=Paper=
{{Paper
|id=Vol-3052/keynote1
|storemode=property
|title=Mixture Proportion Estimation in Weakly Supervised Learning
|pdfUrl=https://ceur-ws.org/Vol-3052/keynote1.pdf
|volume=Vol-3052
|authors=Masashi Sugiyama
|dblpUrl=https://dblp.org/rec/conf/cikm/Sugiyama21
}}
==Mixture Proportion Estimation in Weakly Supervised Learning==
<pdf width="1500px">https://ceur-ws.org/Vol-3052/keynote1.pdf</pdf>
<pre>
Mixture Proportion Estimation
in Weakly Supervised Learning
Masashi Sugiyama
RIKEN and the University of Tokyo, Japan


                                          Abstract
                                          Estimation of mixing coefficients in a mixture distribution has a variety of applications in machine learning. For example,
                                          under class prior shift, estimation of class priors from labeled training data and unlabeled test data plays an essential role in
                                          adaptation; for enabling positive-unlabeled classification, class prior estimation only from positive and unlabeled data is a key
                                          challenge; and to cancel the bias caused by label noise, estimation of the noise transition is a central task. In this talk, I will
                                          give an overview of our advances in mixture proportion estimation and their use in various machine learning tasks.




LQ 2021: 1st International Workshop on Learning to Quantify, Gold
Coast, AU, November 1 and November 5, 2021.
$ sugi@k.u-tokyo.ac.jp (M. Sugiyama)
                                    © 2021 Copyright for this paper by its authors. Use permitted under Creative
                                    Commons License Attribution 4.0 International (CC BY 4.0).
 CEUR
 Workshop
 Proceedings
               http://ceur-ws.org
               ISSN 1613-0073       CEUR Workshop Proceedings (CEUR-WS.org)

</pre>