#wasserstein — Public Fediverse posts

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/

#MachineLearning #Wasserstein

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/

#MachineLearning #Wasserstein

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/

#Wasserstein

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/

#Wasserstein

📐📚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.

🌍 https://openreview.net/forum?id=Xpt0HEC3fO

#OptimalTransport #MachineLearning

📐📚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.

🌍 https://openreview.net/forum?id=Xpt0HEC3fO

#OptimalTransport #MachineLearning

📐 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:

📄 https://arxiv.org/abs/2604.04891

#MachineLearning #WassersteinDistance

📐 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:

📄 https://arxiv.org/abs/2604.04891

#MachineLearning #WassersteinDistance

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

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

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

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

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

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

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

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

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

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

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

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

'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 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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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/

#MachineLearning

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.

🌎 https://www.fabriziomusacchio.com/blog/2023-07-29-wgan/

#MachineLearning

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.

🌎 https://www.fabriziomusacchio.com/blog/2023-07-29-wgan/

#MachineLearning

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/

The #Wasserstein distance (#EMD), sliced Wasserstein distance (#SWD), and the #L2norm are common #metrics used to quantify the ‘distance’ between two distributions. This tutorial compares these three metrics and discusses their advantages and disadvantages.

🌎 https://www.fabriziomusacchio.com/blog/2023-07-26-wasserstein_vs_l2_norm/

#OptimalTransport #MachineLearning

This tutorial takes a different approach to explain the #Wasserstein distance (#EMD) by approximating the #EMD with cumulative distribution functions (#CDF), providing a more intuitive understanding of the metric.

🌎 https://www.fabriziomusacchio.com/blog/2023-07-24-wasserstein_distance_cdf_approximation/

#OptimalTransport

Calculating the #Wasserstein distance (#EMD) 📈 can be computational costly when using #LinearProgramming. The #Sinkhorn algorithm provides a computationally efficient method for approximating the EMD, making it a practical choice for many applications, especially for large datasets 💫. Here is another tutorial, showing how to solve #OptimalTransport problem using the Sinkhorn algorithm in #Python 🐍

🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/

The #Wasserstein distance 📐, aka Earth Mover’s Distance (#EMD), provides a robust and insightful approach for comparing #ProbabilityDistributions 📊. I’ve composed a #Python tutorial 🐍 that explains the #OptimalTransport problem required to calculate EMD. It also shows how to solve the OT problem and calculate the EMD using the Python Optimal Transport (POT) library. Feel free to use and share it 🤗

🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/

An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow

Carles Domingo-Enrich, Aram-Alexandre Pooladian

Action editor: Murat Erdogdu.

https://openreview.net/forum?id=9pWjgQ3y85

#wasserstein #langevin #rao

'Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning', by Titouan Vayer, Rémi Gribonval.

http://jmlr.org/papers/v24/21-1516.html

#compressive #wasserstein #norms

Costs and Benefits of Fair Regression

Han Zhao

https://openreview.net/forum?id=v6anjyEDVW

#fairness #parity #wasserstein