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Trong Nghia Hoang
Trong Nghia Hoang
Assistant Professor, Washington State University
Geverifieerd e-mailadres voor wsu.edu - Homepage
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Bayesian nonparametric federated learning of neural networks
YK Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald ...
International Conference on Machine Learning, 7252-7261, 2019
8992019
Caster: Predicting drug interactions with chemical substructure representation
K Huang, C Xiao, T Hoang, L Glass, J Sun
Proceedings of the AAAI conference on artificial intelligence 34 (01), 702-709, 2020
1532020
A unifying framework of anytime sparse Gaussian process regression models with stochastic variational inference for big data
TN Hoang, QM Hoang, BKH Low
International Conference on Machine Learning, 569-578, 2015
1092015
On the design of black-box adversarial examples by leveraging gradient-free optimization and operator splitting method
P Zhao, S Liu, PY Chen, N Hoang, K Xu, B Kailkhura, X Lin
Proceedings of the IEEE/CVF International Conference on Computer Vision, 121-130, 2019
812019
Information-based multi-fidelity Bayesian optimization
Y Zhang, TN Hoang, BKH Low, M Kankanhalli
NIPS workshop on Bayesian optimization 49, 2017
812017
Nonmyopic ε-Bayes-optimal active learning of Gaussian processes
TN Hoang, BKH Low, P Jaillet, M Kankanhalli
International conference on machine learning, 739-747, 2014
732014
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs
TN Hoang, QM Hoang, R Ouyang, KH Low
32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2018
682018
A distributed variational inference framework for unifying parallel sparse Gaussian process regression models
TN Hoang, QM Hoang, BKH Low
International Conference on Machine Learning, 382-391, 2016
622016
Verifiably safe exploration for end-to-end reinforcement learning
N Hunt, N Fulton, S Magliacane, TN Hoang, S Das, A Solar-Lezama
Proceedings of the 24th International Conference on Hybrid Systems …, 2021
602021
Interactive POMDP Lite: Towards practical planning to predict and exploit intentions for interacting with self-interested agents
TN Hoang, KH Low
arXiv preprint arXiv:1304.5159, 2013
602013
Collective online learning of Gaussian processes in massive multi-agent systems
TN Hoang, QM Hoang, KH Low, J How
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 7850-7857, 2019
492019
Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance.
P Natarajan, TN Hoang, KH Low, MS Kankanhalli
AAMAS, 155-162, 2012
452012
Near-optimal active learning of multi-output Gaussian processes
Y Zhang, TN Hoang, KH Low, M Kankanhalli
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
432016
A generalized stochastic variational Bayesian hyperparameter learning framework for sparse spectrum Gaussian process regression
QM Hoang, TN Hoang, KH Low
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
412017
Collective Model Fusion for Multiple Black-Box Experts
CK Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low
International Conference on Machine Learning, 2742-2750, 2019
392019
Statistical model aggregation via parameter matching
M Yurochkin, M Agarwal, S Ghosh, K Greenewald, N Hoang
Advances in neural information processing systems 32, 2019
392019
Ddl: Deep dictionary learning for predictive phenotyping
T Fu, TN Hoang, C Xiao, J Sun
IJCAI: proceedings of the conference 2019, 5857, 2019
352019
Stochastic variational inference for Bayesian sparse Gaussian process regression
H Yu, T Nghia, BKH Low, P Jaillet
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
322019
Recent advances in scaling up Gaussian process predictive models for large spatiotemporal data
KH Low, J Chen, TN Hoang, N Xu, P Jaillet
Dynamic Data-Driven Environmental Systems Science: First International …, 2015
232015
Model fusion for personalized learning
TC Lam, N Hoang, BKH Low, P Jaillet
International Conference on Machine Learning, 5948-5958, 2021
222021
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Artikelen 1–20