University of Cambridge > Talks.cam > Language Technology Lab Seminars > Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information

Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Marinela Parovic.

We describe a simple and effective method (Spectral Attribute removaL; SAL ) to remove guarded information from neural representations. Our method uses singular value decomposition and eigenvalue decomposition to project the input representations into directions with reduced covariance with the guarded information rather than maximal covariance, as normally, these factorization methods are used. We begin with linear information removal and proceed to generalize our algorithm to the case of nonlinear information removal using kernels. Our experiments demonstrate that our algorithm retains better main task performance after removing the guarded information compared to previous methods. In addition, our experiments demonstrate that we need a relatively small amount of guarded attribute data to remove information about these attributes, which lowers the exposure to such possibly sensitive data and fits better low-resource scenarios.

This talk is part of the Language Technology Lab Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity