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

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

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

Sharp bounds for compressive learning

Add to your list(s) 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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

cuscrs Irregular seminars in TCM European Research Seminar Series 2015-2016 CPERG: Cambridge Peace and Education Research Group Organismal Proteostasis: Molecular Strategies for Proteome Protection in Health and Disease CUID

Other talks

The evolution of photosynthetic efficiency Mass Spectrometry Solving the Reproducibility Crisis Pain and physiological processes in sixteenth-century medical texts from Mexico and Spain 'Cambridge University, Past and Present' Coin Betting for Backprop without Learning Rates and More DataFlow SuperComputing for BigData Constructing the virtual fundamental cycle Discovering regulators of insulin output with flies and human islets: implications for diabetes and pancreas cancer Nuclear fuel manufacture at Westinghouse Springfields past, present and future
 

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