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 | 107 | 2015 |
Decentralized high-dimensional Bayesian optimization with factor graphs TN Hoang, QM Hoang, R Ouyang, KH Low Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 65 | 2018 |
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 | 60 | 2016 |
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 | 50 | 2019 |
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 | 40 | 2017 |
Collective model fusion for multiple black-box experts M Hoang, N Hoang, BKH Low, C Kingsford International Conference on Machine Learning, 2742-2750, 2019 | 39 | 2019 |
Differentiable learning of sequence-specific minimizer schemes with DeepMinimizer M Hoang, H Zheng, C Kingsford Journal of Computational Biology 29 (12), 1288-1304, 2022 | 9 | 2022 |
Tuning of viscosity and density of non-Newtonian fluids through mixing process using multimodal sensors, sensor fusion and models M Hoang Høgskolen i Sørøst-Norge, 2016 | 8 | 2016 |
DeepMinimizer: A differentiable framework for optimizing sequence-specific minimizer schemes M Hoang, H Zheng, C Kingsford International Conference on Research in Computational Molecular Biology, 52-69, 2022 | 7 | 2022 |
Revisiting the sample complexity of sparse spectrum approximation of gaussian processes M Hoang, N Hoang, H Pham, D Woodruff Advances in Neural Information Processing Systems 33, 12710-12720, 2020 | 6 | 2020 |
Learning Surrogates for Offline Black-Box Optimization via Gradient Matching M Hoang, A Fadhel, A Deshwal, J Doppa, TN Hoang Forty-first International Conference on Machine Learning, 2024 | 5 | 2024 |
Remarques sur la structure phonologique du Vietnamien. Essais Linguistiques (Remarks on the phonological structure of Vietnamese) T Hoàng, M Hoàng Etudes Vietnamiennes (Vietnamese studies) 40, 1975 | 5 | 1975 |
Personalized neural architecture search for federated learning M Hoang, C Kingsford 1st NeurIPS workshop on new frontiers in federated learning (NFFL 2021), 2021 | 4 | 2021 |
Masked Minimizers: Unifying sequence sketching methods M Hoang, G Marçais, C Kingsford bioRxiv, 2022.10. 18.512430, 2022 | 3 | 2022 |
Harvestman: a framework for hierarchical feature learning and selection from whole genome sequencing data TS Frisby, SJ Baker, G Marçais, QM Hoang, C Kingsford, CJ Langmead BMC bioinformatics 22, 1-19, 2021 | 3 | 2021 |
Optimizing dynamic structures with bayesian generative search M Hoang, C Kingsford International Conference on Machine Learning, 4271-4281, 2020 | 3 | 2020 |
Few-Shot Learning via Repurposing Ensemble of Black-Box Models M Hoang, TN Hoang Proceedings of the AAAI Conference on Artificial Intelligence 38 (11), 12448 …, 2024 | 2 | 2024 |
Density and Conservation Optimization of the Generalized Masked-Minimizer Sketching Scheme M Hoang, G Marçais, C Kingsford Journal of Computational Biology 31 (1), 2-20, 2024 | 2 | 2024 |
Probabilistic Federated Prompt-Tuning in Data Imbalance Settings P Weng, M Hoang, L Nguyen, M Thai, L Weng, TN Hoang Annual Conference on Neural Information Processing Systems, 2024 | | 2024 |
Approximate and Exact Optimization Algorithms for the Beltway and Turnpike Problems with Duplicated, Missing, Partially Labeled, and Uncertain Measurements CS Elder, M Hoang, M Ferdosi, C Kingsford Journal of Computational Biology 31 (10), 908-926, 2024 | | 2024 |