Theoretical Foundations for Learning from Easy Data

7 - 11 November 2016

Venue: Lorentz Center@Snellius

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Description and aim

There exist a plethora of conditions (such as margin conditions in classification, exp-concavity of the losses in sequence prediction and perturbation robustness for clustering) under which learning becomes easier than in the worst-case.  This workshop investigates how reasonable such conditions really are, and aims to further develop algorithms that simultaneously exploit easy situations while still being close to worst-case optimal.

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