Density Uncertainty Layers

This repository contains the PyTorch implementation for the paper "Density Uncertainty Layers for Reliable Uncertainty Estimation" published in AISTATS 2024.

Requirements

The code is implemented using PyTorch 1.12.1.

To install the required packages (other than PyTorch)

pip install -r requirements.txt

Running Experiments

To train Rank1 Density Uncertainty Layers WRN28 on CIFAR-10/100,

python run_cifar.py --dataset={cifar10/cifar100} --model=rank1_density_wrn28

To train other models, simply replace the model argument with one of

density_wrn28, bayesian_wrn28, mcdropout_wrn28, vdropout_wrn28, rank1_wrn28

For UCI benchmarks,

python run_uci.py --model={density_mlp/bayesian_mlp/mcdropout_mlp/vdropout_mlp/rank1_mlp}

Citation

@inproceedings{park2024,
  title={Density Uncertainty Layers for Reliable Uncertainty Estimation},
  author={Park, Yookoon and Blei, David},
  booktitle={AISTATS},
  year={2024}
}