Nhat Ho

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berkeley college

Nhat Ho is an Assistant Professor of Statistics and Data Sciences at the University of Texas, Austin.

Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout

Approximate inference in deep Bayesian networks exhibits a dilemma of ho...
02/16/2021 ∙ by Son Nguyen, et al. ∙ 45 ∙ share

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BoMb-OT: On Batch of Mini-batches Optimal Transport

Mini-batch optimal transport (m-OT) has been successfully used in practi...
02/11/2021 ∙ by Khai Nguyen, et al. ∙ 12 ∙ share

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On the computational and statistical complexity of over-parameterized matrix sensing

We consider solving the low rank matrix sensing problem with Factorized ...
01/27/2021 ∙ by Jiacheng Zhuo, et al. ∙ 0 ∙ share

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Multivariate Smoothing via the Fourier Integral Theorem and Fourier Kernel

Starting with the Fourier integral theorem, we present natural Monte Car...
12/28/2020 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein

Relational regularized autoencoder (RAE) is a framework to learn the dis...
10/05/2020 ∙ by Khai Nguyen, et al. ∙ 3 ∙ share

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Projection Robust Wasserstein Distance and Riemannian Optimization

Projection robust Wasserstein (PRW) distance, or Wasserstein projection ...
06/12/2020 ∙ by Tianyi Lin, et al. ∙ 0 ∙ share

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Probabilistic Best Subset Selection via Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...
06/11/2020 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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Probabilistic Best Subset Selection by Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...
06/11/2020 ∙ by Mingzhang Yin, et al. ∙ 0 ∙ share

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On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression

We study the convergence rates of the EM algorithm for learning two-comp...
06/04/2020 ∙ by Jeongyeol Kwon, et al. ∙ 11 ∙ share

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Uniform Convergence Rates for Maximum Likelihood Estimation under Two-Component Gaussian Mixture Models

We derive uniform convergence rates for the maximum likelihood estimator...
06/01/2020 ∙ by Tudor Manole, et al. ∙ 0 ∙ share

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Instability, Computational Efficiency and Statistical Accuracy

Many statistical estimators are defined as the fixed point of a data-dep...
05/22/2020 ∙ by Nhat Ho, et al. ∙ 13 ∙ share

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Distributional Sliced-Wasserstein and Applications to Generative Modeling

Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserst...
02/18/2020 ∙ by Khai Nguyen, et al. ∙ 4 ∙ share

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Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms

We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...
02/12/2020 ∙ by Tianyi Lin, et al. ∙ 4 ∙ share

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Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm

We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...
02/12/2020 ∙ by Tianyi Lin, et al. ∙ 0 ∙ share

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Computational Hardness and Fast Algorithm for Fixed-Support Wasserstein Barycenter

We study in this paper the fixed-support Wasserstein barycenter problem ...
02/12/2020 ∙ by Tianyi Lin, et al. ∙ 0 ∙ share

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On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

We provide a computational complexity analysis for the Sinkhorn algorith...
02/09/2020 ∙ by Khiem Pham, et al. ∙ 0 ∙ share

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Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing

We study the problem of sampling from the power posterior distribution i...
12/11/2019 ∙ by Wenlong Mou, et al. ∙ 27 ∙ share

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On Scalable Variant of Wasserstein Barycenter

We study a variant of Wasserstein barycenter problem, which we refer to ...
10/10/2019 ∙ by Tam Le, et al. ∙ 8 ∙ share

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Computationally Efficient Tree Variants of Gromov-Wasserstein

We propose two novel variants of Gromov-Wasserstein (GW) between probabi...
10/10/2019 ∙ by Tam Le, et al. ∙ 3 ∙ share

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On the Complexity of Approximating Multimarginal Optimal Transport

We study the complexity of approximating the multimarginal optimal trans...
09/30/2019 ∙ by Tianyi Lin, et al. ∙ 14 ∙ share

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On Efficient Multilevel Clustering via Wasserstein Distances

We propose a novel approach to the problem of multilevel clustering, whi...
09/19/2019 ∙ by Viet Huynh, et al. ∙ 0 ∙ share

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A Diffusion Process Perspective on Posterior Contraction Rates for Parameters

We show that diffusion processes can be exploited to study the posterior...
09/03/2019 ∙ by Wenlong Mou, et al. ∙ 0 ∙ share

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Convergence Rates for Gaussian Mixtures of Experts

We provide a theoretical treatment of over-specified Gaussian mixtures o...
07/09/2019 ∙ by Nhat Ho, et al. ∙ 6 ∙ share

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On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport

We propose and analyze a novel approach to accelerate the Sinkhorn and G...
06/01/2019 ∙ by Tianyi Lin, et al. ∙ 0 ∙ share

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Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models

Dirichlet process mixture models (DPMM) play a central role in Bayesian ...
05/23/2019 ∙ by Chiao-Yu Yang, et al. ∙ 0 ∙ share

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Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport

We propose and analyze a novel accelerated primal-dual coordinate descen...
05/23/2019 ∙ by Wenshuo Guo, et al. ∙ 0 ∙ share

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Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and ℓ_1-Convex Clustering

We propose a class of first-order gradient-type optimization algorithms ...
04/16/2019 ∙ by Nhat Ho, et al. ∙ 12 ∙ share

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On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms

In recent years, the filtering-clustering problems have been a central t...
04/16/2019 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Challenges with EM in application to weakly identifiable mixture models

We study a class of weakly identifiable location-scale mixture models fo...
02/01/2019 ∙ by Raaz Dwivedi, et al. ∙ 0 ∙ share

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On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms

We provide theoretical analyses for two algorithms that solve the regula...
01/19/2019 ∙ by Tianyi Lin, et al. ∙ 0 ∙ share

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On posterior contraction of parameters and interpretability in Bayesian mixture modeling

We study posterior contraction behaviors for parameters of interest in t...
01/15/2019 ∙ by Aritra Guha, et al. ∙ 0 ∙ share

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Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning

Unsupervised and semi-supervised learning are important problems that ar...
11/01/2018 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Probabilistic Multilevel Clustering via Composite Transportation Distance

We propose a novel probabilistic approach to multilevel clustering probl...
10/29/2018 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Singularity, Misspecification, and the Convergence Rate of EM

A line of recent work has characterized the behavior of the EM algorithm...
10/01/2018 ∙ by Raaz Dwivedi, et al. ∙ 0 ∙ share

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Multilevel Clustering via Wasserstein Means

We propose a novel approach to the problem of multilevel clustering, whi...
06/13/2017 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Singularity structures and impacts on parameter estimation in finite mixtures of distributions

Singularities of a statistical model are the elements of the model's par...
09/09/2016 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Identifiability and optimal rates of convergence for parameters of multiple types in finite mixtures

This paper studies identifiability and convergence behaviors for paramet...
01/11/2015 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Nhat Ho

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