Distributed Computation of Wasserstein Barycenters Over Networks
暂无分享,去创建一个
Angelia Nedic | Darina Dvinskikh | Alexander Gasnikov | Pavel E. Dvurechensky | César A. Uribe | A. Nedić | P. Dvurechensky | A. Gasnikov | D. Dvinskikh
[1] M. Fréchet. Les éléments aléatoires de nature quelconque dans un espace distancié , 1948 .
[2] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[3] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[4] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[5] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[6] L. Kantorovich. On the Translocation of Masses , 2006 .
[7] A. Banerjee. Convex Analysis and Optimization , 2006 .
[8] C. Villani. Optimal Transport: Old and New , 2008 .
[9] Arthur Cayley,et al. The Collected Mathematical Papers: On Monge's “Mémoire sur la théorie des déblais et des remblais” , 2009 .
[10] S. Kakade,et al. On the duality of strong convexity and strong smoothness : Learning applications and matrix regularization , 2009 .
[11] M. Beiglböck,et al. Model-independent bounds for option prices—a mass transport approach , 2011, Finance and Stochastics.
[12] Guillaume Carlier,et al. Barycenters in the Wasserstein Space , 2011, SIAM J. Math. Anal..
[13] Julien Rabin,et al. Wasserstein Barycenter and Its Application to Texture Mixing , 2011, SSVM.
[14] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[15] G. Buttazzo,et al. Optimal-transport formulation of electronic density-functional theory , 2012, 1205.4514.
[16] Marco Cuturi,et al. Sinkhorn Distances: Lightspeed Computation of Optimal Transport , 2013, NIPS.
[17] A. Doucet,et al. Distributed nonlinear consensus in the space of probability measures , 2014 .
[18] Arnaud Doucet,et al. Fast Computation of Wasserstein Barycenters , 2013, ICML.
[19] Hossein Mobahi,et al. Learning with a Wasserstein Loss , 2015, NIPS.
[20] Volkan Cevher,et al. WASP: Scalable Bayes via barycenters of subset posteriors , 2015, AISTATS.
[21] Anton Rodomanov,et al. Primal-Dual Method for Searching Equilibrium in Hierarchical Congestion Population Games , 2016, DOOR.
[22] Gabriel Peyré,et al. A Smoothed Dual Approach for Variational Wasserstein Problems , 2015, SIAM J. Imaging Sci..
[23] Jérémie Bigot,et al. Regularization of barycenters in the Wasserstein space , 2016 .
[24] Steffen Borgwardt,et al. Discrete Wasserstein barycenters: optimal transport for discrete data , 2015, Mathematical Methods of Operations Research.
[25] Gabriel Peyré,et al. Stochastic Optimization for Large-scale Optimal Transport , 2016, NIPS.
[26] Y. Nesterov,et al. Efficient numerical methods for entropy-linear programming problems , 2016, Computational Mathematics and Mathematical Physics.
[27] Alexey Chernov,et al. Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints , 2016, DOOR.
[28] Wei Shi,et al. Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs , 2016, SIAM J. Optim..
[29] Avi Wigderson,et al. Much Faster Algorithms for Matrix Scaling , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).
[30] Angelia Nedić,et al. Fast Convergence Rates for Distributed Non-Bayesian Learning , 2015, IEEE Transactions on Automatic Control.
[31] Justin Solomon. Computational Optimal Transport , 2017 .
[32] Angelia Nedic,et al. Distributed Learning for Cooperative Inference , 2017, ArXiv.
[33] Justin Solomon,et al. Parallel Streaming Wasserstein Barycenters , 2017, NIPS.
[34] Zeyuan Allen Zhu,et al. Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent , 2014, ITCS.
[35] P. Dvurechensky,et al. Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints , 2017 .
[36] Angelia Nedic,et al. Optimal Algorithms for Distributed Optimization , 2017, ArXiv.
[37] Nicolas Courty,et al. Large Scale Optimal Transport and Mapping Estimation , 2017, ICLR.
[38] Alexander Gasnikov,et al. Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm , 2018, ICML.
[39] Justin Solomon,et al. Stochastic Wasserstein Barycenters , 2018, ICML.
[40] Darina Dvinskikh,et al. Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters , 2018, NeurIPS.
[41] Bruno Lévy,et al. Notions of optimal transport theory and how to implement them on a computer , 2017, Comput. Graph..
[42] Vivien Seguy,et al. Smooth and Sparse Optimal Transport , 2017, AISTATS.
[43] Gabriel Peyré,et al. Computational Optimal Transport , 2018, Found. Trends Mach. Learn..
[44] Yi Zhou,et al. Communication-efficient algorithms for decentralized and stochastic optimization , 2017, Mathematical Programming.
[45] Sergey Omelchenko,et al. A Stable Alternative to Sinkhorn's Algorithm for Regularized Optimal Transport , 2017, MOTOR.