Saved in:
| Main Authors: | Cardoso, Gabriel V, Pereira, Mike |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.24556 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Diffusion posterior sampling for simulation-based inference in tall data settings
by: Linhart, Julia, et al.
Published: (2024)
by: Linhart, Julia, et al.
Published: (2024)
Uncertainty quantification in model discovery by distilling interpretable material constitutive models from Gaussian process posteriors
by: Anton, David, et al.
Published: (2025)
by: Anton, David, et al.
Published: (2025)
A Gibbs posterior sampler for inverse problem based on prior diffusion model
by: Giovannelli, Jean-François
Published: (2026)
by: Giovannelli, Jean-François
Published: (2026)
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
by: Stankewitz, Bernhard, et al.
Published: (2024)
by: Stankewitz, Bernhard, et al.
Published: (2024)
Estimation of partially known Gaussian graphical models with score-based structural priors
by: Sevilla, Martín, et al.
Published: (2024)
by: Sevilla, Martín, et al.
Published: (2024)
Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models
by: Porwal, Anupreet, et al.
Published: (2024)
by: Porwal, Anupreet, et al.
Published: (2024)
Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
by: Tang, Pucheng, et al.
Published: (2025)
by: Tang, Pucheng, et al.
Published: (2025)
On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing
by: Petti, Samantha, et al.
Published: (2025)
by: Petti, Samantha, et al.
Published: (2025)
Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators
by: Wang, Xinming, et al.
Published: (2024)
by: Wang, Xinming, et al.
Published: (2024)
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal data
by: Timonen, Juho, et al.
Published: (2021)
by: Timonen, Juho, et al.
Published: (2021)
Robust non-parametric mortality and fertility modelling and forecasting: Gaussian process regression approaches
by: Lam, Ka Kin, et al.
Published: (2021)
by: Lam, Ka Kin, et al.
Published: (2021)
Diffusion priors for Bayesian 3D reconstruction from incomplete measurements
by: Möbius, Julian L., et al.
Published: (2024)
by: Möbius, Julian L., et al.
Published: (2024)
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller
by: Houdouin, Pierre, et al.
Published: (2025)
by: Houdouin, Pierre, et al.
Published: (2025)
Mitigating covariate shift in non-colocated data with learned parameter priors
by: Khan, Behraj, et al.
Published: (2024)
by: Khan, Behraj, et al.
Published: (2024)
Neural Koopman prior for data assimilation
by: Frion, Anthony, et al.
Published: (2023)
by: Frion, Anthony, et al.
Published: (2023)
Towards accurate extreme event likelihoods from diffusion model climate emulators
by: Manshausen, Peter, et al.
Published: (2026)
by: Manshausen, Peter, et al.
Published: (2026)
Diffusion priors enhanced velocity model building from time-lag images using a neural operator
by: Ma, Xiao, et al.
Published: (2025)
by: Ma, Xiao, et al.
Published: (2025)
Learning battery model parameter dynamics from data with recursive Gaussian process regression
by: Aitio, Antti, et al.
Published: (2023)
by: Aitio, Antti, et al.
Published: (2023)
Gradient-enhanced deep Gaussian processes for multifidelity modelling
by: Bone, Viv, et al.
Published: (2024)
by: Bone, Viv, et al.
Published: (2024)
Expressive Mortality Models through Gaussian Process Kernels
by: Ludkovski, Mike, et al.
Published: (2023)
by: Ludkovski, Mike, et al.
Published: (2023)
Training data membership inference via Gaussian process meta-modeling: a post-hoc analysis approach
by: Huang, Yongchao, et al.
Published: (2025)
by: Huang, Yongchao, et al.
Published: (2025)
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation
by: Cho, Soonhong, et al.
Published: (2024)
by: Cho, Soonhong, et al.
Published: (2024)
Overparametrized models with posterior drift
by: Coqueret, Guillaume, et al.
Published: (2025)
by: Coqueret, Guillaume, et al.
Published: (2025)
Student-t processes as infinite-width limits of posterior Bayesian neural networks
by: Caporali, Francesco, et al.
Published: (2025)
by: Caporali, Francesco, et al.
Published: (2025)
Variational Autoencoder for Generating Broader-Spectrum prior Proposals in Markov chain Monte Carlo Methods
by: Borges, Marcio, et al.
Published: (2025)
by: Borges, Marcio, et al.
Published: (2025)
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
by: Venkatraman, Siddarth, et al.
Published: (2025)
by: Venkatraman, Siddarth, et al.
Published: (2025)
Diffusion prior as a direct regularization term for FWI
by: Xie, Yuke, et al.
Published: (2025)
by: Xie, Yuke, et al.
Published: (2025)
PDE-constrained Gaussian process surrogate modeling with uncertain data locations
by: Ye, Dongwei, et al.
Published: (2023)
by: Ye, Dongwei, et al.
Published: (2023)
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
by: Esche, Sebastian, et al.
Published: (2025)
by: Esche, Sebastian, et al.
Published: (2025)
Scalable Gaussian process modeling of parametrized spatio-temporal fields
by: Dama, Srinath, et al.
Published: (2026)
by: Dama, Srinath, et al.
Published: (2026)
Bayesian ECG reconstruction using denoising diffusion generative models
by: Cardoso, Gabriel V., et al.
Published: (2023)
by: Cardoso, Gabriel V., et al.
Published: (2023)
Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
by: Warrior, Kane, et al.
Published: (2026)
by: Warrior, Kane, et al.
Published: (2026)
Generalizing Score-based generative models for Heavy-tailed Distributions
by: Fassina, Tiziano, et al.
Published: (2026)
by: Fassina, Tiziano, et al.
Published: (2026)
MLPrE -- A tool for preprocessing and exploratory data analysis prior to machine learning model construction
by: Maxwell, David S, et al.
Published: (2025)
by: Maxwell, David S, et al.
Published: (2025)
Rolled Gaussian process models for curves on manifolds
by: Preston, Simon, et al.
Published: (2025)
by: Preston, Simon, et al.
Published: (2025)
Prequential posteriors
by: Sinha-Roy, Shreya, et al.
Published: (2025)
by: Sinha-Roy, Shreya, et al.
Published: (2025)
Predictive variational inference: Learn the predictively optimal posterior distribution
by: Lai, Jinlin, et al.
Published: (2024)
by: Lai, Jinlin, et al.
Published: (2024)
Expert-elicitation method for non-parametric joint priors using normalizing flows
by: Bockting, Florence, et al.
Published: (2024)
by: Bockting, Florence, et al.
Published: (2024)
Using ARIMA to Predict the Expansion of Subscriber Data Consumption
by: Nkongolo, Mike Wa
Published: (2024)
by: Nkongolo, Mike Wa
Published: (2024)
Surrogate modeling for Bayesian optimization beyond a single Gaussian process
by: Lu, Qin, et al.
Published: (2022)
by: Lu, Qin, et al.
Published: (2022)
Similar Items
-
Diffusion posterior sampling for simulation-based inference in tall data settings
by: Linhart, Julia, et al.
Published: (2024) -
Uncertainty quantification in model discovery by distilling interpretable material constitutive models from Gaussian process posteriors
by: Anton, David, et al.
Published: (2025) -
A Gibbs posterior sampler for inverse problem based on prior diffusion model
by: Giovannelli, Jean-François
Published: (2026) -
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
by: Stankewitz, Bernhard, et al.
Published: (2024) -
Estimation of partially known Gaussian graphical models with score-based structural priors
by: Sevilla, Martín, et al.
Published: (2024)