AISTATS
|
2022

Learning from Multiple Noisy Partial Labelers

P. Yu, et al

Abstract

This work enables users to create partial labelers that output subsets of possible class labels would greatly expand the expressivity of programmatic weak supervision.

Share this article
Coming Fall 2026
ImageImage
A one-day, invite-only summit providing a first look at the benchmarks and research that will shape the frontier. Sign up for updates.