Featured Publications
Detecting model misconducts in decentralized healthcare federated learning
Kuo T, Pham A. Detecting model misconducts in decentralized healthcare federated learning. International Journal Of Medical Informatics 2021, 158: 104658. PMID: 34923447, PMCID: PMC10017272, DOI: 10.1016/j.ijmedinf.2021.104658.Peer-Reviewed Original ResearchFederated learningFederated machine learningDetection methodDetection frameworkLearning iterationsMalicious intentLearning methodsMachine learningArtificial intelligenceParameter tuningComputational costCross-institutional collaborationLearningLearning processIterationPerformance detectorsDetectionIncorrect modelFrameworkAlgorithmDatasetDetect misconductIntelligencePlagiarismMethod
2020
The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm
Kuo T. The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm. JAMIA Open 2020, 3: 201-208. PMID: 32734160, PMCID: PMC7382618, DOI: 10.1093/jamiaopen/ooaa017.Peer-Reviewed Original ResearchBlockchain-based approachOnline machine learningSoftware development perspectiveProtect patient dataPrivacy technologiesBlockchain networkProgramming languageBlockchain technologyAlgorithmic objectivityImplementation detailsNon-determinismMachine learningSystem architectureBlockchainPatient dataDevelopment configurationAlgorithmTechnical componentsOnline learningSoftwareLearningTechnologyDesign detailsImplementationPrediction model
2014
Minimizing Expected Loss for Risk-Avoiding Reinforcement Learning
Yeh J, Kuo T, Chen W, Lin S. Minimizing Expected Loss for Risk-Avoiding Reinforcement Learning. 2014, 11-17. DOI: 10.1109/dsaa.2014.7058045.Peer-Reviewed Original ResearchA Transfer-Learning Approach to Exploit Noisy Information for Classification and Its Application on Sentiment Detection
Lin W, Kuo T, Huang Y, Lu W, Lin S. A Transfer-Learning Approach to Exploit Noisy Information for Classification and Its Application on Sentiment Detection. Lecture Notes In Computer Science 2014, 8916: 262-273. DOI: 10.1007/978-3-319-13987-6_25.Peer-Reviewed Original Research