talwagner / efficient_kdeLinks
Efficient LSH-based kernel density estimation
☆29Updated 5 years ago
Alternatives and similar repositories for efficient_kde
Users that are interested in efficient_kde are comparing it to the libraries listed below
Sorting:
- Coresets☆37Updated 3 years ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆29Updated 6 years ago
- Python implementation of smooth optimal transport.☆59Updated 4 years ago
- Keras implementation of Deep Wasserstein Embeddings☆48Updated 7 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆104Updated 5 years ago
- Code for Sliced Gromov-Wasserstein☆68Updated 5 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.☆73Updated 8 years ago
- MATLAB implementation of linear support vector classification in hyperbolic space☆20Updated 7 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago
- Non-Parametric Calibration for Classification (AISTATS 2020)☆19Updated 3 years ago
- python implementation of Sinkhorn-Knopp☆33Updated 7 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆50Updated 4 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- Spatio-temporal alignements: Optimal transport in space and time☆47Updated 2 months ago
- ☆50Updated 2 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆45Updated 2 years ago
- Sliced Wasserstein Distance for Learning Gaussian Mixture Models☆63Updated 2 years ago
- Matlab code for tree-Wasserstein distance in the paper "Tree-Sliced Variants of Wasserstein Distances", NeurIPS, 2019. (Tam Le, Makoto Y…☆11Updated 5 years ago
- Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…☆22Updated 5 years ago
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆56Updated 6 years ago
- Stochastic algorithms for computing Regularized Optimal Transport☆57Updated 6 years ago
- ☆12Updated 6 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆56Updated 3 years ago
- A Python implementation of Monge optimal transportation☆49Updated last year
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 5 years ago
- Implementation of the Multiscale Laplacian Graph Kernel☆19Updated 5 years ago
- Distributional Shapley: A Distributional Framework for Data Valuation☆30Updated last year
- Deep convolutional gaussian processes.☆79Updated 5 years ago