Nguyen, Vu

39 publications

AISTATS 2025 High Dimensional Bayesian Optimization Using Lasso Variable Selection Vu Viet Hoang, Hung The Tran, Sunil Gupta, Vu Nguyen

TMLR 2025 Meta-Learning Population-Based Methods for Reinforcement Learning Johannes Hog, Raghu Rajan, André Biedenkapp, Noor Awad, Frank Hutter, Vu Nguyen

ICLR 2025 SAVA: Scalable Learning-Agnostic Data Valuation Samuel Kessler, Tam Le, Vu Nguyen

AISTATS 2024 Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael A. Osborne

ACML 2024 Asian Conference on Machine Learning: Preface Vu Nguyen, Hsuan-Tien Lin

TMLR 2024 High-Dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy Lam Ngo, Huong Ha, Jeffrey Chan, Vu Nguyen, Hongyu Zhang

NeurIPS 2024 Rejection via Learning Density Ratios Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen

NeurIPS 2023 Distributionally Robust Bayesian Optimization with $\varphi$-Divergences Hisham Husain, Vu Nguyen, Anton van den Hengel

TMLR 2023 Global Contrastive Learning for Long-Tailed Classification Thong Bach, Anh Tong, Truong Son Hy, Vu Nguyen, Thanh Nguyen-Tang

AAAI 2023 Mixed-Variable Black-Box Optimisation Using Value Proposal Trees Yan Zuo, Vu Nguyen, Amir Dezfouli, David Alexander, Benjamin Ward Muir, Iadine Chades

CVPR 2023 Zero-Shot Object Counting Jingyi Xu, Hieu Le, Vu Nguyen, Viresh Ranjan, Dimitris Samaras

JAIR 2022 Automated Reinforcement Learning (AutoRL): A Survey and Open Problems Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer

AutoML 2022 Bayesian Generational Population-Based Training Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne

ICLRW 2022 Bayesian Generational Population-Based Training Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne

CVPR 2022 Retrieval Augmented Classification for Long-Tail Visual Recognition Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton van den Hengel

UAI 2021 Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen J. Roberts

ICML 2021 Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search Vu Nguyen, Tam Le, Makoto Yamada, Michael A. Osborne

ICML 2021 Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael A. Osborne

NeurIPS 2021 Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen J Roberts

ICML 2020 Bayesian Optimisation over Multiple Continuous and Categorical Inputs Binxin Ru, Ahsan Alvi, Vu Nguyen, Michael A. Osborne, Stephen Roberts

NeurIPS 2020 Bayesian Optimization for Iterative Learning Vu Nguyen, Sebastian Schulze, Michael Osborne

NeurIPS 2020 Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael Osborne, Frank Wood

ICML 2020 Knowing the What but Not the Where in Bayesian Optimization Vu Nguyen, Michael A. Osborne

NeurIPS 2020 Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits Jack Parker-Holder, Vu Nguyen, Stephen J. Roberts

ECCV 2018 A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation Hieu Le, Tomas F. Yago Vicente, Vu Nguyen, Minh Hoai, Dimitris Samaras

NeurIPS 2018 Algorithmic Assurance: An Active Approach to Algorithmic Testing Using Bayesian Optimisation Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh

ECML-PKDD 2018 Exploration Enhanced Expected Improvement for Bayesian Optimization Julian Berk, Vu Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh

IJCAI 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

JMLR 2017 Approximation Vector Machines for Large-Scale Online Learning Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

IJCAI 2017 Discriminative Bayesian Nonparametric Clustering Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui

IJCAI 2017 High Dimensional Bayesian Optimization Using Dropout Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Alistair Shilton

ICML 2017 High Dimensional Bayesian Optimization with Elastic Gaussian Process Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh

ACML 2017 Regret for Expected Improvement over the Best-Observed Value and Stopping Condition Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

ICCV 2017 Shadow Detection with Conditional Generative Adversarial Networks Vu Nguyen, Tomas F. Yago Vicente, Maozheng Zhao, Minh Hoai, Dimitris Samaras

ACML 2016 A Bayesian Nonparametric Approach for Multi-Label Classification Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

UAI 2016 Budgeted Semi-Supervised Support Vector Machine Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

NeurIPS 2016 Dual Space Gradient Descent for Online Learning Trung Le, Tu Nguyen, Vu Nguyen, Dinh Phung

ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen, Trung Le, Vu Nguyen, Tu Nguyen, Dinh Phung

AISTATS 2016 Nonparametric Budgeted Stochastic Gradient Descent Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung