Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift

Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, and Mihaela van der Schaar

International Conference on Machine Learning (ICML) 2020

Last Updated: 11 March 2021

Code Author: Alex J. Chan ([email protected])

An implementation of a transductive dropout network class can be found in models.py and a walkthrough of its use in an example regression problem is provided in tutorial.ipynb

This repo is pip installable - clone it, optionally create a virtual env, and install it (this will automatically install dependencies):

git clone https://github.com/XanderJC/transductive-dropout.git

cd transductive-dropout

pip install -e .

Example usage:

from TD import transductive

# Get data somehow

X,y,unlabelled_X = get_data()

# Instantiate transductive dropout model

model = transductive([1,32,64,1], d_units=8)

# Train the model
model.train(X, y, unlabelled_X, iters=1000)

Citing

If you use this software please cite as follows:

@inproceedings{chan2020unlabelled,
  title={Unlabelled data improves {B}ayesian uncertainty calibration under covariate shift},
  author={Alex James Chan and Ahmed Alaa and Zhaozhi Qian and Mihaela van der Schaar},
  booktitle={International Conference on Machine Learning},
  year={2020}
}