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
Publications
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