Trong Nghia Hoang

Orcid: 0000-0002-9175-6246

According to our database1, Trong Nghia Hoang authored at least 55 papers between 2008 and 2024.

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Bibliography

2024
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization.
CoRR, 2024

FedCert: Federated Accuracy Certification.
CoRR, 2024

Incentives in Private Collaborative Machine Learning.
CoRR, 2024

Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems.
AI Mag., 2024

Pre-trained Recommender Systems: A Causal Debiasing Perspective.
Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Boosting Offline Optimizers with Surrogate Sensitivity.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Few-Shot Learning via Repurposing Ensemble of Black-Box Models.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Collaborative Learning across Heterogeneous Systems with Pre-Trained Models.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Offline Model-Based Optimization via Policy-Guided Gradient Search.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization.
CoRR, 2023

Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs.
CoRR, 2023

Federated learning of models pre-trained on different features with consensus graphs.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Personalized federated domain adaptation for item-to-item recommendation.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches.
Proceedings of the IEEE International Conference on Data Mining, 2023

2022
CHEER: Rich Model Helps Poor Model via Knowledge Infusion.
IEEE Trans. Knowl. Data Eng., 2022

Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms.
CoRR, 2022

Bayesian federated estimation of causal effects from observational data.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

The First Impression Counts! The Importance of Onboarding for Educational Chatbots.
Proceedings of the DELFI 2022, 2022

Adaptive Multi-Source Causal Inference from Observational Data.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Federated Estimation of Causal Effects from Observational Data.
CoRR, 2021

Adaptive Multi-Source Causal Inference.
CoRR, 2021

AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings.
Proceedings of the WWW '21: The Web Conference 2021, 2021

Model Fusion for Personalized Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Verifiably safe exploration for end-to-end reinforcement learning.
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.
CoRR, 2020

Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion.
Proceedings of the 37th International Conference on Machine Learning, 2020

CASTER: Predicting Drug Interactions with Chemical Substructure Representation.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Statistical Model Aggregation via Parameter Matching.
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.
Proceedings of the International Joint Conference on Neural Networks, 2019

RDPD: Rich Data Helps Poor Data via Imitation.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

DDL: Deep Dictionary Learning for Predictive Phenotyping.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Bayesian Nonparametric Federated Learning of Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

Collective Model Fusion for Multiple Black-Box Experts.
Proceedings of the 36th International Conference on Machine Learning, 2019

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems.
CoRR, 2018

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems.
Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Decentralized High-Dimensional Bayesian Optimization With Factor Graphs.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models.
CoRR, 2017

A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression.
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.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Near-Optimal Active Learning of Multi-Output Gaussian Processes.
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.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes.
Proceedings of the 31th International Conference on Machine Learning, 2014

Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance.
Proceedings of the International Conference on Distributed Smart Cameras, 2014

Recent Advances in Scaling Up Gaussian Process Predictive Models for Large Spatiotemporal Data.
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.
Proceedings of the IJCAI 2013, 2013

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior.
Proceedings of the IJCAI 2013, 2013

2012
Decision-theoretic coordination and control for active multi-camera surveillance in uncertain, partially observable environments.
Proceedings of the Sixth International Conference on Distributed Smart Cameras, 2012

Decision-theoretic approach to maximizing observation of multiple targets in multi-camera surveillance.
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.
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.
Proceedings of the Agent and Multi-Agent Systems: Technologies and Applications, 2008


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