Saved in:
| Main Authors: | Figueiredo, Eduardo, Adams, Steven, Laurenti, Luca |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.08063 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance
by: Adams, Steven, et al.
Published: (2025)
by: Adams, Steven, et al.
Published: (2025)
Efficient Uncertainty Propagation with Guarantees in Wasserstein Distance
by: Figueiredo, Eduardo, et al.
Published: (2025)
by: Figueiredo, Eduardo, et al.
Published: (2025)
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
by: Adams, Steven, et al.
Published: (2024)
by: Adams, Steven, et al.
Published: (2024)
Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification
by: Reed, Robert, et al.
Published: (2024)
by: Reed, Robert, et al.
Published: (2024)
Formal Uncertainty Propagation for Stochastic Dynamical Systems with Additive Noise
by: Adams, Steven, et al.
Published: (2025)
by: Adams, Steven, et al.
Published: (2025)
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
by: Kong, Chun-Wei, et al.
Published: (2024)
by: Kong, Chun-Wei, et al.
Published: (2024)
Bounds in Wasserstein Distance for Locally Stationary Processes
by: Tinio, Jan Nino G., et al.
Published: (2024)
by: Tinio, Jan Nino G., et al.
Published: (2024)
Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction
by: Schumann, Julian F., et al.
Published: (2026)
by: Schumann, Julian F., et al.
Published: (2026)
Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
by: Xu, Ao, et al.
Published: (2026)
by: Xu, Ao, et al.
Published: (2026)
Bounds in Wasserstein Distance for Locally Stationary Functional Time Series
by: Tinio, Jan Nino G., et al.
Published: (2025)
by: Tinio, Jan Nino G., et al.
Published: (2025)
Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances
by: Nguyen, Khai, et al.
Published: (2025)
by: Nguyen, Khai, et al.
Published: (2025)
Promises of Deep Kernel Learning for Control Synthesis
by: Reed, Robert, et al.
Published: (2023)
by: Reed, Robert, et al.
Published: (2023)
The Observable Wasserstein Distance
by: Santos, Edivaldo Lopes dos, et al.
Published: (2026)
by: Santos, Edivaldo Lopes dos, et al.
Published: (2026)
Private Wasserstein Distance
by: Li, Wenqian, et al.
Published: (2024)
by: Li, Wenqian, et al.
Published: (2024)
Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
by: Wang, Lei, et al.
Published: (2025)
by: Wang, Lei, et al.
Published: (2025)
An Empirical Study of Self-supervised Learning with Wasserstein Distance
by: Yamada, Makoto, et al.
Published: (2023)
by: Yamada, Makoto, et al.
Published: (2023)
Verification of Unknown Dynamical Systems via Autoencoder Latent Space
by: Reed, Robert, et al.
Published: (2025)
by: Reed, Robert, et al.
Published: (2025)
Finite-Sample Wasserstein Error Bounds and Concentration Inequalities for Nonlinear Stochastic Approximation
by: Kong, Seo Taek, et al.
Published: (2026)
by: Kong, Seo Taek, et al.
Published: (2026)
Distance-Based Tree-Sliced Wasserstein Distance
by: Tran, Hoang V., et al.
Published: (2025)
by: Tran, Hoang V., et al.
Published: (2025)
Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
by: Betteti, Simone, et al.
Published: (2026)
by: Betteti, Simone, et al.
Published: (2026)
Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
by: Arsenyan, Vahan, et al.
Published: (2025)
by: Arsenyan, Vahan, et al.
Published: (2025)
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
by: Adams, Reuben, et al.
Published: (2022)
by: Adams, Reuben, et al.
Published: (2022)
A Relaxed Wasserstein Distance Formulation for Mixtures of Radially Contoured Distributions
by: Chen, Keyu, et al.
Published: (2025)
by: Chen, Keyu, et al.
Published: (2025)
An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2024)
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2024)
Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application
by: Li, Chenze, et al.
Published: (2025)
by: Li, Chenze, et al.
Published: (2025)
Feature Selection Based on Wasserstein Distance
by: Li, Fuwei
Published: (2024)
by: Li, Fuwei
Published: (2024)
Relative Translation Invariant Wasserstein Distance
by: Wang, Binshuai, et al.
Published: (2024)
by: Wang, Binshuai, et al.
Published: (2024)
The Z-Gromov-Wasserstein Distance
by: Bauer, Martin, et al.
Published: (2024)
by: Bauer, Martin, et al.
Published: (2024)
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
by: Vertovec, Nikolaus, et al.
Published: (2025)
by: Vertovec, Nikolaus, et al.
Published: (2025)
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
by: Aminian, Gholamali, et al.
Published: (2022)
by: Aminian, Gholamali, et al.
Published: (2022)
Sliced-Wasserstein Distance-based Data Selection
by: Pallage, Julien, et al.
Published: (2025)
by: Pallage, Julien, et al.
Published: (2025)
Fast Gradient Computation for Gromov-Wasserstein Distance
by: Zhang, Wei, et al.
Published: (2024)
by: Zhang, Wei, et al.
Published: (2024)
Spherical Tree-Sliced Wasserstein Distance
by: Tran, Viet-Hoang, et al.
Published: (2025)
by: Tran, Viet-Hoang, et al.
Published: (2025)
Robust Estimation under the Wasserstein Distance
by: Nietert, Sloan, et al.
Published: (2023)
by: Nietert, Sloan, et al.
Published: (2023)
Semidefinite Relaxations of the Gromov-Wasserstein Distance
by: Chen, Junyu, et al.
Published: (2023)
by: Chen, Junyu, et al.
Published: (2023)
Wasserstein Distances, Neuronal Entanglement, and Sparsity
by: Sawmya, Shashata, et al.
Published: (2024)
by: Sawmya, Shashata, et al.
Published: (2024)
Slicing the Gaussian Mixture Wasserstein Distance
by: Piening, Moritz, et al.
Published: (2025)
by: Piening, Moritz, et al.
Published: (2025)
Distributional Reinforcement Learning with Regularized Wasserstein Loss
by: Sun, Ke, et al.
Published: (2022)
by: Sun, Ke, et al.
Published: (2022)
Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026)
by: Chen, Guixian, et al.
Published: (2026)
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
by: Figueiredo, Eduardo, et al.
Published: (2024)
by: Figueiredo, Eduardo, et al.
Published: (2024)
Similar Items
-
discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance
by: Adams, Steven, et al.
Published: (2025) -
Efficient Uncertainty Propagation with Guarantees in Wasserstein Distance
by: Figueiredo, Eduardo, et al.
Published: (2025) -
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
by: Adams, Steven, et al.
Published: (2024) -
Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification
by: Reed, Robert, et al.
Published: (2024) -
Formal Uncertainty Propagation for Stochastic Dynamical Systems with Additive Noise
by: Adams, Steven, et al.
Published: (2025)