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)
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