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DTSTART:20241103T020000
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DESCRIPTION:Note: BIS 526 students are required to attend in person. Other
 s are invited to attend in person but may also attend via zoom. Speaker K
 un Chen\, Ph.D. Title- “Transfer Learning under Real-World Heterogeneity:
  Across-Site Contamination” Abstract Transfer learning and data fusion ai
 m to improve prediction for a target population by borrowing information 
 from related sources. There has been an increasing need for such methods 
 in multi-site EHR networks and health information exchange (HIE) settings
 . In this talk\, we discuss transfer learning under real-world heterogene
 ity\, which can be either a friend or a foe. As a friend\, heterogeneity 
 is structured : sources and the target form clusters of similarity. In th
 is regime\, we present a three-stage\, cluster-aware transfer-learning pr
 ocedure that improves the bias–variance tradeoff and demonstrate it in fa
 cility-specific suicide risk modeling across 27 Connecticut hospitals. As
  a foe\, heterogeneity is unstructured : external data may be large but c
 ontaminated by arbitrary outliers. In this regime\, we study how to selec
 t/sample from the external data\, deriving non-asymptotic theory that mak
 es explicit how performance depends on target size\, sampling rate\, sign
 al strength\, outlier magnitude\, and error tail behavior. The take-home 
 message is simple: more is not necessarily better\, and how much you can 
 borrow depends on how much you have.\n\nAdmission:\nFree\n\nDetails URL:\
 nhttps://medicine.yale.edu/event/ysph-biostatistics-seminar-15/\n
DTEND;TZID=America/New_York:20260421T130000
DTSTAMP:20261028T161324Z
DTSTART;TZID=America/New_York:20260421T120000
GEO:41.302961;-72.931638
LOCATION:106 A&B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar- “Transfer Learning under Real-World He
 terogeneity: Across-Site Contamination”
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