Web: http://arxiv.org/abs/2207.14514

Sept. 19, 2022, 1:13 a.m. | Dirk Tasche

stat.ML updates on arXiv.org arxiv.org

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 dataset by a method called Joint Importance Aligning.
For the multinomial (multiclass) classification setting, we derive a
representation of factorizable joint shift in terms of the source (training)
distribution, the target (test) prior class probabilities and the target
marginal distribution of the features. On the basis of this result, we propose
alternatives …

arxiv classification multinomial shift

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