Code

Sparse Approximation by the Generalized Soft-Min Penalty

Solver for the sparse approximation problem, based on the Generalized Soft-Min (GSM) penalty.

Part of our paper titled “The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty.”
Matlab C

Fourier Sliced-Wasserstein Embedding

Efficient implementation of the FSW embedding — a Euclidean embedding for multisets and measures, which is bi-Lipschitz on multisets.

Part of our paper titled “Fourier Sliced-Wasserstein Embedding for Multisets and Measures”
PyTorch

Fourier Sliced-Wasserstein Graph Neural Network (FSW-GNN)

A 1-WL poweful graph neural network for graphs with multidimensional vertex and edge features.

• When randomly initialized, this GNN computes a bi-Lipschitz Euclidean embedding for such graphs.
• Supports continuous edge weights while maintaining 1-WL separation (but not bi-Lipschitzness, which is provably impossible).

Part of our paper titled “FSW-GNN: Bi-Lipschitz Euclidean Embedding for Graphs”
PyTorch Geometric