BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv7.32.0//EN
X-ORIGINAL-URL:https://dcn.nat.fau.eu/
X-WR-CALNAME:
X-WR-CALDESC:FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T030000
RRULE:FREQ=YEARLY;BYMONTH=03;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=4SU
END:STANDARD
END:VTIMEZONE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-d01f770d77f135a0762a9b8643607b39@dcn.nat.fau.eu
DTSTART;TZID=Europe/Berlin:20201109T080000
DTEND;TZID=Europe/Berlin:20201113T180000
DTSTAMP:20211020T084750Z
CREATED:20211020
LAST-MODIFIED:20220117
PRIORITY:5
SEQUENCE:0
TRANSP:OPAQUE
SUMMARY:Optimal Control, Optimal Transport and Data Science
DESCRIPTION:IMA – Institute for Mathematics and its Applications with Carnegie Mellon University and IBM organizes the workshop: Optimal Control, Optimal Transport and Data Science\nOptimal control and optimal transportation have begun to play an important role in data science. Many techniques of machine learning, including deep learning, high-dimensional statistical learning, transfer learning, anomaly detection, and prediction from expert advice, rely on optimal transport and optimal control to model tasks, compute solutions, and provide theoretical analysis of the algorithms. Optimal transport has been successfully used as a loss function in both theory and practice to provide robust learning approaches. Furthermore, Wasserstein Generative Adversarial Networks (WGAN) have achieved state-of-the-art results for generative learning problems. In deep learning, optimal control of ordinary differential equations has been connected recently with the stability of ResNet neural networks. Reformulation training as an optimal control problem has led to new training algorithms based on Pontryagin’s maximum principle. Furthermore, minimax optimal strategies for minimizing regret have been connected to continuum Hamilton-Jacobi-Isaacs partial differential equations (PDEs). Despite this flurry of work and recent success, many theoretical and computational challenges remain, such as fast computation of optimal transportation distances and maps.\nApply to the IMA workshop\nxx\n
URL:https://dcn.nat.fau.eu/events/optimal-control-optimal-transport-and-data-science/
CATEGORIES:Workshop
ATTACH;FMTTYPE=image/jpeg:https://dcn.nat.fau.eu/wp-content/uploads/workshop-1742721_1920.jpg
END:VEVENT
END:VCALENDAR