9 citations · 39 across the 37 of their papers we have counts for
37 papers
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…
Streaming Sliced Optimal Transport
Khai Nguyen
Sliced optimal transport (SOT), or sliced Wasserstein (SW) distance, is widely recognized for its statistical and computational scalability. In this work, we further enhance comput…
Improving Routing in Sparse Mixture of Experts with Graph of Tokens
Tam Nguyen, Ngoc N. Tran, Khai Nguyen +1
Sparse Mixture of Experts (SMoE) has emerged as a key to achieving unprecedented scalability in deep learning. By activating only a small subset of parameters per sample, SMoE achi…
Bayesian Multivariate Density-Density Regression
Khai Nguyen, Yang Ni, Peter Mueller
We introduce a novel and scalable Bayesian framework for multivariate-density-density regression (DDR), designed to model relationships between multivariate distributions. Our appr…
Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning
Manh Luong, Khai Nguyen, Dinh Phung +2
Audio captioning systems face a fundamental challenge: teacher-forcing training creates exposure bias that leads to caption degeneration during inference. While contrastive methods…
Lightspeed Geometric Dataset Distance via Sliced Optimal Transport
Khai Nguyen, Hai Nguyen, Tuan Pham +1
We introduce sliced optimal transport dataset distance (s-OTDD), a model-agnostic, embedding-agnostic approach for dataset comparison that requires no training, is robust to variat…
Summarizing Bayesian Nonparametric Mixture Posterior -- Sliced Optimal Transport Metrics for Gaussian Mixtures
Khai Nguyen, Peter Mueller
Existing methods to summarize posterior inference for mixture models focus on identifying a point estimate of the implied random partition for clustering, with density estimation a…
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
Manh Luong, Khai Nguyen, Nhat Ho +3
The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport approach for learning the underlying ground metric between two sources of data, facilitati…
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…
Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions
Khai Nguyen, Nhat Ho
Sliced Wasserstein (SW) and Generalized Sliced Wasserstein (GSW) have been widely used in applications due to their computational and statistical scalability. However, the SW and t…
Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning
Tung Le, Khai Nguyen, Shanlin Sun +2
In the realm of computer vision and graphics, accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking, registration, te…
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…
Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction
Tung Le, Khai Nguyen, Shanlin Sun +3
Mesh deformation plays a pivotal role in many 3D vision tasks including dynamic simulations, rendering, and reconstruction. However, defining an efficient discrepancy between predi…
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…
Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models
Dat Do, Huy Nguyen, Khai Nguyen +1
We study the maximum likelihood estimation (MLE) in the multivariate deviated model where the data are generated from the density function $(1-λ^{\ast})h_{0}(x)+λ^{\ast}f(x|μ^{\ast…
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…