Articles with public access mandates - Nhat HoLearn more
Available somewhere: 38
Multilevel clustering via Wasserstein means
N Ho, XL Nguyen, M Yurochkin, HH Bui, V Huynh, D Phung
Proceedings of the ICML, 2017, 2017
Mandates: US National Science Foundation, US Department of Defense, Australian …
Convergence rates of parameter estimation for some weakly identifiable finite mixtures
N Ho, XL Nguyen
The Annals of Statistics 44 (6), 2726-2755, 2016
Mandates: US National Science Foundation
On the efficiency of entropic regularized algorithms for optimal transport
T Lin, N Ho, MI Jordan
Journal of Machine Learning Research (JMLR) 23 (137), 1-42, 2022
Mandates: US National Science Foundation, US Department of Defense
On the complexity of approximating multimarginal optimal transport
T Lin*, N Ho*, M Cuturi, MI Jordan
Journal of Machine Learning Research (JMLR) 23 (65), 1-43, 2022
Mandates: US National Science Foundation, US Department of Defense
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
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation, US Department of Defense
Projection robust Wasserstein distance and Riemannian optimization
T Lin*, C Fan*, N Ho, M Cuturi, MI Jordan
Advances in NeurIPS, 2020, 2020
Mandates: US Department of Defense
Fixed-support Wasserstein barycenters: computational hardness and fast algorithm
T Lin, N Ho, X Chen, M Cuturi, MI Jordan
Advances in NeurIPS, 2020, 2020
Mandates: US National Science Foundation, US Department of Defense
On posterior contraction of parameters and interpretability in Bayesian mixture modeling
A Guha, N Ho, XL Nguyen
Bernoulli 27 (4), 2159-2188, 2021
Mandates: US National Science Foundation
Revisiting over-smoothing and over-squashing using Ollivier's Ricci curvature
K Nguyen, H Nong, V Nguyen, N Ho, S Osher, T Nguyen
Proceedings of the ICML, 2023, 2023
Mandates: US National Science Foundation, US Department of Defense
Architecture agnostic federated learning for neural networks
D Makhija, X Han, N Ho, J Ghosh
Proceedings of the ICML, 2022, 2022
Mandates: US National Science Foundation, US Department of Defense
Convergence rates for Gaussian mixtures of experts
N Ho, CY Yang, MI Jordan
Journal of Machine Learning Research, 2022
Mandates: US National Science Foundation, US Department of Defense
Fast algorithms for computational optimal transport and Wasserstein barycenter
W Guo, N Ho, MI Jordan
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020
Mandates: US Department of Defense
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
Mandates: US National Science Foundation
Sharp analysis of Expectation-Maximization for weakly identifiable models
R Dwivedi, N Ho, K Khamaru, M Wainwright, M Jordan, B Yu
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020
Mandates: US National Science Foundation, US Department of Defense
Fourierformer: Transformer meets generalized Fourier integral theorem
T Nguyen, M Pham, T Nguyen, K Nguyen, SJ Osher, N Ho
Advances in NeurIPS, 2022, 2022
Mandates: US National Science Foundation, US Department of Defense
Improving Transformers with probabilistic attention keys
T Nguyen*, TM Nguyen*, D Le, K Nguyen, A Tran, RG Baraniuk, N Ho*, ...
Proceedings of the ICML, 2022, 2022
Mandates: US National Science Foundation, US Department of Defense
Revisiting sliced Wasserstein on images: from vectorization to convolution
K Nguyen, N Ho
Advances in NeurIPS, 2022, 2022
Mandates: US National Science Foundation
LVM-Med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching
DMH Nguyen, H Nguyen, NT Diep, TN Pham, T Cao, BT Nguyen, ...
Advances in NeurIPS, 2023, 2023
Mandates: German Research Foundation, Federal Ministry of Education and Research, Germany
Amortized projection optimization for sliced Wasserstein generative models
K Nguyen, N Ho
Advances in NeurIPS, 2022, 2022
Mandates: US National Science Foundation
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