Imagen de Portada

Scalable Gaussian Processes with Latent Kronecker Structure

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Lin, Jihao Andreas, Ament, Sebastian, Balandat, Maximilian, Eriksson, David, Hernández-Lobato, José Miguel, Bakshy, Eytan
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
por: Lin, Jihao Andreas, et al.
Publicado: (2024)

Empirical Gaussian Processes
por: Lin, Jihao Andreas, et al.
Publicado: (2026)

Robust Gaussian Processes via Relevance Pursuit
por: Ament, Sebastian, et al.
Publicado: (2024)

Unexpected Improvements to Expected Improvement for Bayesian Optimization
por: Ament, Sebastian, et al.
Publicado: (2023)

BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability
por: Daulton, Samuel, et al.
Publicado: (2026)

Joint Composite Latent Space Bayesian Optimization
por: Maus, Natalie, et al.
Publicado: (2023)

Informed Initialization for Bayesian Optimization and Active Learning
por: Hvarfner, Carl, et al.
Publicado: (2025)

Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
por: Feng, Qing, et al.
Publicado: (2025)

Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
por: Lin, Zhiyuan Jerry, et al.
Publicado: (2026)

Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes
por: Lin, Jihao Andreas, et al.
Publicado: (2025)

Improving Iterative Gaussian Processes via Warm Starting Sequential Posteriors
por: Dong, Alan Yufei, et al.
Publicado: (2025)

LILO: Bayesian Optimization with Natural Language Feedback
por: Kobalczyk, Katarzyna, et al.
Publicado: (2025)

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
por: Lin, Jihao Andreas, et al.
Publicado: (2024)

Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning
por: Lin, Jihao Andreas
Publicado: (2025)

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
por: Lin, Jihao Andreas, et al.
Publicado: (2024)

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
por: Lin, Jihao Andreas, et al.
Publicado: (2023)

Stochastic Gradient Descent for Gaussian Processes Done Right
por: Lin, Jihao Andreas, et al.
Publicado: (2023)

Online Laplace Model Selection Revisited
por: Lin, Jihao Andreas, et al.
Publicado: (2023)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
por: Namkoong, Hongseok, et al.
Publicado: (2020)

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
por: Belakaria, Syrine, et al.
Publicado: (2024)

Graph Random Features for Scalable Gaussian Processes
por: Zhang, Matthew, et al.
Publicado: (2025)

Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling
por: Shi, Xinxing, et al.
Publicado: (2025)

Kronecker-Structured Nonparametric Spatiotemporal Point Processes
por: Xu, Zhitong, et al.
Publicado: (2026)

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning
por: Ortega, Luis A., et al.
Publicado: (2024)

A Gaussian Process View on Observation Noise and Initialization in Wide Neural Networks
por: Calvo-Ordoñez, Sergio, et al.
Publicado: (2025)

Decoupled PFNs: Identifiable Epistemic-Aleatoric Decomposition via Structured Synthetic Priors
por: Bergna, Richard, et al.
Publicado: (2026)

Long-term Causal Inference via Modeling Sequential Latent Confounding
por: Chen, Weilin, et al.
Publicado: (2025)

Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
por: Højlund, Mads Greisen, et al.
Publicado: (2026)

Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
por: Bergna, Richard, et al.
Publicado: (2025)

Leveraging Task Structures for Improved Identifiability in Neural Network Representations
por: Chen, Wenlin, et al.
Publicado: (2023)

Preventing Model Collapse in Gaussian Process Latent Variable Models
por: Li, Ying, et al.
Publicado: (2024)

Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens
por: Clarke, Ross M., et al.
Publicado: (2023)

Structured and Fast Optimization: The Kronecker SGD Algorithm
por: Song, Zhao, et al.
Publicado: (2023)

Diagnosing and fixing common problems in Bayesian optimization for molecule design
por: Tripp, Austin, et al.
Publicado: (2024)

Scalable Deep Basis Kernel Gaussian Processes
por: Zhu, Yunqin, et al.
Publicado: (2025)

Scalable Linearized Laplace Approximation via Surrogate Neural Kernel
por: Ortega, Luis A., et al.
Publicado: (2026)

There Was Never a Bottleneck in Concept Bottleneck Models
por: Almudévar, Antonio, et al.
Publicado: (2025)

Transformed Latent Variable Multi-Output Gaussian Processes
por: Jiang, Xiaoyu, et al.
Publicado: (2026)

Weighted-Sum of Gaussian Process Latent Variable Models
por: Odgers, James, et al.
Publicado: (2024)

Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers
por: Rodriguez-Fernandez, P., et al.
Publicado: (2023)