all AI news
Minimax optimal approaches to the label shift problem in non-parametric settings. (arXiv:2003.10443v3 [math.ST] UPDATED)
Nov. 24, 2022, 7:14 a.m. | Subha Maity, Yuekai Sun, Moulinath Banerjee
stat.ML updates on arXiv.org arxiv.org
We study the minimax rates of the label shift problem in non-parametric
classification. In addition to the unsupervised setting in which the learner
only has access to unlabeled examples from the target domain, we also consider
the setting in which a small number of labeled examples from the target domain
is available to the learner. Our study reveals a difference in the difficulty
of the label shift problem in the two settings, and we attribute this
difference to the availability …
More from arxiv.org / stat.ML updates on arXiv.org
Inexact subgradient methods for semialgebraic functions
1 day, 20 hours ago |
arxiv.org
Online and Offline Robust Multivariate Linear Regression
1 day, 20 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Business Data Analyst
@ Alstom | Johannesburg, GT, ZA