Web: http://arxiv.org/abs/1708.04970

June 16, 2022, 1:11 a.m. | Daniel Ting

cs.LG updates on arXiv.org arxiv.org

Sampling is a fundamental problem in computer science and statistics.
However, for a given task and stream, it is often not possible to choose good
sampling probabilities in advance. We derive a general framework for adaptively
changing the sampling probabilities via a collection of thresholds.In general,
adaptive sampling procedures introduce dependence amongst the sampled points,
making it difficult to compute expectations and ensure estimators are unbiased
or consistent. Our framework address this issue and further shows when adaptive
thresholds can …

arxiv ml sampling threshold

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY