Publications

Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data

Published in 39th Annual Conference on Neural Information Processing Systems, 2025

Manh Duong Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, and Phi Le Nguyen

ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge

Published in 39th Annual Conference on Neural Information Processing Systems, 2025

Manh Cuong Dao, The Hung Tran, Phi Le Nguyen, Thao Nguyen Truong, and Trong Nghia Hoang

Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data

Published in International Conference on Computer Vision, 2025

Thu Hang Phung, Manh Duong Nguyen, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Trong Nghia Hoang, and Phi Le Nguyen

SAFA: Handling Sparse and Scarce Data in Federated Learning with Accumulative Learning

Published in IEEE Transactions on Computers, 2025

Nang Hung Nguyen, Thao Nguyen Truong, Trong Nghia Hoang, Hieu H. Pham, Thanh Hung Nguyen, Phi Le Nguyen

Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques

Published in 38th Annual Conference on Neural Information Processing Systems, 2024

Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, and Trong Nghia Hoang

Probabilistic Federated Prompt-Tuning

Published in 38th Annual Conference on Neural Information Processing Systems, 2024

Pei-Yau Weng, Minh Hoang, Lam M. Nguyen, My T. Thai, Tsui-Wei Weng, and Trong Nghia Hoang

Effective Knowledge Representation and Utilization for Sustainable Collaborative Learning across Heterogeneous Systems

Published in AI Magazine, Volume 45, Issue 3, 2024

Trong Nghia Hoang

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching

Published in 42nd International Conference on Machine Learning (ICML), 2024

Minh Hoang, Azza Fadhel, Aryan Deshwal, Jana Doppa, and Trong Nghia Hoang

Boosting Offline Optimizers with Surrogate Sensitivity

Published in 42nd International Conference on Machine Learning (ICML), 2024

Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, and Trong Nghia Hoang

Revisiting Kernel Attention with Correlated Gaussian Process Representation

Published in 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024

Long Minh Bui, Tho Tran Huu, Duy Dinh, Tan Minh Nguyen, and Trong Nghia Hoang

Pre-Trained Recommender Systems: A Causal Debiasing Perspective

Published in 17th ACM International Conference on Web Search and Data Mining, 2024

Ziqian Lin, Hao Ding, Trong Nghia Hoang, Branislav Kveton, Anoop Deoras and Hao Wang

Few-Shot Learning via Repurposing Ensemble of Black-Box Models

Published in 38th Annual AAAI Conference on Artificial Intelligence, 2024

Minh Hoang and Trong Nghia Hoang

Offline Model-based Optimization via Policy-Guided Gradient Search

Published in 38th Annual AAAI Conference on Artificial Intelligence, 2024

Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang and Janardhan Rao Doppa

AutoTransOP: Translating Omics Signatures without Orthologue Requirements using Deep Learning

Published in Nature Partner Journal: Systems Biology and Applications, 2024

Nikolaous Meimetis, Krista M. Pullen, Daniel Y. Zhu, Avlant Nilsson, Trong Nghia Hoang, Sara Magliacane and Douglas A. Lauffenburger

Incentives in Private Collaborative Machine Learning

Published in 37th Annual Conference on Neural Information Processing Systems, 2023

Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low and Patrick Jaillet

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches

Published in 23rd IEEE International Conference on Data Mining, 2023

Linbo Liu, Trong Nghia Hoang, Lam Minh Nguyen and Tsui-Wei Weng

Personalized Federated Domain Adaptation for Item-to-Item Recommendation

Published in 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Ziwei Fan, Hao Ding, Anoop Deoras and Trong Nghia Hoang

Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs

Published in 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Tengfei Ma, Trong Nghia Hoang and Jie Chen

Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms

Published in 11st International Conference on Learning Representations (ICLR), 2023

Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson and Jun Huan

Adaptive Multi-Source Causal Inference from Observational Data

Published in 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022

Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang and Tze-Yun Leong

Bayesian Federated Estimation of Causal Effects from Observational Data

Published in 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022

Thanh Vinh Vo, Young Lee, Trong Nghia Hoang and Tze-Yun Leong

Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback

Published in 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li and George Karypis

Model Fusion for Personalized Learning

Published in 38th International Conference on Machine Learning (ICML), 2021

Chi Thanh Lam (co-first author), Trong Nghia Hoang (co-first author), Kian Hsiang Low and Patrick Jaillet

AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings

Published in 30th The Web Conference (WWW), 2021

Trong Nghia Hoang, Shenda Hong, Cao Xiao, Kian Hsiang Low and Jimeng Sun

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes

Published in 34th Neural Information Processing Systems (NeurIPS), 2020

Quang Minh Hoang (co-first author), Trong Nghia Hoang (co-first author), Hai Pham and David Woodruff

Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion

Published in 37th International Conference on Machine Learning (ICML), 2020

Trong Nghia Hoang (co-first author), Chi Thanh Lam (co-first author), Kian Hsiang Low and Patrick Jaillet

CHEER: Rich Model Helps Poor Model via Knowledge Infusion

Published in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020

Cao Xiao (co-first author), Trong Nghia Hoang (co-first author), Shenda Hong, Tengfei Ma and Jimeng Sun

CASTER: Predicting Drug Interactions with Chemical Substructure Representation

Published in 34th AAAI Conference on on Artificial Intelligence (AAAI), 2020

Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas Glass and Jimeng Sun

Statistical Model Aggregation via Parameter Matching

Published in 33rd Neural Information Processing Systems (NeurIPS), 2019

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald and Trong Nghia Hoang

On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method

Published in International Conference on Computer Vision (ICCV), 2019

Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura and Xue Lin

DDL: Deep Dictionary Learning for Predictive Phenotyping

Published in 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019

Tianfan Fu (co-first author), Trong Nghia Hoang (co-first author), Cao Xiao and Jimeng Sun

RDPD: Rich Data Helps Poor Data via Imitation

Published in 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019

Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li and Jimeng Sun

Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression

Published in International Joint Conference on Neural Networks (IJCNN), 2019

Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low and Patrick Jaillet

Bayesian Nonparametric Federated Learning of Neural Networks

Published in 36th International Conference on Machine Learning (ICML), 2019

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang and Yasaman Khazaeni

Collective Model Fusion for Multiple Black-Box Experts

Published in 36th International Conference on Machine Learning (ICML), 2019

Quang Minh Hoang (co-first author), Trong Nghia Hoang (co-first author), Kian Hsiang Low and Carleton Kingsford

Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems

Published in 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019

Trong Nghia Hoang (co-first author), Quang Minh Hoang (co-first author), Kian Hsiang Low and Jonathan How

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

Published in International Conference on Robotics and Automation (ICRA), 2018

Trong Nghia Hoang (co-first author), Yuchen Xiao (co-first author), Kavinayan Sivakumar, Christopher Amato and Jonathan How

Decentralized High-Dimensional Bayesian Optimization with Factor Graphs

Published in 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018

Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang and Kian Hsiang Low

Information-Based Multi-Fidelity Bayesian Optimization

Published in NIPS-17 Workshop on Bayesian Optimization, 2017

Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low and Mohan Kankanhalli

A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression

Published in 31st AAAI Conference on Artificial Intelligence (AAAI), 2017

Quang Minh Hoang, Trong Nghia Hoang and Kian Hsiang Low

A Distributed Variational Inference Framework for Unifying Parallel Gaussian Process Regression Models

Published in 33rd International Conference on Machine Learning (ICML), 2016

Trong Nghia Hoang, Quang Minh Hoang and Kian Hsiang Low

Near-Optimal Active Learning of Multi-Output Gaussian Processes

Published in 30th AAAI Conference on Artificial Intelligence (AAAI), 2016

Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low and Mohan Kankanhalli

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data

Published in 32nd International Conference on Machine Learning (ICML), 2015

Trong Nghia Hoang, Quang Minh Hoang and Kian Hsiang Low

Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance

Published in 8th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), 2014

Prabhu Natarajan, Trong Nghia Hoang, Yongkang Wong, Kian Hsiang Low and Mohan Kankanhalli

Active Learning is Planning: Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes

Published in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML/PKDD-14 NECTAR (New Scientific and Technical Advances in Research), 2014

Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet and Mohan Kankanhalli

Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes

Published in 31st International Conference on Machine Learning (ICML), 2014

Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet and Mohan Kankanhalli

Recent Advances in Scaling up Gaussian Process Predictive Models for Large Spatiotemporal Data

Published in Dynamic Data-Driven Environmental Systems Science - First International Conference (DyDESS), 2014

Kian Hsiang Low, Jie Chen, Trong Nghia Hoang, Nuo Xu and Patrick Jaillet

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior

Published in 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013

Trong Nghia Hoang and Kian Hsiang Low

Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents

Published in 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013

Trong Nghia Hoang and Kian Hsiang Low

Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments

Published in 6th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), 2012

Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low and Mohan Kankanhalli

Decision-Theoretic Approach to Maximizing Observation of Multiple Targets in Multi-Camera Surveillance

Published in 11th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2012

Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low and Mohan Kankanhalli

Intention-Aware Planning under Uncertainty for Interacting with Self-Interested Boundedly Rational Agents

Published in 11th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2012

Trong Nghia Hoang and Kian Hsiang Low