JasonAltschuler / OptimalTransportNIPS17
Code for NIPS 2017 spotlight paper: "Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration" by Jason Altschuler, Jonathan Weed, and Philippe Rigollet. Full paper available at: https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.
☆30Updated 6 years ago
Related projects ⓘ
Alternatives and complementary repositories for OptimalTransportNIPS17
- Stochastic algorithms for computing Regularized Optimal Transport☆55Updated 6 years ago
- Python implementation of smooth optimal transport.☆56Updated 3 years ago
- Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…☆21Updated 4 years ago
- Implementation of Inexact Proximal point method for Optimal Transport☆45Updated 3 years ago
- demo codes for several approximate optimal transport solvers☆20Updated 6 years ago
- L. Chizat, G. Peyré, B. Schmitzer, F-X. Vialard. Scaling Algorithms for Unbalanced Transport Problems. Preprint Arxiv:1607.05816, 2016.☆42Updated 7 years ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆28Updated 5 years ago
- A Python implementation of Monge optimal transportation☆49Updated last year
- ☆12Updated 6 years ago
- Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.☆73Updated 8 years ago
- Code for http://proceedings.mlr.press/v80/dvurechensky18a.html☆15Updated 6 years ago
- Learning Autoencoders with Relational Regularization☆44Updated 4 years ago
- Code for Sliced Gromov-Wasserstein☆66Updated 4 years ago
- Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…☆20Updated 5 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆26Updated 4 years ago
- Sliced Wasserstein Distance for Learning Gaussian Mixture Models☆58Updated last year
- The code for Differentiable Linearized ADMM (ICML 2019)☆33Updated 5 years ago
- Learning generative models with Sinkhorn Loss☆28Updated 6 years ago
- Sinkhorn Barycenters via Frank-Wolfe algorithm☆24Updated 4 years ago
- Efficient Wasserstein Barycenter in MATLAB (for "Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support" …☆25Updated 5 years ago
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆54Updated 5 years ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆53Updated 3 years ago
- Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)☆19Updated 6 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆71Updated 5 years ago
- J-D. Benamou, G. Carlier, M. Cuturi, L. Nenna, G. Peyré. Iterative Bregman Projections for Regularized Transportation Problems. SIAM Jour…☆32Updated 7 years ago
- A collection of adaptive sparse multi-scale solvers for optimal transport and related optimization problems.☆53Updated 3 years ago
- ☆13Updated 5 years ago
- LEARNING LATENT PERMUTATIONS WITH GUMBEL-SINKHORN NETWORKS IMPLEMENTATION WITH PYTORCH☆77Updated last year
- It is a repo which allows to compute all divergences derived from the theory of entropically regularized, unbalanced optimal transport. I…☆28Updated last year
- ☆13Updated 4 years ago