b'Nhat Ho'

research

∙ 04/30/2023

Control Variate Sliced Wasserstein Estimators

The sliced Wasserstein (SW) distances between two probability measures a...

5 Khai Nguyen, et al. ∙

research

∙ 04/26/2023

Energy-Based Sliced Wasserstein Distance

The sliced Wasserstein (SW) distance has been widely recognized as a sta...

8 Khai Nguyen, et al. ∙

research

∙ 01/27/2023

On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances

Dirichlet Process mixture models (DPMM) in combination with Gaussian ker...

0 Aritra Guha, et al. ∙

research

∙ 01/12/2023

Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction

Max sliced Wasserstein (Max-SW) distance has been widely known as a solu...

5 Khai Nguyen, et al. ∙

research

∙ 01/10/2023

Markovian Sliced Wasserstein Distances: Beyond Independent Projections

Sliced Wasserstein (SW) distance suffers from redundant projections due ...

4 Khai Nguyen, et al. ∙

research

∙ 01/01/2023

Neural Collapse in Deep Linear Network: From Balanced to Imbalanced Data

Modern deep neural networks have achieved superhuman performance in task...

8 Hien Dang, et al. ∙

research

∙ 12/04/2022

Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering

Collecting large-scale medical datasets with fully annotated samples for...

10 Duy M. H. Nguyen, et al. ∙

research

∙ 11/28/2022

Revisiting Over-smoothing and Over-squashing using Ollivier's Ricci Curvature

Graph Neural Networks (GNNs) had been demonstrated to be inherently susc...

0 Khang Nguyen, et al. ∙

research

∙ 11/24/2022

Improving Multi-task Learning via Seeking Task-based Flat Regions

Multi-Task Learning (MTL) is a widely-used and powerful learning paradig...

0 Hoang Phan, et al. ∙

research

∙ 10/11/2022

Robustify Transformers with Robust Kernel Density Estimation

Recent advances in Transformer architecture have empowered its empirical...

9 Xing Han, et al. ∙

research

∙ 09/29/2022

Improving Generative Flow Networks with Path Regularization

Generative Flow Networks (GFlowNets) are recently proposed models for le...

12 Anh Do, et al. ∙

research

∙ 06/04/2022

Stochastic Multiple Target Sampling Gradient Descent

Sampling from an unnormalized target distribution is an essential proble...

9 Hoang Phan, et al. ∙

research

∙ 06/01/2022

Transformer with Fourier Integral Attentions

Multi-head attention empowers the recent success of transformers, the st...

79 Tan Nguyen, et al. ∙

research

∙ 05/27/2022

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering

We propose a novel approach to the problem of clustering hierarchically ...

12 Xing Han, et al. ∙

research

∙ 05/25/2022

Federated Self-supervised Learning for Heterogeneous Clients

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

0 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 ...

5 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...

12 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...

11 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 ...

2 Khai Nguyen, et al. ∙

research

∙ 03/01/2022

Global-Local Regularization Via Distributional Robustness

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

0 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...

0 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 ...

0 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...

14 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...

0 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...

0 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...

11 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...

9 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...

0 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...

7 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 ...

0 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...

0 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...

0 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...

44 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...

12 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 ...

0 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...

0 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...

3 Khai Nguyen, et al. ∙

research

∙ 06/12/2020

Projection Robust Wasserstein Distance and Riemannian Optimization

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

0 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...

0 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...

0 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...

11 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...

0 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...

13 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...

4 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...

4 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...

0 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 ...

0 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...

0 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...

27 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 ...

8 Tam Le, et al. ∙

Nhat Ho

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