Algorithms for abstention, calibration and domain adaptation to label shift.

Associated papers:

Shrikumar A*†, Alexandari A*, Kundaje A†, A Flexible and Adaptive Framework for Abstention Under Class Imbalance

Alexandari A*, Kundaje A†, Shrikumar A*†, Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

*co-first authors † co-corresponding authors

See https://github.com/blindauth/abstention_experiments and https://github.com/blindauth/labelshiftexperiments for colab notebooks reproducing the experiments in the papers.

pip install abstention

For calibration:

• Platt Scaling
• Isotonic Regression
• Temperature Scaling
• Vector Scaling
• Bias-Corrected Temperature Scaling
• No-Bias Vector Scaling

For domain adaptation to label shift:

• Expectation Maximization (Saerens et al., 2002)
• Black-Box Shift Learning (BBSL) (Lipton et al., 2018)
• Regularized Learning under Label Shifts (RLLS) (Azizzadenesheli et al., 2019)

For abstention:

• Metric-specific abstention methods described in A Flexible and Adaptive Framework for Abstention Under Class Imbalance, including abstention to optimize auROC, auPRC, sensitivity at a target specificity and weighted Cohen's Kappa
• Jensen-Shannon Divergence from class priors
• Entropy in the predicted class probabilities (Wan, 1990)
• Probability of the highest-predicted class (Hendrycks & Gimpel, 2016)
• The method of Fumera et al., 2000
• See Colab notebook experiments in https://github.com/blindauth/abstention_experiments for details on how to use the various abstention methods.

If you have any questions, please contact:

Avanti Shrikumar: avanti [dot] shrikumar [at] gmail.com

Amr Alexandari: amr [dot] alexandari [at] gmail.com

Anshul Kundaje: akundaje [at] stanford [dot] edu