57 citations · 432 across the 81 of their papers we have counts for
7 papers · 2 filters
Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing
Wenlong Mou, Nhat Ho, Martin J. Wainwright +2
We study the problem of sampling from the power posterior distribution in Bayesian Gaussian mixture models, a robust version of the classical posterior. This power posterior is kno…
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes
Tam Le, Viet Huynh, Nhat Ho +2
We study in this paper a variant of Wasserstein barycenter problem, which we refer to as tree-Wasserstein barycenter, by leveraging a specific class of ground metrics, namely tree…
Flow-based Alignment Approaches for Probability Measures in Different Spaces
Tam Le, Nhat Ho, Makoto Yamada
Gromov-Wasserstein (GW) is a powerful tool to compare probability measures whose supports are in different metric spaces. GW suffers however from a computational drawback since it…
On the Complexity of Approximating Multimarginal Optimal Transport
Tianyi Lin, Nhat Ho, Marco Cuturi +1
We study the complexity of approximating the multimarginal optimal transport (MOT) distance, a generalization of the classical optimal transport distance, considered here between $…
On Efficient Multilevel Clustering via Wasserstein Distances
Viet Huynh, Nhat Ho, Nhan Dam +4
We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a pote…
Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models
Chiao-Yu Yang, Eric Xia, Nhat Ho +1
Dirichlet process mixture models (DPMM) play a central role in Bayesian nonparametrics, with applications throughout statistics and machine learning. DPMMs are generally used in cl…
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
Nhat Ho, Tianyi Lin, Michael I. Jordan
The filtering-clustering models, including trend filtering and convex clustering, have become an important source of ideas and modeling tools in machine learning and related fields…