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ORCIDMinh-Ngoc Tran
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2020 – today
- 2024
[j23]Minh-Ngoc Tran
, Van-Binh Duong, Young-Han Kim:
Design of Computing-Aware Traffic Steering Architecture for 5G Mobile User Plane. IEEE Access 12: 88370-88382 (2024)- [j22]Minh-Ngoc Tran, Young-Han Kim:
Optimized resource usage with hybrid auto-scaling system for knative serverless edge computing. Future Gener. Comput. Syst. 152: 304-316 (2024) - [j21]Minh-Ngoc Tran, Young-Han Kim:
Concurrent service auto-scaling for Knative resource quota-based serverless system. Future Gener. Comput. Syst. 160: 326-339 (2024) - [j20]Nhat Minh Nguyen, Minh-Ngoc Tran, Rohitash Chandra:
Sequential reversible jump MCMC for dynamic Bayesian neural networks. Neurocomputing 564: 126960 (2024) - 2023
- [c9]Anna Lopatnikova, Minh-Ngoc Tran:
Quantum Variational Bayes on Manifolds. ICASSP 2023: 1-5 - [i10]Chen Liu, Chao Wang, Minh-Ngoc Tran, Robert Kohn:
Realized recurrent conditional heteroskedasticity model for volatility modelling. CoRR abs/2302.08002 (2023) - [i9]Minh-Ngoc Tran, Paco Tseng, Robert Kohn:
Particle Mean Field Variational Bayes. CoRR abs/2303.13930 (2023) - [i8]Chen Liu, Minh-Ngoc Tran, Chao Wang, Richard Gerlach, Robert Kohn:
DeepVol: A Deep Transfer Learning Approach for Universal Asset Volatility Modeling. CoRR abs/2309.02072 (2023) - 2022
- [j19]Minh-Ngoc Tran, Xuan Tuong Vu, Young-Han Kim:
Proactive Stateful Fault-Tolerant System for Kubernetes Containerized Services. IEEE Access 10: 102181-102194 (2022) - [j18]Dinh Dai Vu, Minh-Ngoc Tran, Young-Han Kim:
Predictive Hybrid Autoscaling for Containerized Applications. IEEE Access 10: 109768-109778 (2022) - [j17]Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran:
Adaptive hierarchical hyper-gradient descent. Int. J. Mach. Learn. Cybern. 13(12): 3785-3805 (2022) - [c8]Ta Phuong Bac, Minh-Ngoc Tran, Young-Han Kim:
Serverless Computing Approach for Deploying Machine Learning Applications in Edge Layer. ICOIN 2022: 396-401 - [c7]Minh-Ngoc Tran, Dinh Dai Vu, Younghan Kim:
A Survey of Autoscaling in Kubernetes. ICUFN 2022: 263-265 - [c6]Minh-Ngoc Tran, Younghan Kim:
Network Performance Benchmarking for Containerized Infrastructure in NFV environment. NetSoft 2022: 115-120 - 2021
- [j16]Matias Quiroz, Minh-Ngoc Tran, Mattias Villani, Robert Kohn, Khue-Dung Dang:
The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC. J. Comput. Graph. Stat. 30(4): 877-888 (2021) - [j15]Bingxin Zhou, Junbin Gao, Minh-Ngoc Tran, Richard Gerlach:
Manifold Optimization-Assisted Gaussian Variational Approximation. J. Comput. Graph. Stat. 30(4): 946-957 (2021) - [j14]Xuejun Yu, David J. Nott, Minh-Ngoc Tran, Nadja Klein:
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. J. Comput. Graph. Stat. 30(4): 977-990 (2021) - [j13]Briytone Mutichiro, Minh-Ngoc Tran, Young-Han Kim:
QoS-Based Service-Time Scheduling in the IoT-Edge Cloud. Sensors 21(17): 5797 (2021) - [c5]Minh-Ngoc Tran, Younghan Kim:
A Cloud QoS-driven Scheduler based on Deep Reinforcement Learning. ICTC 2021: 1823-1825 - [i7]Anna Lopatnikova, Minh-Ngoc Tran:
An Introduction to Quantum Computing for Statisticians. CoRR abs/2112.06587 (2021) - 2020
- [j12]Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran:
HyperTube: A Framework for Population-Based Online Hyperparameter Optimization with Resource Constraints. IEEE Access 8: 69038-69057 (2020) - [j11]David Gunawan, Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran:
Subsampling sequential Monte Carlo for static Bayesian models. Stat. Comput. 30(6): 1741-1758 (2020) - [c4]Robert Salomone, Matias Quiroz, Robert Kohn, Mattias Villani, Minh-Ngoc Tran:
Spectral Subsampling MCMC for Stationary Time Series. ICML 2020: 8449-8458 - [c3]Ngoc-Thanh Dinh, Minh-Ngoc Tran, Youngki Park, Younghan Kim:
An Information-centric NFV-based System Implementation for Disaster Management Services. ICOIN 2020: 807-810 - [c2]Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran:
Regularized Flexible Activation Function Combination for Deep Neural Networks. ICPR 2020: 2001-2008 - [c1]Minh-Ngoc Tran, Younghan Kim:
NDN-based Emergency Communication over Edge Computing Infrastructure. ICTC 2020: 353-358 - [i6]Zhengkun Li, Minh-Ngoc Tran, Chao Wang, Richard Gerlach, Junbin Gao:
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting. CoRR abs/2001.08374 (2020) - [i5]Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran:
Regularized Flexible Activation Function Combinations for Deep Neural Networks. CoRR abs/2007.13101 (2020) - [i4]Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran:
Adaptive Multi-level Hyper-gradient Descent. CoRR abs/2008.07277 (2020)
2010 – 2019
- 2019
- [j10]Dao Thanh Tung, Minh-Ngoc Tran, Tran Manh Cuong:
Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models. Commun. Stat. Simul. Comput. 48(2): 530-543 (2019) - [j9]Khue-Dung Dang, Matias Quiroz, Robert Kohn, Minh-Ngoc Tran, Mattias Villani:
Hamiltonian Monte Carlo with Energy Conserving Subsampling. J. Mach. Learn. Res. 20: 100:1-100:31 (2019) - [j8]David Gunawan, Minh-Ngoc Tran, Kosuke Suzuki, Josef Dick, Robert Kohn:
Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins. Stat. Comput. 29(5): 933-946 (2019) - [i3]Bingxin Zhou, Junbin Gao, Minh-Ngoc Tran, Richard Gerlach:
Manifold Optimisation Assisted Gaussian Variational Approximation. CoRR abs/1902.03718 (2019) - [i2]Minh-Ngoc Tran, Dang Hai Nguyen, Duy Nguyen:
Variational Bayes on Manifolds. CoRR abs/1908.03097 (2019) - 2018
- [j7]Victor M. H. Ong, David J. Nott, Minh-Ngoc Tran, Scott A. Sisson, Christopher C. Drovandi:
Likelihood-free inference in high dimensions with synthetic likelihood. Comput. Stat. Data Anal. 128: 271-291 (2018) - [j6]Victor M. H. Ong, David J. Nott, Minh-Ngoc Tran, Scott A. Sisson, Christopher C. Drovandi:
Variational Bayes with synthetic likelihood. Stat. Comput. 28(4): 971-988 (2018) - 2016
- [j5]Minh-Ngoc Tran, Michael K. Pitt, Robert Kohn:
Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals. Stat. Comput. 26(1-2): 361-381 (2016) - 2012
- [j4]David J. Nott, Minh-Ngoc Tran, Chenlei Leng:
Variational approximation for heteroscedastic linear models and matching pursuit algorithms. Stat. Comput. 22(2): 497-512 (2012) - [j3]Minh-Ngoc Tran, David J. Nott, Chenlei Leng:
The predictive Lasso. Stat. Comput. 22(5): 1069-1084 (2012) - [j2]David J. Nott, Lucy A. Marshall, Minh-Ngoc Tran:
The ensemble Kalman filter is an ABC algorithm. Stat. Comput. 22(6): 1273-1276 (2012) - 2010
- [j1]Marcus Hutter, Minh-Ngoc Tran:
Model selection with the Loss Rank Principle. Comput. Stat. Data Anal. 54(5): 1288-1306 (2010) - [i1]Marcus Hutter, Minh-Ngoc Tran:
Model Selection with the Loss Rank Principle. CoRR abs/1003.0516 (2010)
Coauthor Index
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