Cover Image

Equivariant score-based generative models provably learn distributions with symmetries efficiently

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Ziyu, Katsoulakis, Markos A., Zhang, Benjamin J.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Score-based generative models are provably robust: an uncertainty quantification perspective
by: Mimikos-Stamatopoulos, Nikiforos, et al.
Published: (2024)

Wasserstein proximal operators describe score-based generative models and resolve memorization
by: Zhang, Benjamin J., et al.
Published: (2024)

Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
by: Tran, Hoang, et al.
Published: (2025)

Nonlinear denoising score matching for enhanced learning of structured distributions
by: Birrell, Jeremiah, et al.
Published: (2024)

Proximal optimal transport divergences
by: Baptista, Ricardo, et al.
Published: (2025)

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)

Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows
by: Gu, Hyemin, et al.
Published: (2024)

Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
by: Che, Yanming, et al.
Published: (2023)

A Malliavin calculus approach to score functions in diffusion generative models
by: Mirafzali, Ehsan, et al.
Published: (2025)

Residual connections provably mitigate oversmoothing in graph neural networks
by: Chen, Ziang, et al.
Published: (2025)

Sample Complexity of Probability Divergences under Group Symmetry
by: Chen, Ziyu, et al.
Published: (2023)

Robust Generative Learning with Lipschitz-Regularized $α$-Divergences Allows Minimal Assumptions on Target Distributions
by: Chen, Ziyu, et al.
Published: (2024)

Towards provably efficient quantum algorithms for large-scale machine-learning models
by: Liu, Junyu, et al.
Published: (2023)

Statistical Guarantees of Group-Invariant GANs
by: Chen, Ziyu, et al.
Published: (2023)

An invertible generative model for forward and inverse problems
by: van Leeuwen, Tristan, et al.
Published: (2025)

Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models
by: Yi, Jiaxiang, et al.
Published: (2024)

On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates
by: Bruno, Stefano, et al.
Published: (2023)

Efficiently learning and sampling multimodal distributions with data-based initialization
by: Koehler, Frederic, et al.
Published: (2024)

Machine learning-based system reliability analysis with Gaussian Process Regression
by: Zhou, Lisang, et al.
Published: (2024)

Evaluating the design space of diffusion-based generative models
by: Wang, Yuqing, et al.
Published: (2024)

Neural stochastic Volterra equations: learning path-dependent dynamics
by: Bergerhausen, Martin, et al.
Published: (2024)

Divergence-kernel method for linear responses of densities and generative models
by: Ni, Angxiu
Published: (2025)

Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
by: Kan, Kelvin, et al.
Published: (2026)

Gaussian Approximation for Asynchronous Q-learning
by: Rubtsov, Artemy, et al.
Published: (2026)

When and how can inexact generative models still sample from the data manifold?
by: Chandramoorthy, Nisha, et al.
Published: (2025)

Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling
by: Benigni, Lucas, et al.
Published: (2025)

Tunable correlation retention: A statistical method for generating synthetic data
by: Jävergård, Nicklas, et al.
Published: (2024)

Vector preference-based contextual bandits under distributional shifts
by: Shukla, Apurv, et al.
Published: (2025)

Extraction of informative statistical features in the problem of forecasting time series generated by It{ô}-type processes
by: Korolev, Victor, et al.
Published: (2026)

Score matching for bridges without learning time-reversals
by: Baker, Elizabeth L., et al.
Published: (2024)

Approximating G(t)/GI/1 queues with deep learning
by: Sherzer, Eliran, et al.
Published: (2024)

Normalizing Flows on Quotient Manifolds via Boundary Quotients
by: Ghanem, William, et al.
Published: (2025)

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
by: Schillinger, Maybritt, et al.
Published: (2025)

Negative Dependence as a toolbox for machine learning : review and new developments
by: Tran, Hoang-Son, et al.
Published: (2025)

Active operator learning with predictive uncertainty quantification for partial differential equations
by: Winovich, Nick, et al.
Published: (2025)

Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency
by: Kan, Kelvin, et al.
Published: (2025)

Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)

Comparing statistical and deep learning techniques for parameter estimation of continuous-time stochastic differentiable equations
by: Sankoh, Aroon, et al.
Published: (2025)

Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors
by: Pierret, Emile, et al.
Published: (2024)

Causality and Bayesian network PDEs for multiscale representations of porous media
by: Um, Kimoon, et al.
Published: (2019)