Trong Nghia Hoang

Publication Activity (10 Years)

Years Active: 2008-2023
Publications (10 Years): 46

Top Topics

Observational Data

Gaussian Process

Item Recommendation

Top Venues

Publications

Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang
Personalized Federated Domain Adaptation for Item-to-Item Recommendation. CoRR (2023)
Tengfei Ma, Trong Nghia Hoang, Jie Chen
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs. CoRR (2023)
Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang
Personalized federated domain adaptation for item-to-item recommendation. UAI (2023)
Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. ICDM (2023)
Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. ICLR (2023)
Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. CoRR (2023)
Alireza Ghods, Trong Nghia Hoang, Diane J. Cook
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization. CoRR (2023)
Tengfei Ma, Trong Nghia Hoang, Jie Chen
Federated learning of models pre-trained on different features with consensus graphs. UAI (2023)
Thanh Vinh Vo, Young Lee, Trong Nghia Hoang, Tze-Yun Leong
Bayesian federated estimation of causal effects from observational data. UAI (2022)
Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan
Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. CoRR (2022)
Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. AISTATS (2022)
Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong
Adaptive Multi-Source Causal Inference from Observational Data. CIKM (2022)
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) (2022)
Trong Nghia Hoang, Andreas Reich, Matthias Wölfel
The First Impression Counts! The Importance of Onboarding for Educational Chatbots. DELFI (2022)
Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. CoRR (2022)
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)
Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
Model Fusion for Personalized Learning. ICML (2021)
Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong
Adaptive Multi-Source Causal Inference. CoRR (2021)
Thanh Vinh Vo, Trong Nghia Hoang, Young Lee, Tze-Yun Leong
Federated Estimation of Causal Effects from Observational Data. CoRR (2021)
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)
Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun
CHEER: Rich Model Helps Poor Model via Knowledge Infusion. CoRR (2020)
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)
Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas Glass, Jimeng Sun
CASTER: Predicting Drug Interactions with Chemical Substructure Representation. AAAI (2020)
Quang Minh Hoang, Trong Nghia Hoang, Hai Pham, David P. Woodruff
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. CoRR (2020)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
Bayesian Nonparametric Federated Learning of Neural Networks. ICML (2019)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang
Statistical Model Aggregation via Parameter Matching. CoRR (2019)
Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas M. Glass, Jimeng Sun
CASTER: Predicting Drug Interactions with Chemical Substructure Representation. CoRR (2019)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang
Statistical Model Aggregation via Parameter Matching. NeurIPS (2019)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
Bayesian Nonparametric Federated Learning of Neural Networks. CoRR (2019)
Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun
RDPD: Rich Data Helps Poor Data via Imitation. IJCAI (2019)
Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. IJCNN (2019)
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)
Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun
DDL: Deep Dictionary Learning for Predictive Phenotyping. IJCAI (2019)
Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford
Collective Model Fusion for Multiple Black-Box Experts. ICML (2019)
Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low
Decentralized High-Dimensional Bayesian Optimization With Factor Graphs. AAAI (2018)
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)
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 (2018)
Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs. CoRR (2017)
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)
Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet
Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models. CoRR (2017)
Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. CoRR (2017)
Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. CoRR (2016)
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)
Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
Near-Optimal Active Learning of Multi-Output Gaussian Processes. AAAI (2016)
Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
Near-Optimal Active Learning of Multi-Output Gaussian Processes. CoRR (2015)
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)
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)
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)
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)
Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli
Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. ICML (2014)
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)
Trong Nghia Hoang, Kian Hsiang Low
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents CoRR (2013)
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)
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 (2013)
Trong Nghia Hoang, Kian Hsiang Low
Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents. AAMAS (2012)
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 (2012)
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)
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)
Minh Nhat Quang Truong, Trong Nghia Hoang
A Multi-agent Mechanism in Machine Learning Approach to Anti-virus System. KES-AMSTA (2008)