10 papers
Joint Estimation and Feature Selection in Overspecified Finite Mixture of Regressions
Abbas Khalili, Pengqi Liu, Huy Nguyen +1
We investigate joint estimation and feature selection in sparse finite mixtures of regressions with an overspecified number of mixture components or mixture order. More specificall…
Characterizing Heterogeneous Rates in Finite Mixture Estimation via Partial Optimal Transport
Dung Le, Huy Nguyen, Trang Pham +2
Parameter estimation in finite mixture models can exhibit highly heterogeneous convergence behavior: locally isolated components may be estimated substantially faster than groups o…
Partial Differential Equation Barriers to Identifiability in Infinite Mixture Models
Dung Le, Nicola Bariletto, Alessandro Rinaldo +1
We study identifiability of mixing measures in infinite mixture models. We show that, in many common cases, lack of identifiability can be characterized in terms of certain differe…
Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
Nicola Bariletto, Dung Le, Alessandro Rinaldo +1
We study posterior contraction rates for mixing measures in homoscedastic location-scale mixture models with infinitely many components. While posterior convergence at the level of…
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…
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…
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…
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…