#wasserstein — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #wasserstein, aggregated by home.social.
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Following up on this, I also explored a more direct use of #WassersteinDistance in #WGANs: Instead of training a discriminator, the generator is optimized by explicitly computing the #OptimalTransport distance between real and generated samples. This turns the loss into the actual metric of interest and removes the adversarial setup, leading to a more direct and stable training signal. And we can generate cool animations, too ^_^
🌍 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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I actually wrote a short introduction to #WassersteinDistance and #OptimalTransport some time ago, if you’re looking for a more intuitive entry point:
🌍 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐 New preprint by Gabriel Peyré: The paper introduces a new class of spectral #Wasserstein distances, linking #OptimalTransport with normalized #gradient methods. It shows that spectrally normalized #GradientDescent can be interpreted as a gradient flow in this spectral-W geometry, providing a principled bridge between #optimization dynamics and transport metrics:
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This #CMSPaper investigates different #AI #machinelearning methods that aim to find jets that are inconsistent with the standard model. It shows that a new method called #Wasserstein normalized autoencodes works much better than other neural networks arxiv.org/abs/2510.02168
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This #CMSPaper investigates different #AI #machinelearning methods that aim to find jets that are inconsistent with the standard model. It shows that a new method called #Wasserstein normalized autoencodes works much better than other neural networks arxiv.org/abs/2510.02168
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This #CMSPaper investigates different #AI #machinelearning methods that aim to find jets that are inconsistent with the standard model. It shows that a new method called #Wasserstein normalized autoencodes works much better than other neural networks arxiv.org/abs/2510.02168
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This #CMSPaper investigates different #AI #machinelearning methods that aim to find jets that are inconsistent with the standard model. It shows that a new method called #Wasserstein normalized autoencodes works much better than other neural networks arxiv.org/abs/2510.02168
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This #CMSPaper investigate different #AI #machinelearning methods that aim to find jets that are inconsistent with the standard model. It shows that a new method called #Wasserstein normalized autoencoders works much better than other autonomous neural networks at finding those anomalous jets, because the new method is much better at dealing with outlier cases https://arxiv.org/abs/2510.02168
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A short introduction to optimal transport and Wasserstein distance
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/
#HackerNews #optimaltransport #Wasserstein #distance #machinelearning #math #statistics #dataanalysis
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'Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds', by Haoshu Xu, Hongzhe Li.
http://jmlr.org/papers/v26/24-0493.html
#wasserstein #covariates #covariate -
'Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models', by Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu.
http://jmlr.org/papers/v26/24-0902.html
#generative #wasserstein #models -
'Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds', by Clément Bonet, Lucas Drumetz, Nicolas Courty.
http://jmlr.org/papers/v26/24-0359.html
#manifolds #manifold #wasserstein -
'Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"', by Daniel Paulin, Peter A. Whalley.
http://jmlr.org/papers/v25/24-0895.html
#ergodic #wasserstein #approximations -
'Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"', by Daniel Paulin, Peter A. Whalley.
http://jmlr.org/papers/v25/24-0895.html
#ergodic #wasserstein #approximations -
'Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"', by Daniel Paulin, Peter A. Whalley.
http://jmlr.org/papers/v25/24-0895.html
#ergodic #wasserstein #approximations -
'Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"', by Daniel Paulin, Peter A. Whalley.
http://jmlr.org/papers/v25/24-0895.html
#ergodic #wasserstein #approximations -
'Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"', by Daniel Paulin, Peter A. Whalley.
http://jmlr.org/papers/v25/24-0895.html
#ergodic #wasserstein #approximations -
'Entropic Gromov-Wasserstein Distances: Stability and Algorithms', by Gabriel Rioux, Ziv Goldfeld, Kengo Kato.
http://jmlr.org/papers/v25/24-0039.html
#regularization #wasserstein #variational -
'Wasserstein Proximal Coordinate Gradient Algorithms', by Rentian Yao, Xiaohui Chen, Yun Yang.
http://jmlr.org/papers/v25/23-0889.html
#wasserstein #optimization #gradient -
'Characterization of translation invariant MMD on Rd and connections with Wasserstein distances', by Thibault Modeste, Clément Dombry.
http://jmlr.org/papers/v25/22-1338.html
#wasserstein #measures #mmds -
'Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning', by Yiling Xie, Xiaoming Huo.
http://jmlr.org/papers/v25/23-0379.html
#wasserstein #estimators #robust -
'Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning', by Yiling Xie, Xiaoming Huo.
http://jmlr.org/papers/v25/23-0379.html
#wasserstein #estimators #robust -
'Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning', by Yiling Xie, Xiaoming Huo.
http://jmlr.org/papers/v25/23-0379.html
#wasserstein #estimators #robust -
'Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning', by Yiling Xie, Xiaoming Huo.
http://jmlr.org/papers/v25/23-0379.html
#wasserstein #estimators #robust -
'Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning', by Yiling Xie, Xiaoming Huo.
http://jmlr.org/papers/v25/23-0379.html
#wasserstein #estimators #robust -
'Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization', by O. Deniz Akyildiz, Sotirios Sabanis.
http://jmlr.org/papers/v25/21-1423.html
#wasserstein #nonasymptotic #stochastic -
'Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization', by O. Deniz Akyildiz, Sotirios Sabanis.
http://jmlr.org/papers/v25/21-1423.html
#wasserstein #nonasymptotic #stochastic -
'Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization', by O. Deniz Akyildiz, Sotirios Sabanis.
http://jmlr.org/papers/v25/21-1423.html
#wasserstein #nonasymptotic #stochastic -
'Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization', by O. Deniz Akyildiz, Sotirios Sabanis.
http://jmlr.org/papers/v25/21-1423.html
#wasserstein #nonasymptotic #stochastic -
'Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization', by O. Deniz Akyildiz, Sotirios Sabanis.
http://jmlr.org/papers/v25/21-1423.html
#wasserstein #nonasymptotic #stochastic -
'Tangential Wasserstein Projections', by Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee.
http://jmlr.org/papers/v25/23-0708.html
#wasserstein #projections #causal -
'Fair Data Representation for Machine Learning at the Pareto Frontier', by Shizhou Xu, Thomas Strohmer.
http://jmlr.org/papers/v24/22-0005.html
#wasserstein #supervised #fairness -
'A PDE approach for regret bounds under partial monitoring', by Erhan Bayraktar, Ibrahim Ekren, Xin Zhang.
http://jmlr.org/papers/v24/22-1001.html
#wasserstein #forecaster #regret -
Eliminating the middleman: You can apply the computation of the #Wasserstein distance even more directly in #WassersteinGANs (#WGANs), eliminating the need for a discriminator.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Eliminating the middleman: You can apply the computation of the #Wasserstein distance even more directly in #WassersteinGANs (#WGANs), eliminating the need for a discriminator.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Eliminating the middleman: You can apply the computation of the #Wasserstein distance even more directly in #WassersteinGANs (#WGANs), eliminating the need for a discriminator.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Eliminating the middleman: You can apply the computation of the #Wasserstein distance even more directly in #WassersteinGANs (#WGANs), eliminating the need for a discriminator.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Eliminating the middleman: You can apply the computation of the #Wasserstein distance even more directly in #WassersteinGANs (#WGANs), eliminating the need for a discriminator.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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The #Wasserstein #metric (#EMD) can be used, to train #GenerativeAdversarialNetworks (#GANs) more effectively. This tutorial compares a default GAN with a #WassersteinGAN (#WGAN) trained on the #MNIST dataset.
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The #Wasserstein #metric (#EMD) can be used, to train #GenerativeAdversarialNetworks (#GANs) more effectively. This tutorial compares a default GAN with a #WassersteinGAN (#WGAN) trained on the #MNIST dataset.
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The #Wasserstein #metric (#EMD) can be used, to train #GenerativeAdversarialNetworks (#GANs) more effectively. This tutorial compares a default GAN with a #WassersteinGAN (#WGAN) trained on the #MNIST dataset.
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Apart from #Wasserstein Distance (#EMD), other #metrics also play an important role in #MachineLearning tasks such as #clustering, #classification, and #InformationRetrieval. In this tutorial, you can find a discussion of five commonly used metrics: EMD, #KullbackLeiblerDivergence (KL Divergence), #JensenShannonDivergence (JS Divergence), #TotalVariationDistance (TV Distance), and #BhattacharyyaDistance.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-28-probability_density_metrics/
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Apart from #Wasserstein Distance (#EMD), other #metrics also play an important role in #MachineLearning tasks such as #clustering, #classification, and #InformationRetrieval. In this tutorial, you can find a discussion of five commonly used metrics: EMD, #KullbackLeiblerDivergence (KL Divergence), #JensenShannonDivergence (JS Divergence), #TotalVariationDistance (TV Distance), and #BhattacharyyaDistance.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-28-probability_density_metrics/
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Apart from #Wasserstein Distance (#EMD), other #metrics also play an important role in #MachineLearning tasks such as #clustering, #classification, and #InformationRetrieval. In this tutorial, you can find a discussion of five commonly used metrics: EMD, #KullbackLeiblerDivergence (KL Divergence), #JensenShannonDivergence (JS Divergence), #TotalVariationDistance (TV Distance), and #BhattacharyyaDistance.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-28-probability_density_metrics/
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Apart from #Wasserstein Distance (#EMD), other #metrics also play an important role in #MachineLearning tasks such as #clustering, #classification, and #InformationRetrieval. In this tutorial, you can find a discussion of five commonly used metrics: EMD, #KullbackLeiblerDivergence (KL Divergence), #JensenShannonDivergence (JS Divergence), #TotalVariationDistance (TV Distance), and #BhattacharyyaDistance.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-28-probability_density_metrics/