9 citations · 39 across the 37 of their papers we have counts for
21 papers · 1 filter
Amortized Optimal Transport from Sliced Potentials
Minh-Phuc Truong, Khai Nguyen
We propose a novel amortized optimization method for predicting optimal transport (OT) plans across multiple pairs of measures by leveraging Kantorovich potentials derived from sli…
Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances
Khai Nguyen, Hai Nguyen, Nhat Ho
We address the problem of efficiently computing Wasserstein distances for multiple pairs of distributions drawn from a meta-distribution. To this end, we propose a fast estimation…
An Introduction to Sliced Optimal Transport
Khai Nguyen
Sliced Optimal Transport (SOT) is a rapidly developing branch of optimal transport (OT) that exploits the tractability of one-dimensional OT problems. By combining tools from OT, i…
Data-Driven DRO and Economic Decision Theory: An Analytical Synthesis With Bayesian Nonparametric Advancements
Nicola Bariletto, Khai Nguyen, Nhat Ho
We develop an analytical synthesis that bridges data-driven Distributionally Robust Optimization (DRO) and Economic Decision Theory under Ambiguity (DTA). By reinterpreting standar…
Towards Marginal Fairness Sliced Wasserstein Barycenter
Khai Nguyen, Hai Nguyen, Nhat Ho
The sliced Wasserstein barycenter (SWB) is a widely acknowledged method for efficiently generalizing the averaging operation within probability measure spaces. However, achieving m…
On Parameter Estimation in Deviated Gaussian Mixture of Experts
Huy Nguyen, Khai Nguyen, Nhat Ho
We consider the parameter estimation problem in the deviated Gaussian mixture of experts in which the data are generated from $(1 - λ^{\ast}) g_0(Y| X)+ λ^{\ast} \sum_{i = 1}^{k_{\…
Sliced Wasserstein with Random-Path Projecting Directions
Khai Nguyen, Shujian Zhang, Tam Le +1
Slicing distribution selection has been used as an effective technique to improve the performance of parameter estimators based on minimizing sliced Wasserstein distance in applica…
Quasi-Monte Carlo for 3D Sliced Wasserstein
Khai Nguyen, Nicola Bariletto, Nhat Ho
Monte Carlo (MC) integration has been employed as the standard approximation method for the Sliced Wasserstein (SW) distance, whose analytical expression involves an intractable ex…
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts
Huy Nguyen, TrungTin Nguyen, Khai Nguyen +1
Originally introduced as a neural network for ensemble learning, mixture of experts (MoE) has recently become a fundamental building block of highly successful modern deep neural n…
Sliced Wasserstein Estimation with Control Variates
Khai Nguyen, Nhat Ho
The sliced Wasserstein (SW) distances between two probability measures are defined as the expectation of the Wasserstein distance between two one-dimensional projections of the two…
Energy-Based Sliced Wasserstein Distance
Khai Nguyen, Nhat Ho
The sliced Wasserstein (SW) distance has been widely recognized as a statistically effective and computationally efficient metric between two probability measures. A key component…
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Khai Nguyen, Dang Nguyen, Nhat Ho
Max sliced Wasserstein (Max-SW) distance has been widely known as a solution for less discriminative projections of sliced Wasserstein (SW) distance. In applications that have vari…
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Khai Nguyen, Tongzheng Ren, Nhat Ho
Sliced Wasserstein (SW) distance suffers from redundant projections due to independent uniform random projecting directions. To partially overcome the issue, max K sliced Wasserste…
Fast Approximation of the Generalized Sliced-Wasserstein Distance
Dung Le, Huy Nguyen, Khai Nguyen +2
Generalized sliced Wasserstein distance is a variant of sliced Wasserstein distance that exploits the power of non-linear projection through a given defining function to better cap…
Hierarchical Sliced Wasserstein Distance
Khai Nguyen, Tongzheng Ren, Huy Nguyen +3
Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of di…
Amortized Projection Optimization for Sliced Wasserstein Generative Models
Khai Nguyen, Nhat Ho
Seeking informative projecting directions has been an important task in utilizing sliced Wasserstein distance in applications. However, finding these directions usually requires an…
Improving Mini-batch Optimal Transport via Partial Transportation
Khai Nguyen, Dang Nguyen, The-Anh Vu-Le +2
Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications. Despite their practicality, m-OT suffers from mis…
On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity
Khang Le, Huy Nguyen, Tung Pham +1
We study the multi-marginal partial optimal transport (POT) problem between discrete (unbalanced) measures with at most supports. We first prove that we can obtain two equi…
On Transportation of Mini-batches: A Hierarchical Approach
Khai Nguyen, Dang Nguyen, Quoc Nguyen +5
Mini-batch optimal transport (m-OT) has been successfully used in practical applications that involve probability measures with a very high number of supports. The m-OT solves seve…
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
Khai Nguyen, Son Nguyen, Nhat Ho +2
Relational regularized autoencoder (RAE) is a framework to learn the distribution of data by minimizing a reconstruction loss together with a relational regularization on the laten…
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Khai Nguyen, Nhat Ho, Tung Pham +1
Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-SW), have been used widely in the recent years due to their fast computation and scalability…