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ORCIDTrong Nghia Hoang
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2020 – today
- 2024
[j2]Trong Nghia Hoang
:
Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems. AI Mag. 45(3): 404-410 (2024)- [c42]Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang, Janardhan Rao Doppa:
Offline Model-Based Optimization via Policy-Guided Gradient Search. AAAI 2024: 11230-11239 - [c41]Minh Hoang, Trong Nghia Hoang:
Few-Shot Learning via Repurposing Ensemble of Black-Box Models. AAAI 2024: 12448-12455 - [c40]Trong Nghia Hoang:
Collaborative Learning across Heterogeneous Systems with Pre-Trained Models. AAAI 2024: 22668 - [c39]Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang:
Boosting Offline Optimizers with Surrogate Sensitivity. ICML 2024 - [c38]Minh Hoang, Azza Fadhel, Aryan Deshwal, Jana Doppa, Trong Nghia Hoang:
Learning Surrogates for Offline Black-Box Optimization via Gradient Matching. ICML 2024 - [c37]Ziqian Lin, Hao Ding, Trong Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang:
Pre-trained Recommender Systems: A Causal Debiasing Perspective. WSDM 2024: 424-433 - [i26]Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet:
Incentives in Private Collaborative Machine Learning. CoRR abs/2404.01676 (2024) - [i25]Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang, Janardhan Rao Doppa:
Offline Model-Based Optimization via Policy-Guided Gradient Search. CoRR abs/2405.05349 (2024) - [i24]Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong Nguyen, Trong Nghia Hoang, Truong Thao Nguyen, Phi Le Nguyen:
FedCert: Federated Accuracy Certification. CoRR abs/2410.03067 (2024) - [i23]Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia Hoang, Phi Le Nguyen, Thanh Trung Huynh:
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization. CoRR abs/2410.03070 (2024) - 2023
- [c36]Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng:
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. ICDM 2023: 1145-1150 - [c35]Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan:
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. ICLR 2023 - [c34]Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang:
Personalized federated domain adaptation for item-to-item recommendation. UAI 2023: 560-570 - [c33]Tengfei Ma, Trong Nghia Hoang, Jie Chen:
Federated learning of models pre-trained on different features with consensus graphs. UAI 2023: 1336-1346 - [i22]Tengfei Ma, Trong Nghia Hoang, Jie Chen:
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs. CoRR abs/2306.01240 (2023) - [i21]Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang:
Personalized Federated Domain Adaptation for Item-to-Item Recommendation. CoRR abs/2306.03191 (2023) - [i20]Alireza Ghods, Trong Nghia Hoang, Diane J. Cook:
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization. CoRR abs/2308.13722 (2023) - [i19]Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng:
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. CoRR abs/2310.07780 (2023) - 2022
- [j1]Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun:
CHEER: Rich Model Helps Poor Model via Knowledge Infusion. IEEE Trans. Knowl. Data Eng. 34(2): 531-543 (2022) - [c32]Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis:
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. AISTATS 2022: 1062-1077 - [c31]Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong:
Adaptive Multi-Source Causal Inference from Observational Data. CIKM 2022: 1975-1985 - [c30]Trong Nghia Hoang, Andreas Reich, Matthias Wölfel:
The First Impression Counts! The Importance of Onboarding for Educational Chatbots. DELFI 2022: 239-240 - [c29]Thanh Vinh Vo, Young Lee, Trong Nghia Hoang, Tze-Yun Leong:
Bayesian federated estimation of causal effects from observational data. UAI 2022: 2024-2034 - [i18]Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis:
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. CoRR abs/2203.12598 (2022) - [i17]Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan:
Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. CoRR abs/2207.09572 (2022) - 2021
- [c28]Nathan Hunt, Nathan Fulton, Sara Magliacane, Trong Nghia Hoang, Subhro Das, Armando Solar-Lezama:
Verifiably safe exploration for end-to-end reinforcement learning. HSCC 2021: 14:1-14:11 - [c27]Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet:
Model Fusion for Personalized Learning. ICML 2021: 5948-5958 - [c26]Trong Nghia Hoang, Shenda Hong, Cao Xiao, Bryan Low, Jimeng Sun:
AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings. WWW 2021: 3569-3581 - [i16]Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong:
Adaptive Multi-Source Causal Inference. CoRR abs/2105.14877 (2021) - [i15]Thanh Vinh Vo, Trong Nghia Hoang, Young Lee, Tze-Yun Leong:
Federated Estimation of Causal Effects from Observational Data. CoRR abs/2106.00456 (2021) - 2020
- [c25]Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas Glass, Jimeng Sun:
CASTER: Predicting Drug Interactions with Chemical Substructure Representation. AAAI 2020: 702-709 - [c24]Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet:
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. ICML 2020: 4282-4292 - [i14]Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun:
CHEER: Rich Model Helps Poor Model via Knowledge Infusion. CoRR abs/2005.10918 (2020) - [i13]Quang Minh Hoang, Trong Nghia Hoang, Hai Pham, David P. Woodruff:
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. CoRR abs/2011.08432 (2020)
2010 – 2019
- 2019
- [c23]Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How:
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems. AAAI 2019: 7850-7857 - [c22]Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford:
Collective Model Fusion for Multiple Black-Box Experts. ICML 2019: 2742-2750 - [c21]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni:
Bayesian Nonparametric Federated Learning of Neural Networks. ICML 2019: 7252-7261 - [c20]Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun:
DDL: Deep Dictionary Learning for Predictive Phenotyping. IJCAI 2019: 5857-5863 - [c19]Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun:
RDPD: Rich Data Helps Poor Data via Imitation. IJCAI 2019: 5895-5901 - [c18]Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet:
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. IJCNN 2019: 1-8 - [c17]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang:
Statistical Model Aggregation via Parameter Matching. NeurIPS 2019: 10954-10964 - [i12]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni:
Bayesian Nonparametric Federated Learning of Neural Networks. CoRR abs/1905.12022 (2019) - [i11]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang:
Statistical Model Aggregation via Parameter Matching. CoRR abs/1911.00218 (2019) - [i10]Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas M. Glass, Jimeng Sun:
CASTER: Predicting Drug Interactions with Chemical Substructure Representation. CoRR abs/1911.06446 (2019) - 2018
- [c16]Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low:
Decentralized High-Dimensional Bayesian Optimization With Factor Graphs. AAAI 2018: 3231-3238 - [c15]Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How:
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. ICRA 2018: 6373-6380 - [i9]Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How:
Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems. CoRR abs/1805.09266 (2018) - 2017
- [c14]Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low:
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. AAAI 2017: 2007-2014 - [i8]Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How:
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. CoRR abs/1710.06525 (2017) - [i7]Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet:
Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models. CoRR abs/1711.00221 (2017) - [i6]Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low:
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs. CoRR abs/1711.07033 (2017) - 2016
- [c13]Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Near-Optimal Active Learning of Multi-Output Gaussian Processes. AAAI 2016: 2351-2357 - [c12]Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low:
A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. ICML 2016: 382-391 - [i5]Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low:
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. CoRR abs/1611.06080 (2016) - 2015
- [c11]Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low:
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data. ICML 2015: 569-578 - [i4]Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Near-Optimal Active Learning of Multi-Output Gaussian Processes. CoRR abs/1511.06891 (2015) - 2014
- [c10]Kian Hsiang Low, Jie Chen, Trong Nghia Hoang, Nuo Xu, Patrick Jaillet:
Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data. DyDESS 2014: 167-181 - [c9]Prabhu Natarajan, Trong Nghia Hoang, Yongkang Wong, Kian Hsiang Low, Mohan S. Kankanhalli:
Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance. ICDSC 2014: 38:1-38:6 - [c8]Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli:
Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. ICML 2014: 739-747 - [c7]Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli:
Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes. ECML/PKDD (3) 2014: 494-498 - 2013
- [c6]Trong Nghia Hoang, Kian Hsiang Low:
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. IJCAI 2013: 1394-1400 - [c5]Trong Nghia Hoang, Kian Hsiang Low:
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. IJCAI 2013: 2298-2305 - [i3]Trong Nghia Hoang, Kian Hsiang Low:
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. CoRR abs/1304.2024 (2013) - [i2]Trong Nghia Hoang, Kian Hsiang Low:
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. CoRR abs/1304.5159 (2013) - 2012
- [c4]Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance. AAMAS 2012: 155-162 - [c3]Trong Nghia Hoang, Kian Hsiang Low:
Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents. AAMAS 2012: 1233-1234 - [c2]Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments. ICDSC 2012: 1-6 - [i1]Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli:
Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments. CoRR abs/1209.4275 (2012)
2000 – 2009
- 2008
- [c1]Minh Nhat Quang Truong, Trong Nghia Hoang:
A Multi-agent Mechanism in Machine Learning Approach to Anti-virus System. KES-AMSTA 2008: 743-752
Coauthor Index
aka: Bryan Kian Hsiang Low
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