Hoang, Trong Nghia

30 publications

TMLR 2026 Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship Nang Hung Nguyen, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang, Masashi Sugiyama

ICCV 2025 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

NeurIPS 2025 Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data Manh Duong Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Phi Le Nguyen

NeurIPS 2025 ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge Manh Cuong Dao, The Hung Tran, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang

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

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

NeurIPS 2024 Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, 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

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

NeurIPS 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

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

UAI 2023 Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs Tengfei Ma, Trong Nghia Hoang, Jie Chen

UAI 2023 Personalized Federated Domain Adaptation for Item-to-Item Recommendation Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang

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

UAI 2022 Bayesian Federated Estimation of Causal Effects from Observational Data Thanh Vinh Vo, Young Lee, Trong Nghia Hoang, Tze-Yun Leong

ICLRW 2022 Robust Randomized Smoothing via Two Cost-Effective Approaches Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng

AAAI 2020 CASTER: Predicting Drug Interactions with Chemical Substructure Representation Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas Glass, Jimeng Sun

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

IJCAI 2019 DDL: Deep Dictionary Learning for Predictive Phenotyping Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun

IJCAI 2019 RDPD: Rich Data Helps Poor Data via Imitation Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun

AAAI 2018 Decentralized High-Dimensional Bayesian Optimization with Factor Graphs Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low

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

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

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

ICML 2015 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

ECML-PKDD 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

ICML 2014 Nonmyopic Ε-Bayes-Optimal Active Learning of Gaussian Processes Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan Kankanhalli

IJCAI 2013 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