Invited Talk
in
Workshop: I Can’t Believe It’s Not Better (ICBINB): Failure Modes in the Age of Foundation Models
Active and Online Learning with Large (and Combinatorial) Models
Abstract:
Active learning consists in sequentially and adaptively constructing a data-set in the hope of improving the learning speed by avoiding useless data-points where the current model is already correct with large probability and by focusing on uncertainty regions. During this talk, I will give a short reminder on the potential benefits and pitfalls of active learning, especially in large and combinatorial models.
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