wasserstein barycenters: computational hardness and fast algorithm
T Lin, N Ho, X Chen, M Cuturi, MI Jordan
Advances in NeurIPS, 2020, 2020
Singularity, misspecification, and the convergence rate of EM
R Dwivedi*, N Ho*, K Khamaru*, MJ Wainwright, MI Jordan, B Yu
The Annals of Statistics 48(6), 3161-3182, 2020
On the complexity of approximating multimarginal optimal transport
T Lin*, N Ho*, M Cuturi, MI Jordan
arXiv preprint arXiv: 1910.00152, 2019
Singularity structures and impacts on parameter estimation in finite mixtures of distributions
N Ho, XL Nguyen
SIAM Journal on Mathematics of Data Science (SIMODS), 1(4), 730–758, 2019
Distributional sliced-Wasserstein and applications to generative modeling
K Nguyen, N Ho, T Pham, H Bui
ICLR, 2021, 2021
On the efficiency of the Sinkhorn and Greenkhorn algorithms and their acceleration for optimal transport
T Lin, N Ho, MI Jordan
arXiv preprint arXiv:1906.01437, 2019
On posterior contraction of parameters and interpretability in Bayesian mixture modeling
A Guha, N Ho, XL Nguyen
Bernoulli 27 (4), 2159-2188, 2021
Probabilistic Multilevel Clustering via Composite Transportation Distance
N Ho*, V Huynh*, D Phung, MI Jordan
AISTATS, 2019, 2019
LAMDA: Label matching deep domain adaptation
T Le, T Nguyen, N Ho, H Bui, D Phung
Proceedings of the ICML, 2021, 2021
Fast algorithms for computational optimal transport and Wasserstein barycenter
W Guo, N Ho, M Jordan
AISTATS, 2088-2097, 2020
Sharp analysis of Expectation-Maximization for weakly identifiable models
R Dwivedi, N Ho, K Khamaru, M Wainwright, M Jordan, B Yu
AISTATS, 1866-1876, 2020
Projection robust Wasserstein distance and Riemannian optimization
T Lin*, C Fan*, N Ho, M Cuturi, MI Jordan
Advances in NeurIPS, 2020, 2020
Convergence rates for Gaussian mixtures of experts
N Ho, CY Yang, MI Jordan
arXiv preprint arXiv:1907.04377, 2019
Theoretical guarantees for EM under misspecified Gaussian mixture models
R Dwivedi*, N Ho*, K Khamaru*, MJ Wainwright, MI Jordan
Advances in NIPS, 2018, 2018
On the minimax optimality of the EM algorithm for learning two-component mixed linear regression
JY Kwon, N Ho, C Caramanis
AISTATS, 2021, 2021
A Bayesian perspective of convolutional neural networks through a deconvolutional generative model
T Nguyen, N Ho, A Patel, A Anandkumar, MI Jordan, RG Baraniuk
arXiv preprint arXiv:1811.02657, 2018
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