| 2026 | AAAI | Nanoporous Materials Discovery via Search Bias-Guided Surrogate Modeling. | Azza Fadhel, Yassine Chemingui, Minh Hoang, Aryan Deshwal, Trong Nghia Hoang, Jana Doppa |
| 2026 | AAAI | ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model. | Krishu K. Thapa, Supriya Savalkar, Bhupinderjeet Singh, Trong Nghia Hoang, Kirti Rajagopalan, Ananth Kalyanaraman |
| 2025 | ICCV | Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data. | Thu Hang Phung, Duong M. Nguyen, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Trong Nghia Hoang, Phi Le Nguyen |
| 2024 | AAAI | Offline Model-Based Optimization via Policy-Guided Gradient Search. | Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang, Janardhan Rao Doppa |
| 2024 | AAAI | Collaborative Learning across Heterogeneous Systems with Pre-Trained Models. | Trong Nghia Hoang |
| 2024 | AAAI | Few-Shot Learning via Repurposing Ensemble of Black-Box Models. | Minh Hoang, Trong Nghia Hoang |
| 2024 | ICML | Boosting Offline Optimizers with Surrogate Sensitivity. | Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang |
| 2024 | ICML | Learning Surrogates for Offline Black-Box Optimization via Gradient Matching. | Minh Hoang, Azza Fadhel, Aryan Deshwal, Jana Doppa, Trong Nghia Hoang |
| 2024 | NCA | FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization. | Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia Hoang, Phi Le Nguyen, Thanh Trung Huynh |
| 2024 | NCA | 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 |
| 2024 | UAI | Revisiting Kernel Attention with Correlated Gaussian Process Representation. | Long Minh Bui, Tho Tran Huu, Duy Dinh, Tan Minh Nguyen, Trong Nghia Hoang |
| 2024 | WSDM | Pre-trained Recommender Systems: A Causal Debiasing Perspective. | Ziqian Lin, Hao Ding, Trong Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang |
| 2023 | ICDM | Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. | Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng |
| 2023 | ICLR | Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. | Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan |
| 2023 | UAI | Personalized federated domain adaptation for item-to-item recommendation. | Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang |
| 2023 | UAI | Federated learning of models pre-trained on different features with consensus graphs. | Tengfei Ma, Trong Nghia Hoang, Jie Chen |
| 2022 | AISTATS | Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. | Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis |
| 2022 | CIKM | Adaptive Multi-Source Causal Inference from Observational Data. | Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong |
| 2022 | UAI | Bayesian federated estimation of causal effects from observational data. | Thanh Vinh Vo, Young Lee, Trong Nghia Hoang, Tze-Yun Leong |
| 2021 | ICML | Model Fusion for Personalized Learning. | Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | WWW | 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 |
| 2020 | AAAI | CASTER: Predicting Drug Interactions with Chemical Substructure Representation. | Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas Glass, Jimeng Sun |
| 2020 | ICML | 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 |
| 2019 | AAAI | Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems. | Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How |
| 2019 | ICML | Collective Model Fusion for Multiple Black-Box Experts. | Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford |
| 2019 | ICML | Bayesian Nonparametric Federated Learning of Neural Networks. | Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni |
| 2019 | IJCAI | DDL: Deep Dictionary Learning for Predictive Phenotyping. | Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun |
| 2019 | IJCAI | RDPD: Rich Data Helps Poor Data via Imitation. | Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun |
| 2019 | IJCNN | Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. | Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2018 | AAAI | Decentralized High-Dimensional Bayesian Optimization With Factor Graphs. | Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low |
| 2018 | ICRA | Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. | Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How |
| 2017 | AAAI | A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. | Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low |
| 2016 | AAAI | Near-Optimal Active Learning of Multi-Output Gaussian Processes. | Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli |
| 2016 | ICML | A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. | Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low |
| 2015 | ICML | 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 |
| 2014 | ICML | Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. | Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli |
| 2013 | IJCAI | A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. | Trong Nghia Hoang, Kian Hsiang Low |
| 2013 | IJCAI | Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. | Trong Nghia Hoang, Kian Hsiang Low |
| 2012 | AAMAS | Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents. | Trong Nghia Hoang, Kian Hsiang Low |
| 2012 | AAMAS | Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance. | Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli |