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Vu Nguyen

34 papers · 2016–2025 · 15 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (9) 🐝 Cross-Pollinator (14) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (15) πŸ—ΊοΈ Taxonomy Completionist (15) 🌈 Renaissance Researcher (6) 🧭 Keyword Pioneer 🌱 Topic Pioneer πŸ† Grand Slam πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (3) πŸ—ƒοΈ Keyword Collector (134) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (34) πŸ”₯ Unstoppable (6) ⚑ Prolific Year (5)

Conferences

NIPS (8) ICML (5) ACML (4) AISTATS (3) IJCAI (3) CVPR (2) AAAI (1) AUTOML (1) ECCV (1) EMNLP (1) ICCV (1) ICLR (1) JMLR (1) MIDL (1) UAI (1)

Top co-authors

Sunil Gupta (6) Svetha Venkatesh (5) Michael A. Osborne (5) Santu Rana (5) Dinh Phung (5) Trung Le (5) Dimitris Samaras (4) Cheng Li (4) Michael Osborne (3) Binxin Ru (3)

Keywords

bayesian optimization (10) gaussian process (7) hyperparameter optimization (6) acquisition function (4) reinforcement learning (3) regret bound (3) bayesian nonparametric (3) kernel online learning (3) hyperparameter tuning (3) bayesian optimisation (3) black-box optimization (3) multi-armed bandit (2) variational autoencoder (2) high-dimensional optimization (2) image classification (2) active learning (2) multi-label classification (2) stochastic gradient descent (2) variational inference (2) data augmentation (2)

Papers

SAVA: Scalable Learning-Agnostic Data Valuation ICLR 2025
High Dimensional Bayesian Optimization using Lasso Variable Selection AISTATS 2025
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach AISTATS 2024
Asian Conference on Machine Learning: Preface ACML 2024
Rejection via Learning Density Ratios NIPS 2024
Few Shot Hematopoietic Cell Classification MIDL 2023
Distributionally Robust Bayesian Optimization with $\varphi$-divergences NIPS 2023
Zero-Shot Object Counting CVPR 2023
Mixed-Variable Black-Box Optimisation Using Value Proposal Trees AAAI 2023
Bayesian Generational Population-Based Training AUTOML 2022
Retrieval Augmented Classification for Long-Tail Visual Recognition CVPR 2022
Hierarchical Indian buffet neural networks for Bayesian continual learning UAI 2021
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL NIPS 2021
Bayesian Topic Regression for Causal Inference EMNLP 2021
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search ICML 2021
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces ICML 2021
Knowing The What But Not The Where in Bayesian Optimization ICML 2020
Bayesian Optimisation over Multiple Continuous and Categorical Inputs ICML 2020
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits NIPS 2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective NIPS 2020
Bayesian Optimization for Iterative Learning NIPS 2020
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation NIPS 2018
Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning IJCAI 2018
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation ECCV 2018
Discriminative Bayesian Nonparametric Clustering IJCAI 2017
Regret for Expected Improvement over the Best-Observed Value and Stopping Condition ACML 2017
Approximation Vector Machines for Large-scale Online Learning JMLR 2017
Shadow Detection With Conditional Generative Adversarial Networks ICCV 2017
High Dimensional Bayesian Optimization with Elastic Gaussian Process ICML 2017
High Dimensional Bayesian Optimization using Dropout IJCAI 2017
A Bayesian Nonparametric Approach for Multi-label Classification ACML 2016
Dual Space Gradient Descent for Online Learning NIPS 2016
Nonparametric Budgeted Stochastic Gradient Descent AISTATS 2016
Multiple Kernel Learning with Data Augmentation ACML 2016