Talks
Spring 2022
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Learning Uninformative Representations
Friday, February 25th, 2022, 9:00 am–9:45 am
Speaker:
Richard Zemel (Columbia University)
Location:
Calvin Lab Auditorium
In many learning scenarios an important aim is to learn a representation that does not carry any information about some feature of the input. For privacy reasons, the identity of an input datum should be obfuscated; for fairness considerations, surfacing particular attributes may be undesirable; and disentanglement entails separating the learned subspaces of the representation. In this talk I will discuss methods for accomplishing this, and the degree to which they have been successful.
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