Score-based generative models are provably robust: an uncertainty quantification perspective
Fuente:
arXiv
Saved in:
| Main Authors: | Mimikos-Stamatopoulos, Nikiforos, Zhang, Benjamin J., Katsoulakis, Markos A. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Equivariant score-based generative models provably learn distributions with symmetries efficiently
by: Chen, Ziyu, et al.
Published: (2024)
by: Chen, Ziyu, et al.
Published: (2024)
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)
by: Zhang, Benjamin J., et al.
Published: (2025)
Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
by: Kan, Kelvin, et al.
Published: (2026)
by: Kan, Kelvin, et al.
Published: (2026)
Sample Complexity of Probability Divergences under Group Symmetry
by: Chen, Ziyu, et al.
Published: (2023)
by: Chen, Ziyu, et al.
Published: (2023)
Variational bagging: a robust approach for Bayesian uncertainty quantification
by: Fan, Shitao, et al.
Published: (2025)
by: Fan, Shitao, et al.
Published: (2025)
Probabilistic computation and uncertainty quantification with emerging covariance
by: Ma, Hengyuan, et al.
Published: (2023)
by: Ma, Hengyuan, et al.
Published: (2023)
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
by: Zhu, Libin, et al.
Published: (2026)
by: Zhu, Libin, et al.
Published: (2026)
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
by: Bieringer, Sebastian, et al.
Published: (2023)
by: Bieringer, Sebastian, et al.
Published: (2023)
Consistent estimation of generative model representations in the data kernel perspective space
by: Acharyya, Aranyak, et al.
Published: (2024)
by: Acharyya, Aranyak, et al.
Published: (2024)
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows
by: Gu, Hyemin, et al.
Published: (2024)
by: Gu, Hyemin, et al.
Published: (2024)
Wasserstein proximal operators describe score-based generative models and resolve memorization
by: Zhang, Benjamin J., et al.
Published: (2024)
by: Zhang, Benjamin J., et al.
Published: (2024)
A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning
by: Dewolf, Nicolas
Published: (2024)
by: Dewolf, Nicolas
Published: (2024)
A provable initialization and robust clustering method for general mixture models
by: Jana, Soham, et al.
Published: (2024)
by: Jana, Soham, et al.
Published: (2024)
An analysis of the noise schedule for score-based generative models
by: Strasman, Stanislas, et al.
Published: (2024)
by: Strasman, Stanislas, et al.
Published: (2024)
Uncertainty quantification in metric spaces
by: Lugosi, Gábor, et al.
Published: (2024)
by: Lugosi, Gábor, et al.
Published: (2024)
Flow-based generative models as iterative algorithms in probability space
by: Xie, Yao, et al.
Published: (2025)
by: Xie, Yao, et al.
Published: (2025)
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters
by: Liu, Hao, et al.
Published: (2024)
by: Liu, Hao, et al.
Published: (2024)
Score-based sampling without diffusions: Guidance from a simple and modular scheme
by: Wainwright, M. J.
Published: (2025)
by: Wainwright, M. J.
Published: (2025)
Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
by: Zhang, Kaihong, et al.
Published: (2024)
by: Zhang, Kaihong, et al.
Published: (2024)
Uncertainty quantification for iterative algorithms in linear models with application to early stopping
by: Bellec, Pierre C., et al.
Published: (2024)
by: Bellec, Pierre C., et al.
Published: (2024)
KL Convergence Guarantees for Score diffusion models under minimal data assumptions
by: Conforti, Giovanni, et al.
Published: (2023)
by: Conforti, Giovanni, et al.
Published: (2023)
Point processes with event time uncertainty
by: Cheng, Xiuyuan, et al.
Published: (2024)
by: Cheng, Xiuyuan, et al.
Published: (2024)
On uncertainty-penalized Bayesian information criterion
by: Thanasutives, Pongpisit, et al.
Published: (2024)
by: Thanasutives, Pongpisit, et al.
Published: (2024)
On damage of interpolation to adversarial robustness in regression
by: Peng, Jingfu, et al.
Published: (2026)
by: Peng, Jingfu, et al.
Published: (2026)
Incorporating structural uncertainty in causal decision making
by: Kaptein, Maurits
Published: (2025)
by: Kaptein, Maurits
Published: (2025)
Convergence of the Inexact Langevin Algorithm in KL Divergence with Application to Score-based Generative Models
by: Yang, Kaylee Yingxi, et al.
Published: (2022)
by: Yang, Kaylee Yingxi, et al.
Published: (2022)
Bayesian Cramér-Rao Bound Estimation with Score-Based Models
by: Crafts, Evan Scope, et al.
Published: (2023)
by: Crafts, Evan Scope, et al.
Published: (2023)
A review of predictive uncertainty estimation with machine learning
by: Tyralis, Hristos, et al.
Published: (2022)
by: Tyralis, Hristos, et al.
Published: (2022)
Inference via robust optimal transportation: theory and methods
by: Ma, Yiming, et al.
Published: (2023)
by: Ma, Yiming, et al.
Published: (2023)
Distributed quasi-Newton robust estimation under differential privacy
by: Wang, Chuhan, et al.
Published: (2024)
by: Wang, Chuhan, et al.
Published: (2024)
An adaptive transfer learning perspective on classification in non-stationary environments
by: Reeve, Henry W J
Published: (2024)
by: Reeve, Henry W J
Published: (2024)
Scoring Rules and Calibration for Imprecise Probabilities
by: Fröhlich, Christian, et al.
Published: (2024)
by: Fröhlich, Christian, et al.
Published: (2024)
Beyond Scores: Proximal Diffusion Models
by: Fang, Zhenghan, et al.
Published: (2025)
by: Fang, Zhenghan, et al.
Published: (2025)
A theoretical framework for M-posteriors: frequentist guarantees and robustness properties
by: Marusic, Juraj, et al.
Published: (2025)
by: Marusic, Juraj, et al.
Published: (2025)
Rectifying Conformity Scores for Better Conditional Coverage
by: Plassier, Vincent, et al.
Published: (2025)
by: Plassier, Vincent, et al.
Published: (2025)
Doubly robust inference via calibration
by: van der Laan, Lars, et al.
Published: (2024)
by: van der Laan, Lars, et al.
Published: (2024)
On the physics of nested Markov models: a generalized probabilistic theory perspective
by: Zhang, Xingjian, et al.
Published: (2024)
by: Zhang, Xingjian, et al.
Published: (2024)
Causal inference through multi-stage learning and doubly robust deep neural networks
by: Zhang, Yuqian, et al.
Published: (2024)
by: Zhang, Yuqian, et al.
Published: (2024)
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
by: Bobbia, Benjamin, et al.
Published: (2024)
by: Bobbia, Benjamin, et al.
Published: (2024)
Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty
by: Charpentier, Arthur, et al.
Published: (2026)
by: Charpentier, Arthur, et al.
Published: (2026)
Similar Items
-
Equivariant score-based generative models provably learn distributions with symmetries efficiently
by: Chen, Ziyu, et al.
Published: (2024) -
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025) -
Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
by: Kan, Kelvin, et al.
Published: (2026) -
Sample Complexity of Probability Divergences under Group Symmetry
by: Chen, Ziyu, et al.
Published: (2023) -
Variational bagging: a robust approach for Bayesian uncertainty quantification
by: Fan, Shitao, et al.
Published: (2025)