사진

Kim, Gi-Soo (김지수)

Department
Department of Industrial Engineering(산업공학과)
Lab
Statistical Decision Making (통계적 의사결정 )
Research Keywords
순차적 의사결정, 다중슬롯머신, 온라인 학습, Sequential Decision, Multi-armed bandit algorithms, Online learning, Causal inference, Policy evaluation, Missing data analysis
Research Interests
Our research interests are focused on statistical approaches to the sequential decision problem. The multi-armed bandit (MAB) problem formulates the sequential decision problem in which a learner is sequentially faced with a set of available actions, chooses an action, and receives a random reward in response. The actions are often described as the arms of a bandit slot machine. The act of choosing an action is characterized as pulling an arm of the bandit machine, where different arms give possibly different rewards. By repeating the process of pulling arms and receiving rewards, the learner accumulates information about the reward compensation mechanism and learns from it, choosing the arm that is close to optimal as time elapses. In our lab, we integrate online learning and optimization techniques to develop algorithms that efficiently learn the reward model while maximizing the rewards. We also apply the developed algorithms to real tasks such as recommendation systems and mobile health apps. We also use causal inference to evaluate the performance of multi-armed bandit algorithms in a retrospective way.
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Issue DateTitleAuthor(s)TypeViewAltmetrics
2020-10Validation of an integrated service model, Health-RESPECT, for older patients in long-term care institution using information and communication technologies: protocol of a cluster randomised controlled trialChoi, Jung-Yeon; Kim, Kwang-il; Kim, Hongsoo, et alARTICLE67 Validation of an integrated service model, Health-RESPECT, for older patients in long-term care institution using information and communication technologies: protocol of a cluster randomised controlled trial
2020-07Cluster-specific nonignorably missing, endogenous, and continuous regressors in multilevel model for binary outcomeKim, Gi-Soo; Lee, Youngjo; Kim, Hongsoo, et alARTICLE109 Cluster-specific nonignorably missing, endogenous, and continuous regressors in multilevel model for binary outcome
2020-05시각자료를 활용한 북한산 둘레길 방문객의 야외 여가활동 장소지표 선호도 분석윤지인; 김지수ARTICLE85
2017-09Causal inference with observational data under cluster-specific non-ignorable assignment mechanismKim, Gi-Soo; Paik, Myunghee Cho; Kim, HongsooARTICLE68 Causal inference with observational data under cluster-specific non-ignorable assignment mechanism
2017-04Evaluation of a technology-enhanced integrated care model for frail older persons: protocol of the SPEC study, a stepped-wedge cluster randomized trial in nursing homesKim, Hongsoo; Park, Yeon-Hwan; Jung, Young-il, et alARTICLE195 Evaluation of a technology-enhanced integrated care model for frail older persons: protocol of the SPEC study, a stepped-wedge cluster randomized trial in nursing homes
2015-12Predictors and outcomes of unplanned readmission to a different hospitalKim, Hongsoo; Hung, William W.; Paik, Myunghee Cho, et alARTICLE283 Predictors and outcomes of unplanned readmission to a different hospital

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