Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics
Zhichen Zeng, Ruizhong Qiu, Wenxuan Bao et al.
Action editor: Boyu Wang
https://openreview.net/forum?id=dTPBqTKGPs
#graphs #adapting #wasserstein
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
待望の Google Pixel Watch 4 用直挿し充電器発売 - Jetstream
jetstream.blog/2026/02/20/m...
➡️Wasserstein 製 Google Pixel Watch 4 用ポータブル充電器が登場
➡️ケーブル不要となる USB Type-C 直挿しのポータブル設計
➡️安心の Made for Google 認定を取得済み
#GooglePixelWatch4 #Wasserstein #スマートウォッチ
詳細はこちら👇
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
Gaussian mixture layers for neural networks
Sinho Chewi, Philippe Rigollet, Yuling Yan
Action editor: Jeffrey Pennington
https://openreview.net/forum?id=sAptI2o5cP
#layers #gaussian #wasserstein
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
The Performance Of The Unadjusted Langevin Algorithm Without Smoothness Assumptions
Tim Johnston, Iosif Lytras, Nikolaos Makras, Sotirios Sabanis
Action editor: Murat Erdogdu
https://openreview.net/forum?id=TTNeuyYdhg
#langevin #wasserstein #sampling
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
Slicing the Gaussian Mixture Wasserstein Distance
Moritz Piening, Robert Beinert
Action editor: Makoto Yamada
https://openreview.net/forum?id=yPBtJ4JPwi
#wasserstein #generative #minimization
Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud
Mengxi Wu, Hao Huang, Yi Fang, Mohammad Rostami
Action editor: Mathieu Salzmann
https://openreview.net/forum?id=ePXWnH7rGk
#deformation #curvature #wasserstein
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gr...
Stefano Bruno, Sotirios Sabanis
Action editor: Sylvain Le Corff
https://openreview.net/forum?id=vS9iVRB7XF
#generative #wasserstein #distributions
New #J2C Certification:
Slicing the Gaussian Mixture Wasserstein Distance
Moritz Piening, Robert Beinert
https://openreview.net/forum?id=yPBtJ4JPwi
#wasserstein #generative #minimization
New #J2C Certification:
Wasserstein Coreset via Sinkhorn Loss
Haoyun Yin, Yixuan Qiu, Xiao Wang
https://openreview.net/forum?id=DrMCDS88IL
#wasserstein #optimization #sinkhorn
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
Gradient Flow Scales Wasserstein Barycenter Computation
Researchers recast Wasserstein barycenter computation as a gradient flow. Two algorithms are presented—for empirical measures and Gaussian mixtures—and they claim convergence guarantees. getnews.me/gradient-flow-scales-was... #wasserstein #gradientflow
Wasserstein Bounds for Diffusion Models with Gaussian Tails
Research finds diffusion models’ Wasserstein distance scales as O(√d) (up to log factor), so sampling complexity grows with the square‑root of dimension. Submitted 15 Dec 2024. Read more: getnews.me/wasserstein-bounds-for-d... #diffusion #wasserstein
Differentially Private Algorithms for Wasserstein Barycenters
Researchers unveil the first differentially private algorithms for Wasserstein barycenters, delivering results on synthetic data, MNIST, and a U.S. population set. Read more: getnews.me/differentially-private-a... #differentialprivacy #wasserstein
Spectral‑Grassmann Wasserstein Metric Boosts Machine‑Learning for Dynamical Systems
The Spectral-Grassmann Wasserstein metric offers a distance between dynamical systems that stays invariant to sampling frequency, announced in September 2025. getnews.me/spectral-grassmann-wasse... #spectralgrassmann #wasserstein
Variable-Preconditioned Transformed Primal-Dual Method for Wasserstein Flows
The VPTPD algorithm uses adaptive step‑size and variable preconditioning to reduce iterations in 1‑ to 3‑dimensional Wasserstein gradient flow simulations. Read more: getnews.me/variable-preconditioned-... #wasserstein #gradientflows
Forward Euler Breaks on Wasserstein Flows; Regularization Helps
Study shows forward‑Euler diverges on Wasserstein KL gradient flows, but a regularizing term restores convergence to the global minimizer (16 Sep 2025). Read more: getnews.me/forward-euler-breaks-on-... #wasserstein #gradientflow #euler
Transport Alpha Divergences Connect Wasserstein Geometry to AI
Researchers introduced transport alpha divergences, a one‑parameter family linking Itakura–Saito and a transport‑adjusted KL, with the arXiv paper revised on 12 Sep 2025. getnews.me/transport-alpha-divergen... #transportalphadivergence #wasserstein
New Multiscale Framework Unveiled for Wasserstein Space Analysis
Researchers unveiled a multiscale framework for analyzing measure sequences in Wasserstein spaces, handling data types with an optimality number metric to detect anomalies. Read more: getnews.me/new-multiscale-framework... #wasserstein #multiscale
@arasselvi.bsky.social from @imperialcollegeldn.bsky.social will present his paper: 'It's all in the mix: #Wasserstein classification and regression with mixed features' to the Management School on 2 July.
➡️ shorturl.at/3aJ2k
#OSCM #Operations #SupplyChain #Management
64/n | Sareta has lectured at Harvard Law School’s #HumanRights Program during her time as a #Wasserstein Fellow, at Stanford Law School as their inaugural global practitioner-in-residence, and at the London School of Economics’ #MiddleEast Centre.
A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning
Amadou Siaka SANGARE, Nicolas Dunou, Jhony H. Giraldo, Fragkiskos D. Malliaros
Action editor: Moshe Eliasof
https://openreview.net/forum?id=J7cY9Jr9WM
#subgraph #graphs #wasserstein
Wasserstein Coreset via Sinkhorn Loss
Haoyun Yin, Yixuan Qiu, Xiao Wang
Action editor: Brian Kingsbury
https://openreview.net/forum?id=DrMCDS88IL
#wasserstein #optimization #sinkhorn
An analysis of the noise schedule for score-based generative models
Stanislas Strasman, Antonio Ocello, Claire Boyer, Sylvain Le Corff, Vincent Lemaire
Action editor: Bruno Loureiro
https://openreview.net/forum?id=BlYIPa0Fx1
#generative #wasserstein #hyperparameters
MicroCloud Hologram Inc. Introduces Innovative Theory for Quantum State Measurement #China #Shenzhen #Wasserstein #MicroCloud #Quantum_States
Minimax Posterior Contraction Rates for Unconstrained Distribution Estimation on $[0,1]^d$ under ...
Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya, Debdeep Pati
Action editor: Alp Kucukelbir
https://openreview.net/forum?id=UrSgGSTM2J
#wasserstein #posterior #histograms
Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space
Antoine Salmona, Agnes Desolneux, Julie Delon
Action editor: Jeff Phillips
https://openreview.net/forum?id=7t7fJT4Gym
#metric #distances #wasserstein
