Label Shift Estimation for Class-Imbalance Problem: A Bayesian Approach

Author:

Ye Changkun1,Tsuchida Russell2,Petersson Lars2,Barnes Nick1

Affiliation:

1. Australian National University,Canberra ACT,Australia

2. Data61 CSIRO,Acton ACT,Australia

Publisher

IEEE

Reference44 articles.

1. Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation;Alexandari

2. Regularized learning for domain adaptation under label shifts;Azizzadenesheli

3. A conceptual introduction to hamiltonian monte carlo;Betancourt,2017

4. Pyro: Deep Universal Probabilistic Programming;Bingham;Journal of Machine Learning Research,2018

5. A systematic study of the class imbalance problem in convolutional neural networks

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