Vu Nguyen

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

Achievements

+13 more ↓

🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🗺️ Taxonomy Completionist (15) 🌍 Conference Polyglot (15)

🗺️ Taxonomy Completionist (15) 🌈 Renaissance Researcher (6) 🧭 Keyword Pioneer 🌱 Topic Pioneer 🏆 Grand Slam 🔬 Deep Specialist (13) 🏆 Keyword Champion (3) 🗃️ Keyword Collector (134) 🚀 Conference Pioneer 📈 Trend Setter 💎 Century Club (34) 🔥 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) bayesian nonparametrics (3) hyperparameter tuning (3) bayesian optimisation (3) black-box optimization (3) kernel online learning (3) regret bound (3) multi-armed bandit (2) variational autoencoder (2) image classification (2) high-dimensional optimization (2) data augmentation (2) active learning (2) multi-label classification (2) variational inference (2) stochastic gradient descent (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