b'Nhat Ho'

research

∙ 05/25/2022

Federated Self-supervised Learning for Heterogeneous Clients

Federated Learning has become an important learning paradigm due to its ...

Disha Makhija, et al. ∙

research

∙ 05/23/2022

Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures

The Expectation-Maximization (EM) algorithm has been predominantly used ...

Tongzheng Ren, et al. ∙

research

∙ 05/16/2022

An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models

Using gradient descent (GD) with fixed or decaying step-size is standard...

Nhat Ho, et al. ∙

research

∙ 04/04/2022

Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution

The conventional sliced Wasserstein is defined between two probability m...

Khai Nguyen, et al. ∙

research

∙ 03/25/2022

Amortized Projection Optimization for Sliced Wasserstein Generative Models

Seeking informative projecting directions has been an important task in ...

Khai Nguyen, et al. ∙

research

∙ 03/01/2022

Global-Local Regularization Via Distributional Robustness

Despite superior performance in many situations, deep neural networks ar...

Hoang Phan, et al. ∙

research

∙ 02/17/2022

Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models

We revisit convergence rates for maximum likelihood estimation (MLE) und...

Tudor Manole, et al. ∙

research

∙ 02/15/2022

Architecture Agnostic Federated Learning for Neural Networks

With growing concerns regarding data privacy and rapid increase in data ...

Disha Makhija, et al. ∙

research

∙ 02/09/2022

Improving Computational Complexity in Statistical Models with Second-Order Information

It is known that when the statistical models are singular, i.e., the Fis...

Tongzheng Ren, et al. ∙

research

∙ 02/05/2022

Beyond Black Box Densities: Parameter Learning for the Deviated Components

As we collect additional samples from a data population for which a know...

Dat Do, et al. ∙

research

∙ 01/10/2022

Bayesian Consistency with the Supremum Metric

We present simple conditions for Bayesian consistency in the supremum me...

Nhat Ho, et al. ∙

research

∙ 10/29/2021

Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment

Layer-wise model fusion via optimal transport, named OTFusion, applies s...

Dang Nguyen, et al. ∙

research

∙ 10/16/2021

Transformer with a Mixture of Gaussian Keys

Multi-head attention is a driving force behind state-of-the-art transfor...

Tam Nguyen, et al. ∙

research

∙ 10/15/2021

Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent

We study the statistical and computational complexities of the Polyak st...

Tongzheng Ren, et al. ∙

research

∙ 08/22/2021

An Efficient Mini-batch Method via Partial Transportation

Mini-batch optimal transport (m-OT) has been widely used recently to dea...

Khai Nguyen, et al. ∙

research

∙ 07/22/2021

On Integral Theorems: Monte Carlo Estimators and Optimal Functions

We introduce a class of integral theorems based on cyclic functions and ...

Nhat Ho, et al. ∙

research

∙ 06/29/2021

BONuS: Multiple multivariate testing with a data-adaptivetest statistic

We propose a new adaptive empirical Bayes framework, the Bag-Of-Null-Sta...

Chiao-Yu Yang, et al. ∙

research

∙ 06/11/2021

Statistical Analysis from the Fourier Integral Theorem

Taking the Fourier integral theorem as our starting point, in this paper...

Nhat Ho, et al. ∙

research

∙ 02/16/2021

Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout

Approximate inference in deep Bayesian networks exhibits a dilemma of ho...

Son Nguyen, et al. ∙

research

∙ 02/11/2021

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

Mini-batch optimal transport (m-OT) has been successfully used in practi...

Khai Nguyen, et al. ∙

research

∙ 01/27/2021

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

We consider solving the low rank matrix sensing problem with Factorized ...

Jiacheng Zhuo, et al. ∙

research

∙ 12/28/2020

Multivariate Smoothing via the Fourier Integral Theorem and Fourier Kernel

Starting with the Fourier integral theorem, we present natural Monte Car...

Nhat Ho, et al. ∙

research

∙ 10/05/2020

Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein

Relational regularized autoencoder (RAE) is a framework to learn the dis...

Khai Nguyen, et al. ∙

research

∙ 06/12/2020

Projection Robust Wasserstein Distance and Riemannian Optimization

Projection robust Wasserstein (PRW) distance, or Wasserstein projection ...

Tianyi Lin, et al. ∙

research

∙ 06/11/2020

Probabilistic Best Subset Selection via Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...

Mingzhang Yin, et al. ∙

research

∙ 06/11/2020

Probabilistic Best Subset Selection by Gradient-Based Optimization

In high-dimensional statistics, variable selection is an optimization pr...

Mingzhang Yin, et al. ∙

research

∙ 06/04/2020

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...

Jeongyeol Kwon, et al. ∙

research

∙ 06/01/2020

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

We derive uniform convergence rates for the maximum likelihood estimator...

Tudor Manole, et al. ∙

research

∙ 05/22/2020

Instability, Computational Efficiency and Statistical Accuracy

Many statistical estimators are defined as the fixed point of a data-dep...

Nhat Ho, et al. ∙

research

∙ 02/18/2020

Distributional Sliced-Wasserstein and Applications to Generative Modeling

Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserst...

Khai Nguyen, et al. ∙

research

∙ 02/12/2020

Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms

We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...

Tianyi Lin, et al. ∙

research

∙ 02/12/2020

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm

We study the fixed-support Wasserstein barycenter problem (FS-WBP), whic...

Tianyi Lin, et al. ∙

research

∙ 02/12/2020

Computational Hardness and Fast Algorithm for Fixed-Support Wasserstein Barycenter

We study in this paper the fixed-support Wasserstein barycenter problem ...

Tianyi Lin, et al. ∙

research

∙ 02/09/2020

On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

We provide a computational complexity analysis for the Sinkhorn algorith...

Khiem Pham, et al. ∙

research

∙ 12/11/2019

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

We study the problem of sampling from the power posterior distribution i...

Wenlong Mou, et al. ∙

research

∙ 10/10/2019

On Scalable Variant of Wasserstein Barycenter

We study a variant of Wasserstein barycenter problem, which we refer to ...

Tam Le, et al. ∙

research

∙ 10/10/2019

Computationally Efficient Tree Variants of Gromov-Wasserstein

We propose two novel variants of Gromov-Wasserstein (GW) between probabi...

Tam Le, et al. ∙

research

∙ 09/30/2019

On the Complexity of Approximating Multimarginal Optimal Transport

We study the complexity of approximating the multimarginal optimal trans...

Tianyi Lin, et al. ∙

research

∙ 09/19/2019

On Efficient Multilevel Clustering via Wasserstein Distances

We propose a novel approach to the problem of multilevel clustering, whi...

Viet Huynh, et al. ∙

research

∙ 09/03/2019

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters

We show that diffusion processes can be exploited to study the posterior...

Wenlong Mou, et al. ∙

research

∙ 07/09/2019

Convergence Rates for Gaussian Mixtures of Experts

We provide a theoretical treatment of over-specified Gaussian mixtures o...

Nhat Ho, et al. ∙

research

∙ 06/01/2019

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...

Tianyi Lin, et al. ∙

research

∙ 05/23/2019

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

Dirichlet process mixture models (DPMM) play a central role in Bayesian ...

Chiao-Yu Yang, et al. ∙

research

∙ 05/23/2019

Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport

We propose and analyze a novel accelerated primal-dual coordinate descen...

Wenshuo Guo, et al. ∙

research

∙ 04/16/2019

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 ...

Nhat Ho, et al. ∙

research

∙ 04/16/2019

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

In recent years, the filtering-clustering problems have been a central t...

Nhat Ho, et al. ∙

research

∙ 02/01/2019

Challenges with EM in application to weakly identifiable mixture models

We study a class of weakly identifiable location-scale mixture models fo...

Raaz Dwivedi, et al. ∙

research

∙ 01/19/2019

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

We provide theoretical analyses for two algorithms that solve the regula...

Tianyi Lin, et al. ∙

research

∙ 01/15/2019

On posterior contraction of parameters and interpretability in Bayesian mixture modeling

We study posterior contraction behaviors for parameters of interest in t...

Aritra Guha, et al. ∙

research

∙ 11/01/2018

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

Unsupervised and semi-supervised learning are important problems that ar...

Nhat Ho, et al. ∙

Nhat Ho

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