On Robust Optimal Transport: Computational Complexity and Barycenter Computation
暂无分享,去创建一个
Huy L. Nguyen | Nhat Ho | Hung Bui | Huy Nguyen | Khang Le | Tung Pham | Quang Nguyen | Nhat Ho | H. Bui | Tung Pham | Khang Le | Q. Nguyen
[1] Nhat Ho,et al. On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm , 2020, ICML.
[2] Aaron Sidford,et al. Towards Optimal Running Times for Optimal Transport , 2018, ArXiv.
[3] Michael I. Jordan,et al. On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms , 2019, ICML.
[4] Julien Rabin,et al. Adaptive color transfer with relaxed optimal transport , 2014, 2014 IEEE International Conference on Image Processing (ICIP).
[5] Dinh Q. Phung,et al. Multilevel Clustering via Wasserstein Means , 2017, ICML.
[6] Nicolas Courty,et al. Optimal Transport for Domain Adaptation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Bernhard Schölkopf,et al. Wasserstein Auto-Encoders , 2017, ICLR.
[8] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[9] Stephen Boyd,et al. A Rewriting System for Convex Optimization Problems , 2017, ArXiv.
[10] Alexander Gasnikov,et al. Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm , 2018, ICML.
[11] Lawrence Carin,et al. Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching , 2019, NeurIPS.
[12] Marco Cuturi,et al. Sinkhorn Distances: Lightspeed Computation of Optimal Transport , 2013, NIPS.
[13] Nicolas Courty,et al. CO-Optimal Transport , 2020, NeurIPS.
[14] P. Rigollet,et al. Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming , 2017, bioRxiv.
[15] David B. Dunson,et al. Scalable Bayes via Barycenter in Wasserstein Space , 2015, J. Mach. Learn. Res..
[16] Darina Dvinskikh,et al. On the Complexity of Approximating Wasserstein Barycenters , 2019, ICML.
[17] Jason Altschuler,et al. Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration , 2017, NIPS.
[18] Alessandro Rudi,et al. Massively scalable Sinkhorn distances via the Nyström method , 2018, NeurIPS.
[19] Michael I. Jordan,et al. Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm , 2020, NeurIPS.
[20] Nathaniel Lahn,et al. A Graph Theoretic Additive Approximation of Optimal Transport , 2019, NeurIPS.
[21] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[22] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[23] Nicolas Courty,et al. Mapping Estimation for Discrete Optimal Transport , 2016, NIPS.
[24] Gabriel Peyré,et al. Fast Dictionary Learning with a Smoothed Wasserstein Loss , 2016, AISTATS.
[25] Volkan Cevher,et al. WASP: Scalable Bayes via barycenters of subset posteriors , 2015, AISTATS.
[26] X. Nguyen. Convergence of latent mixing measures in finite and infinite mixture models , 2011, 1109.3250.
[27] Yu Cheng,et al. Graph Optimal Transport for Cross-Domain Alignment , 2020, ICML.
[28] François-Xavier Vialard,et al. Scaling algorithms for unbalanced optimal transport problems , 2017, Math. Comput..
[29] J. A. Cuesta-Albertos,et al. Trimmed Comparison of Distributions , 2008 .
[30] Gabriel Peyré,et al. Iterative Bregman Projections for Regularized Transportation Problems , 2014, SIAM J. Sci. Comput..
[31] Gabriel Peyré,et al. Learning Generative Models with Sinkhorn Divergences , 2017, AISTATS.
[32] Justin Solomon,et al. Outlier-Robust Optimal Transport , 2020, ICML.
[33] Rama Chellappa,et al. Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation , 2020, NeurIPS.
[34] Gabriel Peyré,et al. Computational Optimal Transport , 2018, Found. Trends Mach. Learn..