Trong Nghia Hoang

According to our database, Trong Nghia Hoang authored at least 54 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, Trong Nghia Hoang, Phi Le Nguyen, Thanh Trung Huynh

CoRR, 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

CoRR, 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

Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems.

Trong Nghia Hoang

AI Mag., 2024

Pre-trained Recommender Systems: A Causal Debiasing Perspective.

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

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Boosting Offline Optimizers with Surrogate Sensitivity.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Minh Hoang, Trong Nghia Hoang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Collaborative Learning across Heterogeneous Systems with Pre-Trained Models.

Trong Nghia Hoang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Offline Model-Based Optimization via Policy-Guided Gradient Search.

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

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization.

Alireza Ghods, Trong Nghia Hoang, Diane J. Cook

CoRR, 2023

Federated learning of models pre-trained on different features with consensus graphs.

Tengfei Ma, Trong Nghia Hoang, Jie Chen

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Personalized federated domain adaptation for item-to-item recommendation.

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

Proceedings of the Uncertainty in Artificial Intelligence, 2023

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

Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches.

Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng

Proceedings of the IEEE International Conference on Data Mining, 2023

2022

CHEER: Rich Model Helps Poor Model via Knowledge Infusion.

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

IEEE Trans. Knowl. Data Eng., 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

Bayesian federated estimation of causal effects from observational data.

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

Proceedings of the Uncertainty in Artificial Intelligence, 2022

The First Impression Counts! The Importance of Onboarding for Educational Chatbots.

Trong Nghia Hoang, Andreas Reich, Matthias Wölfel

Proceedings of the DELFI 2022, 2022

Adaptive Multi-Source Causal Inference from Observational Data.

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

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

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

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Federated Estimation of Causal Effects from Observational Data.

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

CoRR, 2021

Adaptive Multi-Source Causal Inference.

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

CoRR, 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

Proceedings of the WWW '21: The Web Conference 2021, 2021

Model Fusion for Personalized Learning.

Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet

Proceedings of the 38th International Conference on Machine Learning, 2021

Verifiably safe exploration for end-to-end reinforcement learning.

Nathan Hunt, Nathan Fulton, Sara Magliacane, Trong Nghia Hoang, Subhro Das, Armando Solar-Lezama

Proceedings of the HSCC '21: 24th ACM International Conference on Hybrid Systems: Computation and Control, 2021

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

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

Proceedings of the 37th International Conference on Machine Learning, 2020

CASTER: Predicting Drug Interactions with Chemical Substructure Representation.

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

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Statistical Model Aggregation via Parameter Matching.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression.

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

Proceedings of the International Joint Conference on Neural Networks, 2019

RDPD: Rich Data Helps Poor Data via Imitation.

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

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

DDL: Deep Dictionary Learning for Predictive Phenotyping.

Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Bayesian Nonparametric Federated Learning of Neural Networks.

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

Proceedings of the 36th International Conference on Machine Learning, 2019

Collective Model Fusion for Multiple Black-Box Experts.

Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford

Proceedings of the 36th International Conference on Machine Learning, 2019

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems.

Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

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

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

Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How

Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Decentralized High-Dimensional Bayesian Optimization With Factor Graphs.

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

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models.

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

CoRR, 2017

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

Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

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

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

Proceedings of the 33nd International Conference on Machine Learning, 2016

Near-Optimal Active Learning of Multi-Output Gaussian Processes.

Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes.

Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli

Proceedings of the 31th International Conference on Machine Learning, 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

Proceedings of the International Conference on Distributed Smart Cameras, 2014

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

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

Proceedings of the Dynamic Data-Driven Environmental Systems Science, 2014

2013

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

Trong Nghia Hoang, Kian Hsiang Low

Proceedings of the IJCAI 2013, 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

Proceedings of the IJCAI 2013, 2013

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

Proceedings of the Sixth International Conference on Distributed Smart Cameras, 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

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, 2012

Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents.

Trong Nghia Hoang, Kian Hsiang Low

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, 2012

2008

A Multi-agent Mechanism in Machine Learning Approach to Anti-virus System.

Minh Nhat Quang Truong, Trong Nghia Hoang

Proceedings of the Agent and Multi-Agent Systems: Technologies and Applications, 2008