Vu Nguyen

According to our database, Vu Nguyen authored at least 44 papers between 2012 and 2024.

Collaborative distances:

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

2024

Self-Supervision Improves Diffusion Models for Tabular Data Imputation.

Yixin Liu, Thalaiyasingam Ajanthan, Hisham Husain, Vu Nguyen

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2021

Personalized brain stimulation for effective neurointervention across participants.

Nienke E. R. van Bueren, Thomas L. Reed, Vu Nguyen, James G. Sheffield, Sanne H. G. van der Ven, Michael A. Osborne, Evelyn H. Kroesbergen, Roi Cohen Kadosh

PLoS Comput. Biol., 2021

2020

Bayesian optimisation in unknown bounded search domains.

Julian Berk, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh

Knowl. Based Syst., 2020

Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling.

Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Antonio Robles-Kelly, Svetha Venkatesh

CoRR, 2020

2019

Filtering Bayesian optimization approach in weakly specified search space.

Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

Knowl. Inf. Syst., 2019

GoGP: scalable geometric-based Gaussian process for online regression.

Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Knowl. Inf. Syst., 2019

Efficient Bayesian Optimization for Uncertainty Reduction Over Perceived Optima Locations.

Vu Nguyen, Sunil Gupta, Santu Rana, My T. Thai, Cheng Li, Svetha Venkatesh

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Bayesian Optimization for Accelerating Hyper-Parameter Tuning.

Vu Nguyen

Proceedings of the 2nd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, 2019

2018

Practical Batch Bayesian Optimization for Less Expensive Functions.

Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

CoRR, 2018

Exploration Enhanced Expected Improvement for Bayesian Optimization.

Julian Berk, Vu Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation.

Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning.

Xiao Zhang, Wenzhong Li, Vu Nguyen, Fuzhen Zhuang, Hui Xiong, Sanglu Lu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Accelerating Experimental Design by Incorporating Experimenter Hunches.

Cheng Li, Santu Rana, Sunil Gupta, Vu Nguyen, Svetha Venkatesh, Alessandra Sutti, David Rubin de Celis Leal, Teo Slezak, Murray Height, Mazher Mohammed, Ian Gibson

Proceedings of the IEEE International Conference on Data Mining, 2018

2017

A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process.

Nguyen Cong Thuong, Vu Nguyen, Flora D. Salim, Duc Viet Le, Dinh Q. Phung

Pervasive Mob. Comput., 2017

Approximation Vector Machines for Large-scale Online Learning.

Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

J. Mach. Learn. Res., 2017

Budgeted Batch Bayesian Optimization With Unknown Batch Sizes.

Vu Nguyen, Santu Rana, Sunil Gupta, Cheng Li, Svetha Venkatesh

CoRR, 2017

Discriminative Bayesian Nonparametric Clustering.

Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

High Dimensional Bayesian Optimization using Dropout.

Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Alistair Shilton

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

High Dimensional Bayesian Optimization with Elastic Gaussian Process.

Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh

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

Bayesian Optimization in Weakly Specified Search Space.

Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

GoGP: Fast Online Regression with Gaussian Processes.

Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Regret for Expected Improvement over the Best-Observed Value and Stopping Condition.

Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

Proceedings of The 9th Asian Conference on Machine Learning, 2017

2016

Scalable Support Vector Machine for Semi-supervised Learning.

Trung Le, Khanh Nguyen, Van Nguyen, Vu Nguyen, Dinh Q. Phung

CoRR, 2016

Budgeted Semi-supervised Support Vector Machine .

Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

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

SECC: Simultaneous extraction of context and community from pervasive signals.

Nguyen Cong Thuong, Vu Nguyen, Flora D. Salim, Dinh Q. Phung

Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications, 2016

Learning Multi-faceted Activities from Heterogeneous Data with the Product Space Hierarchical Dirichlet Processes.

Nguyen Thanh Binh, Vu Nguyen, Svetha Venkatesh, Dinh Q. Phung

Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining, 2016

Sparse Adaptive Multi-hyperplane Machine.

Khanh Nguyen, Trung Le, Vu Nguyen, Dinh Q. Phung

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2016

Dual Space Gradient Descent for Online Learning.

Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

MCNC: Multi-Channel Nonparametric Clustering from heterogeneous data.

Nguyen Thanh Binh, Vu Nguyen, Svetha Venkatesh, Dinh Q. Phung

Proceedings of the 23rd International Conference on Pattern Recognition, 2016

Distributed data augmented support vector machine on Spark.

Tu Dinh Nguyen, Vu Nguyen, Trung Le, Dinh Q. Phung

Proceedings of the 23rd International Conference on Pattern Recognition, 2016

Multiple adverse effects prediction in longitudinal cancer treatment.

Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, David Ashely, Trish Livingston

Proceedings of the 23rd International Conference on Pattern Recognition, 2016

Budgeted Batch Bayesian Optimization.

Vu Nguyen, Santu Rana, Sunil Kumar Gupta, Cheng Li, Svetha Venkatesh

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems.

Vu Nguyen, Tu Dinh Nguyen, Trung Le, Svetha Venkatesh, Dinh Q. Phung

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Learning Multifaceted Latent Activities from Heterogeneous Mobile Data.

Nguyen Thanh Binh, Vu Nguyen, Nguyen Cong Thuong, Svetha Venkatesh, Mohan Kumar, Dinh Q. Phung

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Cascade Bayesian Optimization.

Thanh Dai Nguyen, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Kyle J. Deane, Paul G. Sanders

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

Nonparametric Budgeted Stochastic Gradient Descent.

Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Multiple Kernel Learning with Data Augmentation.

Khanh Nguyen, Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung

Proceedings of The 8th Asian Conference on Machine Learning, 2016

A Bayesian Nonparametric Approach for Multi-label Classification.

Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

Proceedings of The 8th Asian Conference on Machine Learning, 2016

2015

A Bayesian Nonparametric Approach to Multilevel Regression.

Vu Nguyen, Dinh Q. Phung, Svetha Venkatesh, Hung Hai Bui

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

2014

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts.

Tien-Vu Nguyen, Dinh Quoc Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Bui

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Interactive browsing system for anomaly video surveillance.

Tien-Vu Nguyen, Dinh Q. Phung, Sunil Gupta, Svetha Venkatesh

Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, 2013

Topic Model Kernel: An Empirical Study towards Probabilistically Reduced Features for Classification.

Tien-Vu Nguyen, Dinh Q. Phung, Svetha Venkatesh

Proceedings of the Neural Information Processing - 20th International Conference, 2013

2012

Higher Order Conditional Random Field for Multi-Label Interactive Image Segmentation.

Tien-Vu Nguyen, Nghia Pham, Trung Tran, Bac Le

Proceedings of the 2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), Ho Chi Minh City, Vietnam, February 27, 2012

Multi-modal abnormality detection in video with unknown data segmentation.

Tien-Vu Nguyen, Dinh Q. Phung, Santu Rana, Duc-Son Pham, Svetha Venkatesh

Proceedings of the 21st International Conference on Pattern Recognition, 2012