Attribute Efficient Linear Regression with Distribution-Dependent Sampling
published: Sept. 27, 2015, recorded: July 2015, views: 1835
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We consider a budgeted learning setting, where the learner can only choose and observe a small subset of the attributes of each training example. We develop efficient algorithms for Ridge and Lasso linear regression, which utilize the geometry of the data by a novel distribution-dependent sampling scheme, and have excess risk bounds which are better a factor of up to O(d/k) over the state-of-the-art, where d is the dimension and k+1 is the number of observed attributes per example. Moreover, under reasonable assumptions, our algorithms are the first in our setting which can provably use *less* attributes than full-information algorithms, which is the main concern in budgeted learning. We complement our theoretical analysis with experiments which support our claims.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !