ermongroup / subsets

Code for Reparameterizable Subset Sampling via Continuous Relaxations, IJCAI 2019.

Reparameterizable Subset Sampling via Continuous Relaxations

This repo contains the code for the paper Reparameterizable Subset Sampling via Continuous Relaxations, which allows you to include subset sampling as a layer in a neural network. This is useful whenever you want to select a discrete number of elements, such as in dynamic feature selection or k-nearest neighbors. This repo contains the experiments for learning feature selectors for explainability, training a deep stochastic k-NN model, and training a parametric t-SNE model using subset sampling.

Supports the following libraries:

• PyTorch (SubsetOperator in subsets/knn/sorting_operator.py)
• TensorFlow (sample_subset in subsets/sample_subsets.py)

To setup, please create a new Python virtualenv with Python 3.6, activate it, navigate to this directory (containing setup.py) and run pip install -e .

To run the experiments, navigate to the experiments/ folder and run the corresponding scripts.

If you find this code useful, please cite

@article{xie2019subsets,
  author    = {Sang Michael Xie and Stefano Ermon},
  title     = {Reparameterizable Subset Sampling via Continuous Relaxations},
  journal   = {International Joint Conference on Artificial Intelligence (IJCAI)},
  year      = {2019}
}