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The sample complexity of agnostic learning under deterministic labels
Published on Jul 15, 20143250 Views
With the emergence of Machine Learning tools that allow handling data with a huge number of features, it becomes reasonable to assume that, over the full set of features, the true labeling is (almost)
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Chapter list
The sample complexity of agnostic learning under deterministic labels00:00
A non-investigated corner in (agnostic) PAC theory00:10
Why care?01:45
Results: Noise in labels is not the only source of diculty02:40
Results: Learning rates vary with the class03:15
Results: ERM not always optimal04:13
Results: Unlabeled data is useful04:54
Summary05:30
See you at the poster!06:11