A Multi-agent Mechanism in Machine Learning Approach to Anti-virus System. - Minh Nhat Quang Truong; Trong Nghia Hoang (KES-AMSTA, 2008)

Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents. - Trong Nghia Hoang; Kian Hsiang Low (AAMAS, 2012)

Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance. - Prabhu Natarajan; Trong Nghia Hoang; Kian Hsiang Low; Mohan S. Kankanhalli (AAMAS, 2012)

Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments. - Prabhu Natarajan; Trong Nghia Hoang; Kian Hsiang Low; Mohan S. Kankanhalli (ICDSC, 2012)

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. - Trong Nghia Hoang; Kian Hsiang Low (IJCAI, 2013)

Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. - Trong Nghia Hoang; Kian Hsiang Low (IJCAI, 2013)

Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data. - Kian Hsiang Low; Jie Chen 027; Trong Nghia Hoang; Nuo Xu; Patrick Jaillet (DyDESS, 2014)

Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance. - Prabhu Natarajan; Trong Nghia Hoang; Yongkang Wong; Kian Hsiang Low; Mohan S. Kankanhalli (ICDSC, 2014)

Nonmyopic ( epsilon )-Bayes-Optimal Active Learning of Gaussian Processes. - Trong Nghia Hoang; Bryan Kian Hsiang Low; Patrick Jaillet; Mohan S. Kankanhalli (ICML, 2014)

Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes. - Trong Nghia Hoang; Kian Hsiang Low; Patrick Jaillet; Mohan S. Kankanhalli (ECML/PKDD (3), 2014)

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data. - Trong Nghia Hoang; Quang Minh Hoang; Bryan Kian Hsiang Low (ICML, 2015)

Near-Optimal Active Learning of Multi-Output Gaussian Processes. - Yehong Zhang; Trong Nghia Hoang; Kian Hsiang Low; Mohan S. Kankanhalli (AAAI, 2016)

A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. - Trong Nghia Hoang; Quang Minh Hoang; Bryan Kian Hsiang Low (ICML, 2016)

A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. - Quang Minh Hoang; Trong Nghia Hoang; Kian Hsiang Low (AAAI, 2017)

Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models. - Haibin Yu; Trong Nghia Hoang; Kian Hsiang Low; Patrick Jaillet (CoRR, 2017)

Decentralized High-Dimensional Bayesian Optimization With Factor Graphs. - Trong Nghia Hoang; Quang Minh Hoang; Ruofei Ouyang; Kian Hsiang Low (AAAI, 2018)

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. - Trong Nghia Hoang; Yuchen Xiao; Kavinayan Sivakumar; Christopher Amato; Jonathan P. How (ICRA, 2018)

Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems. - Trong Nghia Hoang; Quang Minh Hoang; Kian Hsiang Low; Jonathan P. How (CoRR, 2018)

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems. - Trong Nghia Hoang; Quang Minh Hoang; Kian Hsiang Low; Jonathan P. How (AAAI, 2019)

Collective Model Fusion for Multiple Black-Box Experts. - Quang Minh Hoang; Trong Nghia Hoang; Bryan Kian Hsiang Low; Carl Kingsford (ICML, 2019)

Bayesian Nonparametric Federated Learning of Neural Networks. - Mikhail Yurochkin; Mayank Agarwal; Soumya Ghosh; Kristjan H. Greenewald; Trong Nghia Hoang; Yasaman Khazaeni (ICML, 2019)

DDL: Deep Dictionary Learning for Predictive Phenotyping. - Tianfan Fu; Trong Nghia Hoang; Cao Xiao; Jimeng Sun 001 (IJCAI, 2019)

RDPD: Rich Data Helps Poor Data via Imitation. - Shenda Hong; Cao Xiao; Trong Nghia Hoang; Tengfei Ma 001; Hongyan Li 002; Jimeng Sun 001 (IJCAI, 2019)

Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. - Haibin Yu; Trong Nghia Hoang; Bryan Kian Hsiang Low; Patrick Jaillet (IJCNN, 2019)

Statistical Model Aggregation via Parameter Matching. - Mikhail Yurochkin; Mayank Agarwal; Soumya Ghosh; Kristjan H. Greenewald; Trong Nghia Hoang (NeurIPS, 2019)

CASTER: Predicting Drug Interactions with Chemical Substructure Representation. - Kexin Huang; Cao Xiao; Trong Nghia Hoang; Lucas Glass; Jimeng Sun 001 (AAAI, 2020)

Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. - Trong Nghia Hoang; Thanh Lam; Bryan Kian Hsiang Low; Patrick Jaillet (ICML, 2020)

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. - Quang Minh Hoang; Trong Nghia Hoang; Hai Pham; David P. Woodruff (CoRR, 2020)

Verifiably safe exploration for end-to-end reinforcement learning. - Nathan Hunt; Nathan Fulton; Sara Magliacane; Trong Nghia Hoang; Subhro Das; Armando Solar-Lezama (HSCC, 2021)

Model Fusion for Personalized Learning. - Thanh Chi Lam; Trong Nghia Hoang; Bryan Kian Hsiang Low; Patrick Jaillet (ICML, 2021)

AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings. - Trong Nghia Hoang; Shenda Hong; Cao Xiao; Bryan Low; Jimeng Sun 001 (WWW, 2021)

Adaptive Multi-Source Causal Inference. - Thanh Vinh Vo; Pengfei Wei; Trong Nghia Hoang; Tze-Yun Leong (CoRR, 2021)

Federated Estimation of Causal Effects from Observational Data. - Thanh Vinh Vo; Trong Nghia Hoang; Young Lee; Tze-Yun Leong (CoRR, 2021)

Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. - Trong Nghia Hoang; Anoop Deoras; Tong Zhao 002; Jin Li 003; George Karypis (AISTATS, 2022)

Adaptive Multi-Source Causal Inference from Observational Data. - Thanh Vinh Vo; Pengfei Wei 001; Trong Nghia Hoang; Tze-Yun Leong (CIKM, 2022)

The First Impression Counts! The Importance of Onboarding for Educational Chatbots. - Trong Nghia Hoang; Andreas Reich; Matthias Wölfel (DELFI, 2022)

Bayesian federated estimation of causal effects from observational data. - Thanh Vinh Vo; Young Lee; Trong Nghia Hoang; Tze-Yun Leong (UAI, 2022)

Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. - Linbo Liu; Youngsuk Park; Trong Nghia Hoang; Hilaf Hasson; Jun Huan (CoRR, 2022)

CHEER: Rich Model Helps Poor Model via Knowledge Infusion. - Cao Xiao; Trong Nghia Hoang; Shenda Hong; Tengfei Ma 001; Jimeng Sun 001 (IEEE Trans. Knowl. Data Eng., 2022)

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. - Linbo Liu; Trong Nghia Hoang; Lam M. Nguyen; Tsui-Wei Weng (ICDM, 2023)

Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. - Linbo Liu; Youngsuk Park; Trong Nghia Hoang; Hilaf Hasson; Luke Huan (ICLR, 2023)

Personalized federated domain adaptation for item-to-item recommendation. - Ziwei Fan 001; Hao Ding 003; Anoop Deoras; Trong Nghia Hoang (UAI, 2023)

Federated learning of models pre-trained on different features with consensus graphs. - Tengfei Ma 001; Trong Nghia Hoang; Jie Chen 007 (UAI, 2023)

Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization. - Alireza Ghods 002; Trong Nghia Hoang; Diane J. Cook (CoRR, 2023)

Offline Model-Based Optimization via Policy-Guided Gradient Search. - Yassine Chemingui; Aryan Deshwal; Trong Nghia Hoang; Janardhan Rao Doppa (AAAI, 2024)

Collaborative Learning across Heterogeneous Systems with Pre-Trained Models. - Trong Nghia Hoang (AAAI, 2024)

Few-Shot Learning via Repurposing Ensemble of Black-Box Models. - Minh Hoang; Trong Nghia Hoang (AAAI, 2024)

Boosting Offline Optimizers with Surrogate Sensitivity. - Manh Cuong Dao; Phi Le Nguyen; Truong Thao Nguyen; Trong Nghia Hoang (ICML, 2024)

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching. - Minh Hoang; Azza Fadhel; Aryan Deshwal; Jana Doppa; Trong Nghia Hoang (ICML, 2024)

FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization. - Manh Duong Nguyen; Trung Thanh Nguyen; Huy Hieu Pham 001; Trong Nghia Hoang; Phi Le Nguyen; Thanh Trung Huynh (NCA, 2024)

FedCert: Federated Accuracy Certification. - Minh Hieu Nguyen; Huu Tien Nguyen; Trung Thanh Nguyen; Manh Duong Nguyen; Trong Nghia Hoang; Truong Thao Nguyen; Phi Le Nguyen (NCA, 2024)

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. - Pei-Yau Weng; Minh Hoang; Lam M. Nguyen; My T. Thai; Lily Weng; Trong Nghia Hoang (NeurIPS, 2024)

Revisiting Kernel Attention with Correlated Gaussian Process Representation. - Long Minh Bui; Tho Tran Huu; Duy Dinh; Tan Minh Nguyen; Trong Nghia Hoang (UAI, 2024)

Pre-trained Recommender Systems: A Causal Debiasing Perspective. - Ziqian Lin; Hao Ding 003; Trong Nghia Hoang; Branislav Kveton; Anoop Deoras; Hao Wang 014 (WSDM, 2024)

Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems. - Trong Nghia Hoang (AI Mag., 2024)

Incentives in Private Collaborative Machine Learning. - Rachael Hwee Ling Sim; Yehong Zhang; Trong Nghia Hoang; Xinyi Xu; Bryan Kian Hsiang Low; Patrick Jaillet (CoRR, 2024)

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. - Pei-Yau Weng; Minh Hoang; Lam M. Nguyen; My T. Thai; Tsui-Wei Weng; Trong Nghia Hoang (CoRR, 2025)

Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques. - Manh Cuong Dao; Phi Le Nguyen; Truong Thao Nguyen; Trong Nghia Hoang (CoRR, 2025)

SAFA: Handling Sparse and Scarce Data in Federated Learning With Accumulative Learning. - Nang Hung Nguyen; Truong Thao Nguyen; Trong Nghia Hoang; Hieu H. Pham 001; Thanh Hung Nguyen; Phi Le Nguyen (IEEE Trans. Computers, 2025)