University of Cambridge > Talks.cam > Statistics > On the sample complexity of multi-objective learning

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

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

ℹī¸ Information

On the sample complexity of multi-objective learning

Download to your calendar using vCal

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

In multi-objective learning (MOL), several possibly competing prediction tasks must be solved jointly by a single model. Achieving good trade-offs may require a model class G with larger capacity than what is necessary for solving the individual tasks. This, in turn, increases the statistical cost, as reflected in known MOL bounds that depend on the complexity of G. We show that this cost is unavoidable for some losses, even in an idealized semi-supervised setting, where the learner has access to the Bayes-optimal solutions for the individual tasks as well as the marginal distributions over the covariates. On the other hand, for objectives defined with Bregman losses, we prove that the complexity of G may come into play only in terms of unlabeled data. Concretely, we establish sample complexity upper bounds, showing precisely when and how unlabeled data can significantly alleviate the need for labeled data. These rates are achieved by a simple, semi-supervised algorithm via pseudo-labeling.

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

CIMR, The Keith Peters Building, The Level 7 Lecture Theatre BAS Seminar Room 2; https://ukri.zoom.us/j/96469833792 Rdxhd

Other talks

Title to be confirmed Group Work Spectral rigidity of random covers of compact hyperbolic surfaces Evolution: Inevitable or Remarkable? Caribbean Trip and Cacti Sheref Mansy, topic TBA
 

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