
Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout
Approximate inference in deep Bayesian networks exhibits a dilemma of ho...
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BoMbOT: On Batch of Minibatches Optimal Transport
Minibatch optimal transport (mOT) has been successfully used in practi...
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On the computational and statistical complexity of overparameterized matrix sensing
We consider solving the low rank matrix sensing problem with Factorized ...
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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...
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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...
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Projection Robust Wasserstein Distance and Riemannian Optimization
Projection robust Wasserstein (PRW) distance, or Wasserstein projection ...
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Probabilistic Best Subset Selection via GradientBased Optimization
In highdimensional statistics, variable selection is an optimization pr...
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Probabilistic Best Subset Selection by GradientBased Optimization
In highdimensional statistics, variable selection is an optimization pr...
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On the Minimax Optimality of the EM Algorithm for Learning TwoComponent Mixed Linear Regression
We study the convergence rates of the EM algorithm for learning twocomp...
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Uniform Convergence Rates for Maximum Likelihood Estimation under TwoComponent Gaussian Mixture Models
We derive uniform convergence rates for the maximum likelihood estimator...
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Instability, Computational Efficiency and Statistical Accuracy
Many statistical estimators are defined as the fixed point of a datadep...
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Distributional SlicedWasserstein and Applications to Generative Modeling
SlicedWasserstein distance (SWD) and its variation, Max SlicedWasserst...
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Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms
We study the fixedsupport Wasserstein barycenter problem (FSWBP), whic...
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FixedSupport Wasserstein Barycenters: Computational Hardness and Fast Algorithm
We study the fixedsupport Wasserstein barycenter problem (FSWBP), whic...
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Computational Hardness and Fast Algorithm for FixedSupport Wasserstein Barycenter
We study in this paper the fixedsupport Wasserstein barycenter problem ...
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On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
We provide a computational complexity analysis for the Sinkhorn algorith...
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Sampling for Bayesian Mixture Models: MCMC with PolynomialTime Mixing
We study the problem of sampling from the power posterior distribution i...
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On Scalable Variant of Wasserstein Barycenter
We study a variant of Wasserstein barycenter problem, which we refer to ...
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Computationally Efficient Tree Variants of GromovWasserstein
We propose two novel variants of GromovWasserstein (GW) between probabi...
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On the Complexity of Approximating Multimarginal Optimal Transport
We study the complexity of approximating the multimarginal optimal trans...
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On Efficient Multilevel Clustering via Wasserstein Distances
We propose a novel approach to the problem of multilevel clustering, whi...
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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...
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Convergence Rates for Gaussian Mixtures of Experts
We provide a theoretical treatment of overspecified Gaussian mixtures o...
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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...
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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 ...
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Accelerated PrimalDual Coordinate Descent for Computational Optimal Transport
We propose and analyze a novel accelerated primaldual coordinate descen...
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Global Error Bounds and Linear Convergence for GradientBased Algorithms for Trend Filtering and ℓ_1Convex Clustering
We propose a class of firstorder gradienttype optimization algorithms ...
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On Structured FilteringClustering: Global Error Bound and Optimal FirstOrder Algorithms
In recent years, the filteringclustering problems have been a central t...
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Challenges with EM in application to weakly identifiable mixture models
We study a class of weakly identifiable locationscale mixture models fo...
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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...
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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...
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Neural Rendering Model: Joint Generation and Prediction for SemiSupervised Learning
Unsupervised and semisupervised learning are important problems that ar...
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Probabilistic Multilevel Clustering via Composite Transportation Distance
We propose a novel probabilistic approach to multilevel clustering probl...
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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...
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Multilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, whi...
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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...
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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...
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Nhat Ho
verfied profile
Nhat Ho is an Assistant Professor of Statistics and Data Sciences at the University of Texas, Austin.