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Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL


Jun 30, 2021
Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen Roberts


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Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces


Feb 14, 2021
Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael A. Osborne

* 9 page, 6 figures (26 pages, 13 figures, 2 tables including references and appendices) 

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Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective


Oct 31, 2020
Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood

* NeurIPS 2020 

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Gaussian Process Bandit Optimization of theThermodynamic Variational Objective


Oct 29, 2020
Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood

* NeurIPS 2020 

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Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search


Jun 13, 2020
Vu Nguyen, Tam Le, Makoto Yamada, Michael A Osborne

* 21 pages 

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Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling


Feb 26, 2020
Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Antonio Robles-Kelly, Svetha Venkatesh


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One-Shot Bayes Opt with Probabilistic Population Based Training


Feb 06, 2020
Jack Parker-Holder, Vu Nguyen, Stephen Roberts


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Indian Buffet Neural Networks for Continual Learning


Dec 04, 2019
Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen Roberts

* Camera-ready submission 

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Bayesian Optimization for Iterative Learning


Oct 07, 2019
Vu Nguyen, Sebastian Schulze, Michael A Osborne

* 17 pages 

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Accelerating Experimental Design by Incorporating Experimenter Hunches


Jul 22, 2019
Cheng Li, Santu Rana, Sunil Gupta, Vu Nguyen, Svetha Venkatesh, Alessandra Sutti, David Rubin, Teo Slezak, Murray Height, Mazher Mohammed, Ian Gibson

* IEEE International Conference on Data Mining (ICDM) 2018 

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Bayesian Optimisation over Multiple Continuous and Categorical Inputs


Jun 20, 2019
Binxin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J Roberts

* 15 pages, 8 figures 

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Knowing The What But Not The Where in Bayesian Optimization


May 11, 2019
Vu Nguyen, Michael A. Osborne

* 15 pages 

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Practical Batch Bayesian Optimization for Less Expensive Functions


Nov 05, 2018
Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

* 8 pages 

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A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation


Jul 27, 2018
Hieu Le, Tomas F. Yago Vicente, Vu Nguyen, Minh Hoai, Dimitris Samaras


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High Dimensional Bayesian Optimization Using Dropout


Feb 15, 2018
Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Alistair Shilton

* 7 pages; Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence 2017 

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Approximation Vector Machines for Large-scale Online Learning


May 28, 2017
Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

* 54 pages 

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Budgeted Batch Bayesian Optimization With Unknown Batch Sizes


Apr 15, 2017
Vu Nguyen, Santu Rana, Sunil Gupta, Cheng Li, Svetha Venkatesh

* 24 pages 

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Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images


Apr 10, 2017
Le Hou, Vu Nguyen, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Tianhao Zhao, Joel H. Saltz


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Scalable Semi-supervised Learning with Graph-based Kernel Machine


Apr 06, 2017
Trung Le, Khanh Nguyen, Van Nguyen, Vu Nguyen, Dinh Phung

* 21 pages 

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Geodesic Distance Histogram Feature for Video Segmentation


Mar 31, 2017
Hieu Le, Vu Nguyen, Chen-Ping Yu, Dimitris Samaras


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Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts


Jan 29, 2014
Vu Nguyen, Dinh Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui

* Full version of ICML 2014 

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