Trong Nghia Hoang

93 papers A* 29A 9B 3Journal 45Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship. CoRR Nang Hung Nguyen, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang, Masashi Sugiyama
2026	J	`jnl`	Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship. Trans. Mach. Learn. Res. Nang Hung Nguyen, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang, Masashi Sugiyama
2026	J	`jnl`	Continual Fine-Tuning with Provably Accurate and Parameter-Free Task Retrieval. CoRR Hang Thi-Thuy Le, Long Minh Bui, Minh Hoang, Trong Nghia Hoang
2026	J	`jnl`	Diffusion-Inspired Reconfiguration of Transformers for Uncertainty Calibration. CoRR Manh Cuong Dao, Quang Hung Pham, Phi Le Nguyen, Truong Thao Nguyen, Bryan Kian Hsiang Low, Trong Nghia Hoang
2026	J	`jnl`	Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data. CoRR Thu Hang Phung, Duong M. Nguyen, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Trong Nghia Hoang, Phi Le Nguyen
2026	A*	`conf`	ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model. AAAI Krishu K. Thapa, Supriya Savalkar, Bhupinderjeet Singh, Trong Nghia Hoang, Kirti Rajagopalan, Ananth Kalyanaraman
2026	J	`jnl`	Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning. CoRR Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo, Trong Nghia Hoang, Hyuk-Yoon Kwon, My T. Thai
2026	A*	`conf`	Nanoporous Materials Discovery via Search Bias-Guided Surrogate Modeling. AAAI Azza Fadhel, Yassine Chemingui, Minh Hoang, Aryan Deshwal, Trong Nghia Hoang, Jana Doppa
2026	J	`jnl`	Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting. CoRR Trong Khiem Tran, Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang
2025	J	`jnl`	Boosting Offline Optimizers with Surrogate Sensitivity. CoRR Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang
2025	J	`jnl`	Expressive and Scalable Quantum Fusion for Multimodal Learning. CoRR Tuyen Nguyen, Trong Nghia Hoang, Phi Le Nguyen, Hai L. Vu, Truong Cong Thang
2025	J	`jnl`	ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model. CoRR Krishu K. Thapa, Supriya Savalkar, Bhupinderjeet Singh, Trong Nghia Hoang, Kirti Rajagopalan, Ananth Kalyanaraman
2025	J	`jnl`	Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques. CoRR Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang
2025	J	`jnl`	Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data. CoRR Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Phi Le Nguyen
2025	J	`jnl`	Learning Surrogates for Offline Black-Box Optimization via Gradient Matching. CoRR Minh Hoang, Azza Fadhel, Aryan Deshwal, Janardhan Rao Doppa, Trong Nghia Hoang
2025	J	`jnl`	Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. CoRR Pei-Yau Weng, Minh Hoang, Lam M. Nguyen, My T. Thai, Tsui-Wei Weng, Trong Nghia Hoang
2025	J	`jnl`	ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge. CoRR Manh Cuong Dao, The Hung Tran, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang
2025	J	`jnl`	Revisiting Kernel Attention with Correlated Gaussian Process Representation. CoRR Long Minh Bui, Tho Tran Huu, Duy Dinh, Tan Minh Nguyen, Trong Nghia Hoang
2025	J	`jnl`	SAFA: Handling Sparse and Scarce Data in Federated Learning With Accumulative Learning. IEEE Trans. Computers Nang Hung Nguyen, Truong Thao Nguyen, Trong Nghia Hoang, Hieu H. Pham, Thanh Hung Nguyen, Phi Le Nguyen
2024	A*	`conf`	Boosting Offline Optimizers with Surrogate Sensitivity. ICML Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang
2024	A*	`conf`	Collaborative Learning across Heterogeneous Systems with Pre-Trained Models. AAAI Trong Nghia Hoang
2024	J	`jnl`	Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems. AI Mag. Trong Nghia Hoang
2024	B	`conf`	FedCert: Federated Accuracy Certification. NCA Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong Nguyen, Trong Nghia Hoang, Truong Thao Nguyen, Phi Le Nguyen
2024	J	`jnl`	FedCert: Federated Accuracy Certification. CoRR Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong Nguyen, Trong Nghia Hoang, Truong Thao Nguyen, Phi Le Nguyen
2024	B	`conf`	FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization. NCA Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia Hoang, Phi Le Nguyen, Thanh Trung Huynh
2024	J	`jnl`	FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization. CoRR Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia Hoang, Phi Le Nguyen, Thanh Trung Huynh
2024	A*	`conf`	Few-Shot Learning via Repurposing Ensemble of Black-Box Models. AAAI Minh Hoang, Trong Nghia Hoang
2024	J	`jnl`	Incentives in Private Collaborative Machine Learning. CoRR Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
2024	A*	`conf`	Learning Surrogates for Offline Black-Box Optimization via Gradient Matching. ICML Minh Hoang, Azza Fadhel, Aryan Deshwal, Jana Doppa, Trong Nghia Hoang
2024	A*	`conf`	Offline Model-Based Optimization via Policy-Guided Gradient Search. AAAI Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang, Janardhan Rao Doppa
2024	J	`jnl`	Offline Model-Based Optimization via Policy-Guided Gradient Search. CoRR Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang, Janardhan Rao Doppa
2024	A	`conf`	Pre-trained Recommender Systems: A Causal Debiasing Perspective. WSDM Ziqian Lin, Hao Ding, Trong Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang
2024	A*	`conf`	Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. NeurIPS Pei-Yau Weng, Minh Hoang, Lam M. Nguyen, My T. Thai, Lily Weng, Trong Nghia Hoang
2024	A	`conf`	Revisiting Kernel Attention with Correlated Gaussian Process Representation. UAI Long Minh Bui, Tho Tran Huu, Duy Dinh, Tan Minh Nguyen, Trong Nghia Hoang
2023	J	`jnl`	Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs. CoRR Tengfei Ma, Trong Nghia Hoang, Jie Chen
2023	A	`conf`	Federated learning of models pre-trained on different features with consensus graphs. UAI Tengfei Ma, Trong Nghia Hoang, Jie Chen
2023	J	`jnl`	Personalized Federated Domain Adaptation for Item-to-Item Recommendation. CoRR Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang
2023	A	`conf`	Personalized federated domain adaptation for item-to-item recommendation. UAI Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang
2023	A*	`conf`	Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. ICDM Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
2023	J	`jnl`	Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. CoRR Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
2023	A*	`conf`	Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. ICLR Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan
2023	J	`jnl`	Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization. CoRR Alireza Ghods, Trong Nghia Hoang, Diane J. Cook
2022	A	`conf`	Adaptive Multi-Source Causal Inference from Observational Data. CIKM Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong
2022	A	`conf`	Bayesian federated estimation of causal effects from observational data. UAI Thanh Vinh Vo, Young Lee, Trong Nghia Hoang, Tze-Yun Leong
2022	J	`jnl`	CHEER: Rich Model Helps Poor Model via Knowledge Infusion. IEEE Trans. Knowl. Data Eng. Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun
2022	A	`conf`	Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. AISTATS Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis
2022	J	`jnl`	Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback. CoRR Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis
2022	—	`conf`	The First Impression Counts! The Importance of Onboarding for Educational Chatbots. DELFI Trong Nghia Hoang, Andreas Reich, Matthias Wölfel
2022	J	`jnl`	Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. CoRR Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan
2021	A*	`conf`	AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings. WWW Trong Nghia Hoang, Shenda Hong, Cao Xiao, Bryan Low, Jimeng Sun
2021	J	`jnl`	Adaptive Multi-Source Causal Inference. CoRR Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong
2021	J	`jnl`	Federated Estimation of Causal Effects from Observational Data. CoRR Thanh Vinh Vo, Trong Nghia Hoang, Young Lee, Tze-Yun Leong
2021	A*	`conf`	Model Fusion for Personalized Learning. ICML Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
2021	—	`conf`	Verifiably safe exploration for end-to-end reinforcement learning. HSCC Nathan Hunt, Nathan Fulton, Sara Magliacane, Trong Nghia Hoang, Subhro Das, Armando Solar-Lezama
2020	A*	`conf`	CASTER: Predicting Drug Interactions with Chemical Substructure Representation. AAAI Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas Glass, Jimeng Sun
2020	J	`jnl`	CHEER: Rich Model Helps Poor Model via Knowledge Infusion. CoRR Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun
2020	A*	`conf`	Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. ICML Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet
2020	J	`jnl`	Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. CoRR Quang Minh Hoang, Trong Nghia Hoang, Hai Pham, David P. Woodruff
2019	A*	`conf`	Bayesian Nonparametric Federated Learning of Neural Networks. ICML Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
2019	J	`jnl`	Bayesian Nonparametric Federated Learning of Neural Networks. CoRR Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
2019	J	`jnl`	CASTER: Predicting Drug Interactions with Chemical Substructure Representation. CoRR Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas M. Glass, Jimeng Sun
2019	A*	`conf`	Collective Model Fusion for Multiple Black-Box Experts. ICML Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford
2019	A*	`conf`	Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems. AAAI Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How
2019	A*	`conf`	DDL: Deep Dictionary Learning for Predictive Phenotyping. IJCAI Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun
2019	A*	`conf`	RDPD: Rich Data Helps Poor Data via Imitation. IJCAI Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun
2019	A*	`conf`	Statistical Model Aggregation via Parameter Matching. NeurIPS Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang
2019	J	`jnl`	Statistical Model Aggregation via Parameter Matching. CoRR Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang
2019	B	`conf`	Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. IJCNN Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
2018	J	`jnl`	Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems. CoRR Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan P. How
2018	A*	`conf`	Decentralized High-Dimensional Bayesian Optimization With Factor Graphs. AAAI Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low
2018	A*	`conf`	Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. ICRA Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How
2017	A*	`conf`	A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. AAAI Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low
2017	J	`jnl`	Decentralized High-Dimensional Bayesian Optimization with Factor Graphs. CoRR Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low
2017	J	`jnl`	Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems. CoRR Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan P. How
2017	J	`jnl`	Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models. CoRR Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet
2016	A*	`conf`	A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models. ICML Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low
2016	J	`jnl`	A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression. CoRR Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low
2016	A*	`conf`	Near-Optimal Active Learning of Multi-Output Gaussian Processes. AAAI Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
2015	A*	`conf`	A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data. ICML Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low
2015	J	`jnl`	Near-Optimal Active Learning of Multi-Output Gaussian Processes. CoRR Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
2014	—	`conf`	Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes. ECML/PKDD (3) Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli
2014	A*	`conf`	Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes. ICML Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan S. Kankanhalli
2014	—	`conf`	Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data. DyDESS Kian Hsiang Low, Jie Chen, Trong Nghia Hoang, Nuo Xu, Patrick Jaillet
2014	—	`conf`	Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance. ICDSC Prabhu Natarajan, Trong Nghia Hoang, Yongkang Wong, Kian Hsiang Low, Mohan S. Kankanhalli
2013	J	`jnl`	A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior CoRR Trong Nghia Hoang, Kian Hsiang Low
2013	A*	`conf`	A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior. IJCAI Trong Nghia Hoang, Kian Hsiang Low
2013	J	`jnl`	Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents CoRR Trong Nghia Hoang, Kian Hsiang Low
2013	A*	`conf`	Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents. IJCAI Trong Nghia Hoang, Kian Hsiang Low
2012	J	`jnl`	Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments. CoRR Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
2012	A	`conf`	Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance. AAMAS Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
2012	—	`conf`	Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments. ICDSC Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
2012	A	`conf`	Intention-aware planning under uncertainty for interacting with self-interested, boundedly rational agents. AAMAS Trong Nghia Hoang, Kian Hsiang Low
2008	—	`conf`	A Multi-agent Mechanism in Machine Learning Approach to Anti-virus System. KES-AMSTA Minh Nhat Quang Truong, Trong Nghia Hoang