T. Hoang

发表

Statistical Model Aggregation via Parameter Matching

Kristjan H. Greenewald, Soumya Ghosh, Mikhail Yurochkin, 2019, NeurIPS.

Supplementary Material for Bayesian Nonparametric Federated Learning of Neural Networks

Kristjan H. Greenewald, Mikhail Yurochkin, Mayank Agarwal, 2019 .

Improving Operational Planning study models with historical generation and load data

Jun Zhu, Aftab Alam, Vincent Frey, 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

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

K. H. Low, T. Hoang, 2012, AAMAS.

Adaptive Multi-Source Causal Inference from Observational Data

Thanh Vinh Vo, T. Leong, Pengfei Wei, 2022, CIKM.

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

Jun Huan, T. Hoang, Hilaf Hasson, 2022, ArXiv.

Bayesian Nonparametric Federated Learning of Neural Networks

Yasaman Khazaeni, Kristjan H. Greenewald, Mayank Agarwal, 2019, ICML.

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

Kian Hsiang Low, Trong Nghia Hoang, K. H. Low, 2013, IJCAI.

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

Mohan S. Kankanhalli, Kian Hsiang Low, Patrick Jaillet, 2014, ICML.

Near-Optimal Active Learning of Multi-Output Gaussian Processes

Mohan S. Kankanhalli, Kian Hsiang Low, Trong Nghia Hoang, 2015, AAAI.

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

Mohan S. Kankanhalli, Kian Hsiang Low, Patrick Jaillet, 2014, ECML/PKDD.

Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments

Mohan S. Kankanhalli, Prabhu Natarajan, Kian Hsiang Low, 2012, 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC).

CASTER: Predicting Drug Interactions with Chemical Substructure Representation

Jimeng Sun, Kexin Huang, Cao Xiao, 2019, AAAI.

Information-Based Multi-Fidelity Bayesian Optimization

Bryan Kian Hsiang Low, Yehong Zhang, Trong Nghia Hoang, 2017 .

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

Patrick Jaillet, Bryan Kian Hsiang Low, Trong Nghia Hoang, 2020, ICML.

DDL: Deep Dictionary Learning for Predictive Phenotyping

Jimeng Sun, Cao Xiao, Trong Nghia Hoang, 2019, IJCAI.

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

T. Hoang, Hilaf Hasson, Youngsuk Park, 2022, ICLR.

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

Kian Hsiang Low, Quang Minh Hoang, Trong Nghia Hoang, 2015, ICML.

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

T. Hoang, Tengfei Ma, Jie Chen, 2023, UAI.

Personalized Federated Domain Adaptation for Item-to-Item Recommendation

Anoop Deoras, T. Hoang, Ziwei Fan, 2023, UAI.

Federated Estimation of Causal Effects from Observational Data pdf

Tze-Yun Leong, Thanh Vinh Vo, Trong Nghia Hoang, 2021, ArXiv.

Adaptive Multi-Source Causal Inference pdf

Tze-Yun Leong, Pengfei Wei, Thanh Vinh Vo, 2021, ArXiv.

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

Jimeng Sun, Cao Xiao, Shenda Hong, 2021, WWW.

Model Fusion for Personalized Learning

Kian Hsiang Low, Trong Nghia Hoang, K. H. Low, 2021, ICML.

Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression

Kian Hsiang Low, Patrick Jaillet, Haibin Yu, 2017, 2019 International Joint Conference on Neural Networks (IJCNN).

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

Trong Nghia Hoang, Minh Nhat Quang Truong, T. Hoang, 2008, KES-AMSTA.

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

Jonathan P. How, Christopher Amato, Trong Nghia Hoang, 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance

Mohan S. Kankanhalli, Prabhu Natarajan, Kian Hsiang Low, 2012, AAMAS.

Autoencoder Model for Translating Omics Signatures

D. Lauffenburger, Avlant Nilsson, Sara Magliacane, 2023, bioRxiv.

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes pdf

David P. Woodruff, Hai Pham, Quang Minh Hoang, 2020, NeurIPS.

Bayesian federated estimation of causal effects from observational data

Thanh Vinh Vo, T. Leong, T. Hoang, 2022, UAI.

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

Kian Hsiang Low, Quang Minh Hoang, Trong Nghia Hoang, 2016, AAAI.

Decentralized High-Dimensional Bayesian Optimization with Factor Graphs pdf

Kian Hsiang Low, Quang Minh Hoang, Trong Nghia Hoang, 2017, AAAI.

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

Kian Hsiang Low, Quang Minh Hoang, Trong Nghia Hoang, 2016, ICML.

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

Kian Hsiang Low, Patrick Jaillet, Nuo Xu, 2014, DyDESS.

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

Kian Hsiang Low, Trong Nghia Hoang, K. H. Low, 2013, IJCAI.

Collective Model Fusion for Multiple Black-Box Experts

Kian Hsiang Low, Carl Kingsford, Quang Minh Hoang, 2019, ICML.

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

K. H. Low, T. Hoang, 2013, IJCAI.

Autoencoder Model for Translating Omics Signatures

D. Lauffenburger, Avlant Nilsson, Sara Magliacane, 2023, bioRxiv.

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

Alireza Ghods, T. Hoang, Diane J. Cook, 2023, ArXiv.