Web: http://arxiv.org/abs/2201.04038

Jan. 12, 2022, 2:10 a.m. | Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian

cs.LG updates on arXiv.org arxiv.org

In many real-world scenarios, we often deal with streaming data that is
sequentially collected over time. Due to the non-stationary nature of the
environment, the streaming data distribution may change in unpredictable ways,
which is known as concept drift. To handle concept drift, previous methods
first detect when/where the concept drift happens and then adapt models to fit
the distribution of the latest data. However, there are still many cases that
some underlying factors of environment evolution are predictable, making it
possible to model the future concept drift trend of …

arxiv data distribution for

Statistics and Computer Science Specialist

@ Hawk-Research | Remote

Data Scientist, Credit/Fraud Strategy

@ Fora Financial | New York City

Postdoctoral Research Associate - Biomedical Natural Language Processing and Deep Learning

@ Oak Ridge National Laboratory - Oak Ridge, TN | Oak Ridge, TN, United States

Senior Machine Learning / Computer Vision Engineer

@ Glass Imaging | Los Altos, CA

Research Scientist in Biomedical Natural Language Processing and Deep Learning

@ Oak Ridge National Laboratory | Oak Ridge, TN

W3-Professorship for Intelligent Energy Management

@ Universität Bayreuth | Bayreuth, Germany