Publications (by topics)

(* = equal contribution )
(** = alphabetical order )
( = co-last author )
( Each paper appears in a single topic )

Graphical models / Hierarchical models / Mixture models / Bayesian nonparametrics (Theories and Applications in Machine Learning and Deep Learning)

Optimal transport (Computation and Methods in Statistical Machine Learning and Deep Learning)

Optimization in statistical settings / Distributed computing

Sampling and Markov chains / (Approximate) Bayesian inference

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

Nonparametric estimation

Deep learning (Generative models, Transformer, Transfer learning, Federated Learning, Bayesian neural networks, etc.)

  • Improving Transformer with an admixture of attention heads . Advances in NeurIPS, 2022.
    Tam Nguyen*, Tan Nguyen*, Hai Do, Khai Nguyen, Vishwanath Saragadam, Minh Pham, Khuong Nguyen,Stanley Osher, Nhat Ho.

  • Stochastic multiple target sampling gradient descent . Advances in NeurIPS, 2022.
    Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung.

  • Point-set distances for learning representations of 3D point clouds. International Conference on Computer Vision (ICCV), 2021.
    Trung Nguyen, Hieu Pham, Tam Le, Nhat Ho, Tung Pham, Son Hua.