Publications (by topics)

(* = equal contribution )
(** = alphabetical order )
( Each paper appears in a single topic )

Graphical models / Hierarchical models / Mixture models / Bayesian nonparametrics


Statistical and computational optimal transport


  • Revisiting Wasserstein barycenter: Hardness and asynchronous distributed algorithms . To be submitted, Journal of Machine Learning Research.
    Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan.

Optimization in statistical settings / Distributed computing


  • Instability, statistical inference and computational efficiency . To be submitted, Annals of Statistics.
    Nhat Ho*, Raaz Dwivedi*, Koulik Khamaru*, Martin J. Wainwright, Michael I. Jordan, Bin Yu.

Sampling and Markov chains / (Approximate) Bayesian inference


  • A partial differential equation perspective on Berstein-Von Mises theorem. To be submitted, Annals of Statistics.
    Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan.

Deep learning / Multiple testing


  • The bag-of-null-statistics procedure: An adaptive framework for selecting a better test statistics . To be submitted, Journal of the Royal Statistical Society: Series B.
    Chiao-Yu Yang, Lihua Lei, Nhat Ho, Will Fithian.

  • Distributional sliced-Wasserstein and applications to deep generative modeling. Under review, ICML.
    Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui.

  • On efficient and robust deep clustering . To be submitted, NeurIPS.
    Khiem Pham, Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui.