An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial data
Fuente:
arXiv
Guardado en:
| Autores principales: | Rambelli, Filippo, Sigrist, Fabio |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Gaussian Process Boosting
por: Sigrist, Fabio
Publicado: (2020)
por: Sigrist, Fabio
Publicado: (2020)
Principal component analysis balancing prediction and approximation accuracy for spatial data
por: Cheng, Si, et al.
Publicado: (2024)
por: Cheng, Si, et al.
Publicado: (2024)
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
por: Kündig, Pascal, et al.
Publicado: (2023)
por: Kündig, Pascal, et al.
Publicado: (2023)
An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data
por: de Reus, Pepijn, et al.
Publicado: (2024)
por: de Reus, Pepijn, et al.
Publicado: (2024)
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
por: Kündig, Pascal, et al.
Publicado: (2025)
por: Kündig, Pascal, et al.
Publicado: (2025)
The trade-off between data minimization and fairness in collaborative filtering
por: Sonboli, Nasim, et al.
Publicado: (2024)
por: Sonboli, Nasim, et al.
Publicado: (2024)
Provable and scalable quantum Gaussian processes for quantum learning
por: Jäger, Jonas, et al.
Publicado: (2026)
por: Jäger, Jonas, et al.
Publicado: (2026)
Online scalable Gaussian processes with conformal prediction for guaranteed coverage
por: Xu, Jinwen, et al.
Publicado: (2024)
por: Xu, Jinwen, et al.
Publicado: (2024)
Selecting Hyperparameters for Tree-Boosting
por: Koster, Floris Jan, et al.
Publicado: (2026)
por: Koster, Floris Jan, et al.
Publicado: (2026)
A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios
por: Kündig, Pascal, et al.
Publicado: (2024)
por: Kündig, Pascal, et al.
Publicado: (2024)
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes
por: Tsitsvero, Mikhail, et al.
Publicado: (2022)
por: Tsitsvero, Mikhail, et al.
Publicado: (2022)
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal data
por: Timonen, Juho, et al.
Publicado: (2021)
por: Timonen, Juho, et al.
Publicado: (2021)
The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems
por: Gottschling, Nina M., et al.
Publicado: (2020)
por: Gottschling, Nina M., et al.
Publicado: (2020)
Slimmable NAM: Neural Amp Models with adjustable runtime computational cost
por: Atkinson, Steven
Publicado: (2025)
por: Atkinson, Steven
Publicado: (2025)
Efficient and scalable clustering of survival curves
por: Villanueva, Nora M., et al.
Publicado: (2025)
por: Villanueva, Nora M., et al.
Publicado: (2025)
Improving the accuracy of food security predictions by integrating conflict data
por: Bertetti, Marco, et al.
Publicado: (2024)
por: Bertetti, Marco, et al.
Publicado: (2024)
Robust variable selection for spatial point processes observed with noise
por: Sturm, Dominik, et al.
Publicado: (2025)
por: Sturm, Dominik, et al.
Publicado: (2025)
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
por: Gyger, Tim, et al.
Publicado: (2024)
por: Gyger, Tim, et al.
Publicado: (2024)
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs
por: Kumar, Abhas, et al.
Publicado: (2024)
por: Kumar, Abhas, et al.
Publicado: (2024)
WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off
por: Giboulot, Eva, et al.
Publicado: (2024)
por: Giboulot, Eva, et al.
Publicado: (2024)
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
por: Risser, Mark D., et al.
Publicado: (2024)
por: Risser, Mark D., et al.
Publicado: (2024)
Edge-aware baselines for ogbn-proteins in PyTorch Geometric: species-wise normalization, post-hoc calibration, and cost-accuracy trade-offs
por: Stanković, Aleksandar, et al.
Publicado: (2025)
por: Stanković, Aleksandar, et al.
Publicado: (2025)
Adaptive sparse variational approximations for Gaussian process regression
por: Nieman, Dennis, et al.
Publicado: (2025)
por: Nieman, Dennis, et al.
Publicado: (2025)
Designing DNNs for a trade-off between robustness and processing performance in embedded devices
por: Gutiérrez-Zaballa, Jon, et al.
Publicado: (2024)
por: Gutiérrez-Zaballa, Jon, et al.
Publicado: (2024)
PDE-constrained Gaussian process surrogate modeling with uncertain data locations
por: Ye, Dongwei, et al.
Publicado: (2023)
por: Ye, Dongwei, et al.
Publicado: (2023)
NorMuon: Making Muon more efficient and scalable
por: Li, Zichong, et al.
Publicado: (2025)
por: Li, Zichong, et al.
Publicado: (2025)
Predictive, scalable and interpretable knowledge tracing on structured domains
por: Zhou, Hanqi, et al.
Publicado: (2024)
por: Zhou, Hanqi, et al.
Publicado: (2024)
Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression
por: Ascolani, Filippo, et al.
Publicado: (2025)
por: Ascolani, Filippo, et al.
Publicado: (2025)
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions
por: Viset, Frida Marie, et al.
Publicado: (2022)
por: Viset, Frida Marie, et al.
Publicado: (2022)
Trained quantum neural networks are Gaussian processes
por: Girardi, Filippo, et al.
Publicado: (2024)
por: Girardi, Filippo, et al.
Publicado: (2024)
Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage
por: Chakraborty, Anirban, et al.
Publicado: (2024)
por: Chakraborty, Anirban, et al.
Publicado: (2024)
Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing
por: Rubio-Martín, Sergio, et al.
Publicado: (2024)
por: Rubio-Martín, Sergio, et al.
Publicado: (2024)
Machine learning for accuracy in density functional approximations
por: Voss, Johannes
Publicado: (2023)
por: Voss, Johannes
Publicado: (2023)
Per-channel autoregressive linear prediction padding in tiled CNN processing of 2D spatial data
por: Niemitalo, Olli, et al.
Publicado: (2025)
por: Niemitalo, Olli, et al.
Publicado: (2025)
MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
por: Hannah, Lauren. A, et al.
Publicado: (2025)
por: Hannah, Lauren. A, et al.
Publicado: (2025)
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
por: Qu, Eric, et al.
Publicado: (2026)
por: Qu, Eric, et al.
Publicado: (2026)
Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
por: Hayakawa, Takashi, et al.
Publicado: (2025)
por: Hayakawa, Takashi, et al.
Publicado: (2025)
GPTreeO: An R package for continual regression with dividing local Gaussian processes
por: Braun, Timo, et al.
Publicado: (2024)
por: Braun, Timo, et al.
Publicado: (2024)
Likelihood approximations via Gaussian approximate inference
por: Bui, Thang D.
Publicado: (2024)
por: Bui, Thang D.
Publicado: (2024)
Gaussian process interpolation with conformal prediction: methods and comparative analysis
por: Pion, Aurélien, et al.
Publicado: (2024)
por: Pion, Aurélien, et al.
Publicado: (2024)
Ejemplares similares
-
Gaussian Process Boosting
por: Sigrist, Fabio
Publicado: (2020) -
Principal component analysis balancing prediction and approximation accuracy for spatial data
por: Cheng, Si, et al.
Publicado: (2024) -
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
por: Kündig, Pascal, et al.
Publicado: (2023) -
An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data
por: de Reus, Pepijn, et al.
Publicado: (2024) -
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
por: Kündig, Pascal, et al.
Publicado: (2025)