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SUMMARY:Biostatistics Seminar Series: “Nonparametric Array Learning and its Applications in Neuroimaging”
DESCRIPTION:The Fred Hutch Biostatistics Program hosts seminars featuring presentations by Hutch and outside scientists to share their latest developments and recent research. Each seminar includes an hour-long presentation and discussion during which speakers showcase their work and findings. \nThis seminar will be held on Zoom due to the COVID-19 pandemic. \n\n\n\n\n\n\n\n\n\n\n\n\n  \nBiostatistics Seminar Series:  \n“Nonparametric array learning and its applications in neuroimaging” \n\n\n\n\n\n\n\n\n\n\n\n\nArray-valued predictors ubiquitously arise in brain connectivity studies\, sensor network localization\, and integrative genomics. We consider the problem of learning the relationship between a scale-valued response and a high-dimensional array-valued predictor. Traditional regression methods take a parametric procedure by imposing a priori functional form between variables. These parametric models\, however\, are inadequate for structure learning and often fail in accurate prediction. Here\, we develop a learning reduction framework to address a range of learning tasks from classification to regression for matrix-valued predictors. Our proposal achieves interpretable prediction via a low-rank two-way sparse halfspace learning for the target function level sets. Statistical accuracy\, excess risk bounds\, and efficient algorithms are established. We demonstrate the advantage of our method over previous approaches through applications to human brain connectome data. Time allowed\, I will present our recent results on nonparametric approaches to tensor estimation and completion. \n\n\n\n\n\nDate:\nWednesday\, February 03\, 2021\n\n\nStart Time:\n12 p.m. PST\n\n\nHost or Sponsor:\nBiostatistics Seminar Series\n\n\nLocation:\nZoom Webinar\n\nhttps://zoom.us/j/95933099692?pwd=ZTBvOWZCK01KTFRCVnFUSjdJeXF5UT09 \n\n\n\nSpeaker or Presenter:\nMiaoyan Wang\n\n\nContact Information:\nshirata@fredhutch.org
URL:https://scienceinseattle.com/event/biostatistics-seminar-series-nonparametric-array-learning-and-its-applications-in-neuroimaging/
LOCATION:Online
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BEGIN:VEVENT
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DTSTAMP:20260718T081803
CREATED:20210201T181305Z
LAST-MODIFIED:20210201T181305Z
UID:25663-1612353600-1612357200@scienceinseattle.com
SUMMARY:Biostatistics Seminar Series: “Nonparametric Array Learning and its Applications in Neuroimaging”
DESCRIPTION:The Fred Hutch Biostatistics Program hosts seminars featuring presentations by Hutch and outside scientists to share their latest developments and recent research. Each seminar includes an hour-long presentation and discussion during which speakers showcase their work and findings. \nThis seminar will be held on Zoom due to the COVID-19 pandemic. \n\n\n\n\n\n\n\n\n\n\n\n\n  \nBiostatistics Seminar Series:  \n“Nonparametric array learning and its applications in neuroimaging” \n\n\n\n\n\n\n\n\n\n\n\n\nArray-valued predictors ubiquitously arise in brain connectivity studies\, sensor network localization\, and integrative genomics. We consider the problem of learning the relationship between a scale-valued response and a high-dimensional array-valued predictor. Traditional regression methods take a parametric procedure by imposing a priori functional form between variables. These parametric models\, however\, are inadequate for structure learning and often fail in accurate prediction. Here\, we develop a learning reduction framework to address a range of learning tasks from classification to regression for matrix-valued predictors. Our proposal achieves interpretable prediction via a low-rank two-way sparse halfspace learning for the target function level sets. Statistical accuracy\, excess risk bounds\, and efficient algorithms are established. We demonstrate the advantage of our method over previous approaches through applications to human brain connectome data. Time allowed\, I will present our recent results on nonparametric approaches to tensor estimation and completion. \n\n\n\n\n\nDate:\nWednesday\, February 03\, 2021\n\n\nStart Time:\n12 p.m. PST\n\n\nHost or Sponsor:\nBiostatistics Seminar Series\n\n\nLocation:\nZoom Webinar\n\nhttps://zoom.us/j/95933099692?pwd=ZTBvOWZCK01KTFRCVnFUSjdJeXF5UT09 \n\n\n\nSpeaker or Presenter:\nMiaoyan Wang\n\n\nContact Information:\nshirata@fredhutch.org
URL:https://scienceinseattle.com/event/biostatistics-seminar-series-nonparametric-array-learning-and-its-applications-in-neuroimaging-2/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20210203T120000
DTEND;TZID=America/Vancouver:20210203T130000
DTSTAMP:20260718T081803
CREATED:20210201T181305Z
LAST-MODIFIED:20210201T181305Z
UID:26628-1612353600-1612357200@scienceinseattle.com
SUMMARY:Biostatistics Seminar Series: “Nonparametric Array Learning and its Applications in Neuroimaging”
DESCRIPTION:The Fred Hutch Biostatistics Program hosts seminars featuring presentations by Hutch and outside scientists to share their latest developments and recent research. Each seminar includes an hour-long presentation and discussion during which speakers showcase their work and findings. \nThis seminar will be held on Zoom due to the COVID-19 pandemic. \n\n\n\n\n\n\n\n\n\n\n\n\n  \nBiostatistics Seminar Series:  \n“Nonparametric array learning and its applications in neuroimaging” \n\n\n\n\n\n\n\n\n\n\n\n\nArray-valued predictors ubiquitously arise in brain connectivity studies\, sensor network localization\, and integrative genomics. We consider the problem of learning the relationship between a scale-valued response and a high-dimensional array-valued predictor. Traditional regression methods take a parametric procedure by imposing a priori functional form between variables. These parametric models\, however\, are inadequate for structure learning and often fail in accurate prediction. Here\, we develop a learning reduction framework to address a range of learning tasks from classification to regression for matrix-valued predictors. Our proposal achieves interpretable prediction via a low-rank two-way sparse halfspace learning for the target function level sets. Statistical accuracy\, excess risk bounds\, and efficient algorithms are established. We demonstrate the advantage of our method over previous approaches through applications to human brain connectome data. Time allowed\, I will present our recent results on nonparametric approaches to tensor estimation and completion. \n\n\n\n\n\nDate:\nWednesday\, February 03\, 2021\n\n\nStart Time:\n12 p.m. PST\n\n\nHost or Sponsor:\nBiostatistics Seminar Series\n\n\nLocation:\nZoom Webinar\n\nhttps://zoom.us/j/95933099692?pwd=ZTBvOWZCK01KTFRCVnFUSjdJeXF5UT09 \n\n\n\nSpeaker or Presenter:\nMiaoyan Wang\n\n\nContact Information:\nshirata@fredhutch.org
URL:https://scienceinseattle.com/event/biostatistics-seminar-series-nonparametric-array-learning-and-its-applications-in-neuroimaging-3/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20210203T170000
DTEND;TZID=America/Vancouver:20210203T200000
DTSTAMP:20260718T081803
CREATED:20210125T194532Z
LAST-MODIFIED:20210125T194532Z
UID:11243-1612371600-1612382400@scienceinseattle.com
SUMMARY:Chiari Malformation Webinar
DESCRIPTION:Live webinar with physicians about Chiari Malformation
URL:https://scienceinseattle.com/event/chiari-malformation-webinar/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20210203T170000
DTEND;TZID=America/Vancouver:20210203T200000
DTSTAMP:20260718T081803
CREATED:20210125T194532Z
LAST-MODIFIED:20210125T194532Z
UID:25661-1612371600-1612382400@scienceinseattle.com
SUMMARY:Chiari Malformation Webinar
DESCRIPTION:Live webinar with physicians about Chiari Malformation
URL:https://scienceinseattle.com/event/chiari-malformation-webinar-2/
LOCATION:Online
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20210203T170000
DTEND;TZID=America/Vancouver:20210203T200000
DTSTAMP:20260718T081803
CREATED:20210125T194532Z
LAST-MODIFIED:20210125T194532Z
UID:26626-1612371600-1612382400@scienceinseattle.com
SUMMARY:Chiari Malformation Webinar
DESCRIPTION:Live webinar with physicians about Chiari Malformation
URL:https://scienceinseattle.com/event/chiari-malformation-webinar-3/
LOCATION:Online
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