Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.
We study a novel variant of the domain adaptation problem, in which the loss function on test data changes due to dependencies on prior predictions. One important instance of this problem area occurs in settings where it is more costly to make a new error than to repeat a previous error. We propose several methods for learning effectively in this setting, and test them empirically on the real-world tasks of malicious URL classification and adversarial advertisement detection.
Questions and AnswersYou need to be logged in to be able to post here.