Nhat Ho

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
XuanLong NguyenUniversity of MichiganVerified email at umich.edu
Hung BuiResearch Scientist, Google DeepMindVerified email at google.com
Dinh PhungProfessor, Monash UniversityVerified email at monash.edu
Tianyi LinUniversity of California, BerkeleyVerified email at berkeley.edu
Martin WainwrightProfessor of EECS and Statistics, UC BerkeleyVerified email at berkeley.edu
Marco CuturiGoogle Brain / CREST-ENSAE.Verified email at google.com
Raaz DwivediPostdoctoral Fellow, Harvard University, MITVerified email at mit.edu
Koulik KhamaruGraduate Student, Department Of Statistics, UC BerkeleyVerified email at berkeley.edu
Mikhail YurochkinResearch Staff Member, IBM Research AIVerified email at umich.edu
Khai NguyenDepartment of Statistics and Data Sciences (SDS), University of Texas at AustinVerified email at utexas.edu
Tam LeResearch Scientist at RIKEN AIPVerified email at iip.ist.i.kyoto-u.ac.jp
Bin YUProfessor of Statistics and EECS, UC BerkeleyVerified email at stat.berkeley.edu
Makoto YamadaKyoto University / RIKEN Center for Advanced Intelligence Project (AIP)Verified email at i.kyoto-u.ac.jp
Xi ChenProfessor at New York UniversityVerified email at nyu.edu
Trung LeFaculty of Information Technology, Monash University, AustraliaVerified email at monash.edu
Richard BaraniukVictor E. Cameron Professor, Electrical & Computer Engineering, Rice UniversityVerified email at rice.edu
Khang LeUniversity of Texas at AustinVerified email at utexas.edu
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinVerified email at utexas.edu
Jeongyeol Kwonuniversity of texas at austinVerified email at utexas.edu

Nhat Ho

University of Texas, Austin

Verified email at utexas.edu - Homepage

Machine Learning Statistics Optimization Optimal Transport Bayesian Inference


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Multilevel Clustering via Wasserstein Means N Ho, XL Nguyen, M Yurochkin, HH Bui, V Huynh, D Phung Proceedings of the ICML, 2017, 2017	89	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	65	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	44	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	30	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	20*	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	20	2020
On the complexity of approximating multimarginal optimal transport T Lin, N Ho, M Cuturi, MI Jordan arXiv preprint arXiv: 1910.00152, 2019	20	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	20	2019
Distributional sliced-Wasserstein and applications to generative modeling K Nguyen, N Ho, T Pham, H Bui ICLR, 2021, 2021	19	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	18*	2019
On posterior contraction of parameters and interpretability in Bayesian mixture modeling A Guha, N Ho, XL Nguyen Bernoulli 27 (4), 2159-2188, 2021	16	2021
Probabilistic Multilevel Clustering via Composite Transportation Distance N Ho, V Huynh, D Phung, MI Jordan AISTATS, 2019, 2019	13	2019
LAMDA: Label matching deep domain adaptation T Le, T Nguyen, N Ho, H Bui, D Phung Proceedings of the ICML, 2021, 2021	12*	2021
Fast algorithms for computational optimal transport and Wasserstein barycenter W Guo, N Ho, M Jordan AISTATS, 2088-2097, 2020	12*	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	12*	2020
Projection robust Wasserstein distance and Riemannian optimization T Lin, C Fan, N Ho, M Cuturi, MI Jordan Advances in NeurIPS, 2020, 2020	11	2020
On the minimax optimality of the EM algorithm for learning two-component mixed linear regression JY Kwon, N Ho, C Caramanis AISTATS, 2021, 2021	10	2021
Convergence rates for Gaussian mixtures of experts N Ho, CY Yang, MI Jordan arXiv preprint arXiv:1907.04377, 2019	10	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	10*	2018
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	9*	2018

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