University of Cambridge > Talks.cam > Statistics > Sharp bounds for compressive learning

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Sharp bounds for compressive learning

Download to your calendar using vCal

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

The first part of the talk will derive sharp bounds on the generalization error of a generic linear classifier trained by empirical risk minimization on randomly-projected data. We make no restrictive assumptions on the data—such as sparsity, separability, or distributional assumptions. Instead, by using elementary techniques, we derive the exact probability of label flipping under Gaussian random projection and use this to bound the effect of random projection on the generalisation error of the compressive classifier. The second part of the talk will present an analogous strategy for regression. This provides a new analysis of the excess risk of compressive linear least squares, which removes a spurious log(N) factor from previous bounds (where N is the number of training points). In addition to tightness, these new bounds have a clear interpretation and reveal meaningful structural properties of the learning problem that make them solvable effectively in a small dimensional random subspace.

This talk is part of the Statistics series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Cambridge University Engineering Department, Board Room Average on weekdays: 9225.16377315 kWh TCM Seminar Room - Cavendish Laboratory & on Zoom: tinyurl.com/domzum

Other talks

Optimal metric and approximation of free boundary minimal surfaces Talk TBC A Few Days in Patagonia - the Deep South Afternoon Break TBC Title to be confirmed
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity