78 citations · 341 across the 31 of their papers we have counts for
37 papers
Sparse joint shift in multinomial classification
Dirk Tasche
Sparse joint shift (SJS) was recently proposed as a tractable model for general dataset shift which may cause changes to the marginal distributions of features and labels as well a…
Factorizable Joint Shift in Multinomial Classification
Dirk Tasche
Factorizable joint shift (FJS) was recently proposed as a type of dataset shift for which the complete characteristics can be estimated from feature data observations on the test d…
Class Prior Estimation under Covariate Shift: No Problem?
Dirk Tasche
We show that in the context of classification the property of source and target distributions to be related by covariate shift may be lost if the information content captured in th…
Minimising quantifier variance under prior probability shift
Dirk Tasche
For the binary prevalence quantification problem under prior probability shift, we determine the asymptotic variance of the maximum likelihood estimator. We find that it is a funct…
Calibrating sufficiently
Dirk Tasche
When probabilistic classifiers are trained and calibrated, the so-called grouping loss component of the calibration loss can easily be overlooked. Grouping loss refers to the gap b…
Proving prediction prudence
Dirk Tasche
We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent. Prudence requires that that the mea…