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| -rw-r--r-- | README.md | 7 | ||||
| -rw-r--r-- | ot/da.py | 6 |
2 files changed, 11 insertions, 2 deletions
@@ -20,7 +20,7 @@ It provides the following solvers: * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] +* Optimal transport for domain adaptation with group lasso and Laplacian regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Linear OT [14] and Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). @@ -183,6 +183,7 @@ The contributors to this library are * [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) * [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) * [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) +* [Ievgen Redko](https://ievred.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -259,4 +260,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019.
\ No newline at end of file +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. + +[28] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. @@ -818,6 +818,9 @@ def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [28] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, + "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching," + in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. See Also -------- @@ -1729,6 +1732,9 @@ class EMDLaplaceTransport(BaseTransport): .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [2] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, + "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching," + in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. """ def __init__(self, reg_lap=1., reg_src=1., alpha=0.5, |