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DTEND:20200108T120000Z
UID:ad275f6ebb9f16615e209867bc3e904a-39
DTSTAMP:19700101T120016Z
DESCRIPTION:MACHINE LEARNING MODELS:FROM BIRTH TO SERVING THE REAL-WORLD
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/39/machine-learning-modelsfrom-birth-to-serving-the-real-world/
SUMMARY:A Machine Learning (ML) Model is born by tting a function around examples sampled from an
unknown distribution, and is designed to generalize to other examples drawn from the same
distribution. The machine learning edice largely rests on this foundation. However, modern ML
models trained for challenging tasks like object detection, speech recognition, and language
understanding require huge amounts of labeled data and leave a large carbon footprint. In return,
a model once trained needs to serve many diverse real-world settings that do not perfectly match
its birth setting. In this talk, I will discuss current research on propping the ML edice against such
shifting foundation. We will span over current props like calibration, out-of-sample detection,
domain adaptation, domain generalization, and robustness and reect on some futuristic topics.
DTSTART:20200108T120000Z
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