LULA --- Learnable Uncertainty under Laplace Approximations

Companion code for the paper "Learnable Uncertainty under Laplace Approximations" (UAI 2021).

• ArXiv version: https://arxiv.org/abs/2010.02720.
• Citation:

@inproceedings{kristiadi2021learnable,
  title={Learnable uncertainty under {L}aplace approximations},
  author={Kristiadi, Agustinus and Hein, Matthias and Hennig, Philipp},
  booktitle={UAI},
  year={2021},
}

LULA Implementation

The source code for LULA is in the lula directory

• lula/model contains classes for augmenting a MAP-trained network with LULA units
• lula/train contains methods for specifically training the associated parameters of LULA units
• lula/util contains useful utilities, both for the construction and training of LULA

Reproducing the Paper

To reproduce all experimental results in the paper, first run:

./train.sh
./repeat.sh

The model files will be stored in pretrained_models directory, while the raw experimental results will be in results (the results used in the paper are already in there).

Then, to obtain the plots/tables used in the paper, run

python plot_MNISTC.py  # rotated-MNIST
python plot_CIFAR10C.py
python table_calib.py  # calibration, i.e. accuracy and ECE
python table_OOD.py --metrics mmc_fpr95

License

MIT License

Copyright (c) 2021 Agustinus Kristiadi

Permission is hereby granted, free of charge, to any person obtaining a copy
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