Nhat Ho

发表

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁1-Convex Clustering pdf

Michael I. Jordan, Tianyi Lin, Nhat Ho, 2019, ArXiv.

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm

Michael I. Jordan, Tianyi Lin, Xi Chen, 2020, NeurIPS.

A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model

Michael I. Jordan, Richard G. Baraniuk, Anima Anandkumar, 2018, 1811.02657.

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

Martin J. Wainwright, Michael I. Jordan, Peter L. Bartlett, 2019, ArXiv.

On posterior contraction of parameters and interpretability in Bayesian mixture modeling

Nhat Ho, X. Nguyen, Aritra Guha, 2019, Bernoulli.

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

Nhat Ho, X. Nguyen, 2015, 1501.02497.

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

Michael I. Jordan, Tianyi Lin, Nhat Ho, 2019, ICML.

Multilevel Clustering via Wasserstein Means

Dinh Q. Phung, Nhat Ho, Hung Hai Bui, 2017, ICML.

On the Complexity of Approximating Multimarginal Optimal Transport pdf

Michael I. Jordan, Tianyi Lin, Marco Cuturi, 2019, J. Mach. Learn. Res..

On Scalable Variant of Wasserstein Barycenter pdf

Makoto Yamada, Dinh Q. Phung, Nhat Ho, 2019, ArXiv.

On Integral Theorems: Monte Carlo Estimators and Optimal Functions

Stephen G. Walker, Nhat Ho, Nhat Ho, 2021 .

Multivariate Smoothing via the Fourier Integral Theorem and Fourier Kernel

Nhat Ho, S. Walker, 2020, 2012.14482.

Computationally Efficient Tree Variants of Gromov-Wasserstein pdf

Makoto Yamada, Nhat Ho, Tam Le, 2019, ArXiv.

Instability, Computational Efficiency and Statistical Accuracy pdf

Martin J. Wainwright, Michael I. Jordan, Raaz Dwivedi, 2020, ArXiv.

Convergence rates of parameter estimation for some weakly identifiable finite mixtures

Nhat Ho, X. Nguyen, 2016 .

Singularity, misspecification and the convergence rate of EM

Michael I. Jordan, Bin Yu, M. Wainwright, 2018, The Annals of Statistics.

On strong identifiability and convergence rates of parameter estimation in finite mixtures

Nhat Ho, X. Nguyen, 2016 .

Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models

Martin J. Wainwright, Michael I. Jordan, Raaz Dwivedi, 2019, AISTATS.

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters

Michael I. Jordan, P. Bartlett, M. Wainwright, 2019, 1909.00966.

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

Michael I. Jordan, Nhat Ho, Chiao-Yu Yang, 2019, ArXiv.

Challenges with EM in application to weakly identifiable mixture models pdf

Martin J. Wainwright, Michael I. Jordan, Bin Yu, 2019, ArXiv.

Distributional Sliced-Wasserstein and Applications to Generative Modeling pdf

Khai Nguyen, Nhat Ho, Hung Bui, 2020, ICLR.

Projection Robust Wasserstein Distance and Riemannian Optimization pdf

Michael I. Jordan, Tianyi Lin, Marco Cuturi, 2020, NeurIPS.

Weak Separation in Mixture Models and Implications for Principal Stratification

Luke W. Miratrix, N. Pillai, Nhat Ho, 2016, AISTATS.

Robust estimation of mixing measures in finite mixture models

Y. Ritov, Nhat Ho, X. Nguyen, 2017, Bernoulli.

Parameter Estimation and Multilevel Clustering with Mixture and Hierarchical Models

Nhat Ho, 2017 .

On Deep Domain Adaptation: Some Theoretical Understandings

Dinh Q. Phung, Nhat Ho, Khanh-Duy Nguyen, 2018 .

The Latent-Dependent Deep Rendering Model

Michael I. Jordan, Ankit B. Patel, Richard Baraniuk, 2018 .

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

Michael I. Jordan, Nhat Ho, Tianyi Lin, 2019 .

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

Nhat Ho, Tudor Manole, 2020, 2006.00704.

DP-MEANS: A PROBABILISTIC OPTIMIZATION VIEW OF BAYESIAN NONPARAMETRICS

Nhat Ho, Mingdi You, Yuqing Zhou, 2013 .

On the Efficiency of Sinkhorn and Greenkhorn and Their Acceleration for Optimal Transport

Michael I. Jordan, Nhat Ho, Tianyi Lin, 2021 .

Computationally Efficient Tree Variants of Gromov

Nhat Ho, M. Yamada, 2019 .

On strong identifiability and optimal rates of parameter estimation in finite mixtures

Nhat Ho, X. Nguyen, 2015 .

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

Nhat Ho, Purnamrita Sarkar, S. Sanghavi, 2022, 2205.07999.

Transformer with Fourier Integral Attentions

Nhat Ho, Tan Nguyen, S. Osher, 2022, ArXiv.

On Transportation of Mini-batches: A Hierarchical Approach

Dinh Q. Phung, Nhat Ho, H. Bui, 2021, ICML.

Federated Self-supervised Learning for Heterogeneous Clients

Nhat Ho, J. Ghosh, Disha Makhija, 2022, ArXiv.

Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering

Nhat Ho, Tongzheng Ren, J. Ghosh, 2022, ITISE 2023.

AUTOENCODERS WITH SPHERICAL SLICED FUSED

Nhat Ho, Khai Nguyen, Gromov Wasserstein, 2021 .

Stochastic Multiple Target Sampling Gradient Descent

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, ArXiv.

Improving Transformers with Probabilistic Attention Keys

Richard Baraniuk, Nhat Ho, Tan Nguyen, 2021, ICML.

Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models

Nhat Ho, Aryan Mokhtari, Tongzheng Ren, 2022, 2206.00207.

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters

Michael I. Jordan, P. Bartlett, M. Wainwright, 2019, SIAM J. Math. Data Sci..

On strong identiﬁability and convergence rates of parameter estimation in ﬁnite mixtures

Nhat Ho, X. Nguyen, 2016 .

Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution

Nhat Ho, Khai Nguyen, 2022, NeurIPS.

A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model

Michael I. Jordan, Ankit B. Patel, Richard Baraniuk, 2018, 1811.02657.

Improving Generative Flow Networks with Path Regularization

Khuong N. Nguyen, Nhat Ho, S. Osher, 2022, arXiv.org.

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

Nhat Ho, S. Sanghavi, Tongzheng Ren, 2022, ArXiv.

Robustify Transformers with Robust Kernel Density Estimation

Nhat Ho, Khai Nguyen, J. Ghosh, 2022, ArXiv.

Stochastic Multiple Target Sampling Gradient Descent

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, ArXiv.

Beyond Black Box Densities: Parameter Learning for the Deviated Components

Nhat Ho, X. Nguyen, Dat Do, 2022, NeurIPS.

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

Lam C. Tran, Dinh Q. Phung, Nhat Ho, 2022, ArXiv.

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

Duy M. H. Nguyen, Binh Duc Nguyen, P. Xie, 2022, 2212.01893.

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

Khuong N. Nguyen, Nhat Ho, T. Nguyen, 2022, ArXiv.

Probabilistic Best Subset Selection by Gradient-Based Optimization

Nhat Ho, Bowei Yan, Mingyuan Zhou, 2020, 2006.06448.

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

Nhat Ho, Hien Dang, T. Nguyen, 2023, ArXiv.

Markovian Sliced Wasserstein Distances: Beyond Independent Projections

Nhat Ho, Khai Nguyen, Tongzheng Ren, 2023, 2301.03749.

Hierarchical Sliced Wasserstein Distance

Nhat Ho, Khai Nguyen, Tongzheng Ren, 2022, ArXiv.

Projection Robust Wasserstein Distance and Riemannian Optimization

Michael I. Jordan, Nhat Ho, Tianyi Lin, 2020, NeurIPS.

Flow-based Alignment Approaches for Probability Measures in Different Spaces

Makoto Yamada, Nhat Ho, Tam Le, 2019, AISTATS.

Singularity Structures and Impacts on Parameter Estimation in Finite Mixtures of Distributions

Nhat Ho, XuanLong Nguyen, Nhat Ho, 2016, SIAM J. Math. Data Sci..

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

Vinh Phu Nguyen, Khuong N. Nguyen, Nhat Ho, 2022, ArXiv.

Entropic Gromov-Wasserstein between Gaussian Distributions

Nhat Ho, Tung Pham, Dung Le, 2021, ICML.

Improving Mini-batch Optimal Transport via Partial Transportation

Khai Nguyen, Nhat Ho, Dang Nguyen, 2021, ICML.

An Efficient Mini-batch Method via Partial Transportation pdf

Khai Nguyen, Nhat Ho, Dang Nguyen, 2021, ArXiv.

On Robust Optimal Transport: Computational Complexity and Barycenter Computation

Huy L. Nguyen, Nhat Ho, Hung Bui, 2021, NeurIPS.

Probabilistic Best Subset Selection via Gradient-Based Optimization

Xiaoning Qian, Mingyuan Zhou, Nhat Ho, 2020, 2006.06448.

Global-Local Regularization Via Distributional Robustness

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, AISTATS.

Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein pdf

Khai Nguyen, Nhat Ho, Hung Bui, 2020, ICLR.

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

Michael I. Jordan, Tianyi Lin, Nhat Ho, 2019, AISTATS.

On Robust Optimal Transport: Computational Complexity, Low-rank Approximation, and Barycenter Computation pdf

Tung Pham, Nhat Ho, Hung Bui, 2021, ArXiv.

On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

Nhat Ho, H. Bui, Tung Pham, 2020, ICML.

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

Constantine Caramanis, Nhat Ho, Jeongyeol Kwon, 2020, AISTATS.

Convergence Rates for Gaussian Mixtures of Experts pdf

Michael I. Jordan, Nhat Ho, Chiao-Yu Yang, 2019, J. Mach. Learn. Res..

Architecture Agnostic Federated Learning for Neural Networks

Nhat Ho, J. Ghosh, Disha Makhija, 2022, ICML.

LAMDA: Label Matching Deep Domain Adaptation

Trung Le, Dinh Phung, Nhat Ho, 2021, ICML.

Theoretical guarantees for EM under misspecified Gaussian mixture models

Martin J. Wainwright, Michael I. Jordan, Raaz Dwivedi, 2018, NeurIPS.

Hierarchical Sliced Wasserstein Distance

Nhat Ho, Khai Nguyen, Tongzheng Ren, 2022, ICLR.

On the Efficiency of Entropic Regularized Algorithms for Optimal Transport

Michael I. Jordan, Nhat Ho, Tianyi Lin, 2019, J. Mach. Learn. Res..

Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes.

Makoto Yamada, Dinh Q. Phung, Nhat Ho, 2019, 1910.04483.

Stochastic Multiple Target Sampling Gradient Descent

Dinh Q. Phung, Nhat Ho, Trung Le, 2022, NeurIPS.

Structured Dropout Variational Inference for Bayesian Neural Networks

Khai Nguyen, Khoat Than, Nhat Ho, 2021, NeurIPS.

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

Nhat Ho, Hien Dang, T. Nguyen, 2023, ICML.

On Efficient Multilevel Clustering via Wasserstein Distances

Dinh Q. Phung, Nhat Ho, X. Nguyen, 2019, J. Mach. Learn. Res..

Instability, Computational Efficiency and Statistical Accuracy

Michael I. Jordan, Bin Yu, M. Wainwright, 2020, ArXiv.

Instability , Computational Efficiency and Statistical Accuracy Instability , Computational Efficiency and Statistical Accuracy

B. Schölkopf, Nhat Ho, B. Scholkopf, 2022 .

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

Michael I. Jordan, Richard G. Baraniuk, Anima Anandkumar, 2018, ArXiv.

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Binh Duc Nguyen, P. Xie, Nhat Ho, 2023, ArXiv.

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

Vinh Phu Nguyen, Khuong N. Nguyen, Nhat Ho, 2022, ICML.

Point-set Distances for Learning Representations of 3D Point Clouds pdf

Tung Pham, Binh-Son Hua, Quang-Hieu Pham, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Amortized Projection Optimization for Sliced Wasserstein Generative Models

Nhat Ho, Khai Nguyen, 2022, NeurIPS.

Energy-Based Sliced Wasserstein Distance

Nhat Ho, Khai Nguyen, 2023, ArXiv.

Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter

Michael I. Jordan, Nhat Ho, Wenshuo Guo, 2019, AISTATS.

Posterior Collapse in Linear Conditional and Hierarchical Variational Autoencoders

Nhat Ho, T. Nguyen, Tho Tran, 2023, ArXiv.

Optimal Rate for Parameter Estimation in Matrix-variate Deviated Models

Nhat Ho, Khai Nguyen, Huy Nguyen, 2023, 2301.11808.

Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models

Nhat Ho, Tudor Manole, 2022, ICML.

FourierFormer: Transformer Meets Generalized Fourier Integral Theorem

Nhat Ho, S. Osher, Khai Nguyen, 2022, NeurIPS.

Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout pdf

Khai Nguyen, Khoat Than, Nhat Ho, 2021, ArXiv.

Probabilistic Multilevel Clustering via Composite Transportation Distance

Michael I. Jordan, Dinh Q. Phung, Nhat Ho, 2018, AISTATS.

A Primal-Dual Framework for Transformers and Neural Networks

Richard Baraniuk, Nhat Ho, S. Osher, 2023, ICLR.

BONuS: Multiple multivariate testing with a data-adaptivetest statistic

Lihua Lei, Nhat Ho, Chiao-Yu Yang, 2021, 2106.15743.

On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity

Nhat Ho, Tung Pham, Huy Nguyen, 2021, AISTATS.

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

Constantine Caramanis, Jiacheng Zhuo, Nhat Ho, 2021, ArXiv.

Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts

Nhat Ho, TrungTin Nguyen, Khai Nguyen, 2023, ArXiv.

Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts

Nhat Ho, Fanqi Yan, Huy Nguyen, 2023, ArXiv.

Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models

Nhat Ho, Khai Nguyen, Huy Nguyen, 2023, 2301.11808.

Improving Computational Complexity in Statistical Models with Second-Order Information

Nhat Ho, S. Sanghavi, Tongzheng Ren, 2022, ArXiv.

Quasi-Monte Carlo for 3D Sliced Wasserstein

Nhat Ho, Khai Nguyen, Nicola Bariletto, 2023, ArXiv.

Diffeomorphic Deformation via Sliced Wasserstein Distance Optimization for Cortical Surface Reconstruction

Nhat Ho, Xiaohui Xie, Khai Nguyen, 2023, ArXiv.

Control Variate Sliced Wasserstein Estimators

Nhat Ho, Khai Nguyen, 2023, ArXiv.

Energy-Based Sliced Wasserstein Distance

Nhat Ho, Khai Nguyen, 2023, ArXiv.

Markovian Sliced Wasserstein Distances: Beyond Independent Projections

Nhat Ho, Khai Nguyen, Tongzheng Ren, 2023, ArXiv.

Fast Approximation of the Generalized Sliced-Wasserstein Distance

Nhat Ho, Nhat Ho, Khai Nguyen, 2022, ArXiv.

Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms pdf

Michael I. Jordan, Tianyi Lin, Xi Chen, 2020, ArXiv.

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

Dinh Q. Phung, Nhat Ho, H. Bui, 2021, ArXiv.

Demystifying Softmax Gating Function in Gaussian Mixture of Experts

Nhat Ho, TrungTin Nguyen, Huy Nguyen, 2023, NeurIPS.

Transformer with a Mixture of Gaussian Keys pdf

Richard G. Baraniuk, Nhat Ho, Stanley J. Osher, 2021, ArXiv.

Improving Transformer with an Admixture of Attention Heads

Nhat Ho, S. Osher, Minh Pham, 2022, NeurIPS.

Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models

Michael I. Jordan, Bin Yu, M. Wainwright, 2019, AISTATS.