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Kwang-Sung Jun
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Kwang-Sung Jun12 visible / 12 total nodes / 24 links

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Co-authorshipCo-authorshipCo-authorshipAuthorshipAuthorshipWorks onAuthorshipWorks onWorks onAuthorshipAuthorshipAuthorshipAuthorshipAuthorshipAuthorshipAuthorshipTopic signalTopic signalTopic signalTopic signalTopic signalTopic signalTopic signalTopic signalKwang-Sung JunAKwang-Sung JunResearcher$\varepsilon$-Good Action Identification in Fixed-Budget Monte Carlo Tree SearchW$\varepsilon$-Good Acti...preprint / 2026Tuan NguyenATuan NguyenResearcherYinan LiAYinan LiResearcherImproved Confidence Bounds for the Linear Logistic Model and Applications to Linear BanditsWImproved Confidence Bou...preprint / 2021Graph-Based Active Learning: A New Look at Expected Error MinimizationWGraph-Based Active Lear...preprint / 2016Machine LearningTMachine Learning49008 worksAn Experimental Design Approach for Regret Minimization in Logistic BanditsWAn Experimental Design ...preprint / 2022Maillard Sampling: Boltzmann Exploration Done OptimallyWMaillard Sampling: Bolt...preprint / 2022Tight Concentrations and Confidence Sequences from the Regret of Universal PortfolioWTight Concentrations an...preprint / 2022Jointly Efficient and Optimal Algorithms for Logistic BanditsWJointly Efficient and O...preprint / 2022Coverage Improvement and Fast Convergence of On-policy Preference LearningWCoverage Improvement an...preprint / 2026
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Kwang-Sung Jun

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Paperε\varepsilon-Good Action Identification in Fixed-Budget Monte Carlo Tree SearchAuthorship / preprint / 2026PersonTuan NguyenCo-authorship / ResearcherPersonYinan LiCo-authorship / ResearcherPaperImproved Confidence Bounds for the Linear Logistic Model and Applications to Linear BanditsAuthorship / preprint / 2021PaperGraph-Based Active Learning: A New Look at Expected Error MinimizationAuthorship / preprint / 2016TopicMachine LearningWorks on / 49008 worksPaperAn Experimental Design Approach for Regret Minimization in Logistic BanditsAuthorship / preprint / 2022PaperMaillard Sampling: Boltzmann Exploration Done OptimallyAuthorship / preprint / 2022

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