Sampled Gromov Wasserstein
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[1] Gershon Wolansky,et al. Optimal Transport , 2021 .
[2] Nicolas Courty,et al. CO-Optimal Transport , 2020, NeurIPS.
[3] Hisashi Kashima,et al. Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces , 2020, ArXiv.
[4] Marco Cuturi,et al. Differentiable Ranks and Sorting using Optimal Transport , 2019, 1905.11885.
[5] Nicolas Courty,et al. Sliced Gromov-Wasserstein , 2019, NeurIPS.
[6] Lawrence Carin,et al. Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching , 2019, NeurIPS.
[7] Stefanie Jegelka,et al. Learning Generative Models across Incomparable Spaces , 2019, ICML.
[8] S. Caracciolo,et al. The Dyck bound in the concave 1-dimensional random assignment model , 2019, Journal of Physics A: Mathematical and Theoretical.
[9] Marco Cuturi,et al. Subspace Robust Wasserstein distances , 2019, ICML.
[10] Hongyuan Zha,et al. Gromov-Wasserstein Learning for Graph Matching and Node Embedding , 2019, ICML.
[11] L. Freeman,et al. Social Networks , 2022, Handbook of Graph Drawing and Visualization.
[12] Nicolas Courty,et al. Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties , 2018, Algorithms.
[13] Gabriel Peyré,et al. Sample Complexity of Sinkhorn Divergences , 2018, AISTATS.
[14] Samir Chowdhury,et al. The Gromov-Wasserstein distance between networks and stable network invariants , 2018, Information and Inference: A Journal of the IMA.
[15] Wen Li,et al. Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation , 2018, IJCAI.
[16] Nicolas Courty,et al. Optimal Transport for structured data with application on graphs , 2018, ICML.
[17] Nicolas Courty,et al. Optimal Transport for structured data , 2018, ArXiv.
[18] Hongyuan Zha,et al. A Fast Proximal Point Method for Computing Exact Wasserstein Distance , 2018, UAI.
[19] Vivien Seguy,et al. Smooth and Sparse Optimal Transport , 2017, AISTATS.
[20] Vladimir G. Kim,et al. GWCNN: A Metric Alignment Layer for Deep Shape Analysis , 2017, Comput. Graph. Forum.
[21] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[22] Prabhu Babu,et al. Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning , 2017, IEEE Transactions on Signal Processing.
[23] Alexander J. Smola,et al. Stochastic Frank-Wolfe methods for nonconvex optimization , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[24] Vladimir G. Kim,et al. Entropic metric alignment for correspondence problems , 2016, ACM Trans. Graph..
[25] Gabriel Peyré,et al. Gromov-Wasserstein Averaging of Kernel and Distance Matrices , 2016, ICML.
[26] Nicolas Courty,et al. Domain Adaptation with Regularized Optimal Transport , 2014, ECML/PKDD.
[27] Roman Garnett,et al. Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping , 2013, MLG 2013.
[28] Marco Cuturi,et al. Sinkhorn Distances: Lightspeed Computation of Optimal Transport , 2013, NIPS.
[29] Julien Rabin,et al. Wasserstein regularization of imaging problem , 2011, 2011 18th IEEE International Conference on Image Processing.
[30] Wolfgang Heidrich,et al. Displacement interpolation using Lagrangian mass transport , 2011, ACM Trans. Graph..
[31] Facundo Mémoli,et al. Gromov–Wasserstein Distances and the Metric Approach to Object Matching , 2011, Found. Comput. Math..
[32] Julie Delon,et al. Local Matching Indicators for Transport Problems with Concave Costs , 2011, SIAM J. Discret. Math..
[33] Guillermo Sapiro,et al. A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching , 2010, International Journal of Computer Vision.
[34] Michael Werman,et al. Fast and robust Earth Mover's Distances , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[35] Facundo Mémoli,et al. Spectral Gromov-Wasserstein distances for shape matching , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.
[36] C. Villani. Optimal Transport: Old and New , 2008 .
[37] Ulrik Brandes,et al. Experiments on Graph Clustering Algorithms , 2003, ESA.
[38] Alan L. Yuille,et al. Convergence Properties of the Softassign Quadratic Assignment Algorithm , 1999, Neural Computation.
[39] Kathryn B. Laskey,et al. Stochastic blockmodels: First steps , 1983 .
[40] Philip Wolfe,et al. An algorithm for quadratic programming , 1956 .
[41] Margaret H. Wright,et al. Direct search methods: Once scorned, now respectable , 1996 .
[42] T. Koopmans,et al. Assignment Problems and the Location of Economic Activities , 1957 .
[43] Facundo Mémoli,et al. Eurographics Symposium on Point-based Graphics (2007) on the Use of Gromov-hausdorff Distances for Shape Comparison , 2022 .
[44] A Fast Proximal Point Method for Computing Exact Wasserstein Distance – Appendix A More Analysis on IPOT , 2022 .
[45] N. Mitra,et al. Eurographics Symposium on Geometry Processing (2005) Robust Global Registration , 2022 .