Viet Huynh

According to our database, Viet Huynh authored at least 20 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Rapid Identification of Protein Formulations with Bayesian Optimisation.

Viet Huynh, Buser Say, Peter Vogel, Lucy Cao, Geoffrey I. Webb, Aldeida Aleti

Proceedings of the International Conference on Machine Learning and Applications, 2023

2021

On efficient multilevel Clustering via Wasserstein distances.

Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung

J. Mach. Learn. Res., 2021

Text Generation with Deep Variational GAN.

Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung

CoRR, 2021

Improved and Efficient Text Adversarial Attacks using Target Information.

Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung

CoRR, 2021

Topic Modelling Meets Deep Neural Networks: A Survey.

He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Optimal Transport for Deep Generative Models: State of the Art and Research Challenges.

Viet Huynh, Dinh Q. Phung, He Zhao

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Neural Topic Model via Optimal Transport.

He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Neural Sinkhorn Topic Model.

He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine

CoRR, 2020

OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling.

Viet Huynh, He Zhao, Dinh Phung

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation.

Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Stein Variational Gradient Descent with Variance Reduction.

Nhan Dam, Trung Le, Viet Huynh, Dinh Phung

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019

On Scalable Variant of Wasserstein Barycenter.

Tam Le, Viet Huynh, Nhat Ho, Dinh Q. Phung, Makoto Yamada

CoRR, 2019

Probabilistic Multilevel Clustering via Composite Transportation Distance.

Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2017

Streaming clustering with Bayesian nonparametric models.

Viet Huynh, Dinh Q. Phung

Neurocomputing, 2017

Supervised Restricted Boltzmann Machines.

Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Multilevel Clustering via Wasserstein Means.

Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung

Proceedings of the 34th International Conference on Machine Learning, 2017

Forward-Backward Smoothing for Hidden Markov Models of Point Pattern Data.

Nhan Dam, Dinh Q. Phung, Ba-Ngu Vo, Viet Huynh

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

2016

Scalable Nonparametric Bayesian Multilevel Clustering.

Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

2015

Learning Conditional Latent Structures from Multiple Data Sources.

Viet Huynh, Dinh Q. Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

Streaming Variational Inference for Dirichlet Process Mixtures.

Viet Huynh, Dinh Q. Phung, Svetha Venkatesh

Proceedings of The 7th Asian Conference on Machine Learning, 2015