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X-WR-CALNAME:Biomedical Mathematics Group
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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20250627T140000
DTEND;TZID=Asia/Seoul:20250627T160000
DTSTAMP:20260423T044426
CREATED:20250426T143642Z
LAST-MODIFIED:20250609T001825Z
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SUMMARY:Data splitting to avoid information leakage with DataSAIL - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper\, “Data splitting to avoid information leakage with DataSAIL” by Roman Joeres\, et al.\, Nature Communications\, 2025. \nAbstract \nInformation leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model’s training\, it risks memorizing the training data instead of learning generalizable properties. This can lead to inflated performance metrics that do not reflect the actual performance at inference time. We present DataSAIL\, a versatile Python package to facilitate leakage-reduced data splitting to enable realistic evaluation of machine learning models for biological data that are intended to be applied in out-of-distribution scenarios. DataSAIL is based on formulating the problem to find leakage-reduced data splits as a combinatorial optimization problem. We prove that this problem is NP-hard and provide a scalable heuristic based on clustering and integer linear programming. Finally\, we empirically demonstrate DataSAIL’s impact on evaluating biomedical machine learning models.
URL:https://www.ibs.re.kr/bimag/event/data-splitting-to-avoid-information-leakage-with-datasail-myna-lim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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