Cover Image

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Jihao Andreas, Padhy, Shreyas, Mlodozeniec, Bruno, Antorán, Javier, Hernández-Lobato, José Miguel
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

A Generative Model of Symmetry Transformations
by: Allingham, James Urquhart, et al.
Published: (2024)

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

Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
by: Antoran, Javier
Published: (2024)

Scalable Gaussian Processes with Latent Kronecker Structure
by: Lin, Jihao Andreas, et al.
Published: (2025)

Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models
by: Zhang, Fengzhe, et al.
Published: (2024)

Completed Hyperparameter Transfer across Modules, Width, Depth, Batch and Duration
by: Mlodozeniec, Bruno, et al.
Published: (2025)

SE(3) Equivariant Augmented Coupling Flows
by: Midgley, Laurence I., et al.
Published: (2023)

Iterative Importance Fine-tuning of Diffusion Models
by: Denker, Alexander, et al.
Published: (2025)

Empirical Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2026)

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

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
by: He, Jiajun, et al.
Published: (2025)

Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks
by: Oldewage, Elre T., et al.
Published: (2023)

Incremental Transformer Neural Processes
by: Mortimer, Philip, et al.
Published: (2026)

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

Transport meets Variational Inference: Controlled Monte Carlo Diffusions
by: Vargas, Francisco, et al.
Published: (2023)

A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization
by: Zhang, Jiazhao, et al.
Published: (2024)

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

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)

Probabilistic Modelling is Sufficient for Causal Inference
by: Mlodozeniec, Bruno, et al.
Published: (2025)

Combining Automated Optimisation of Hyperparameters and Reward Shape
by: Dierkes, Julian, et al.
Published: (2024)

The Kernel Manifold: A Geometric Approach to Gaussian Process Model Selection
by: Islam, Md Shafiqul, et al.
Published: (2026)

Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions
by: Hong, Steve, et al.
Published: (2025)

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

Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
by: Ha, Huong, et al.
Published: (2023)

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

Improving the Linearized Laplace Approximation via Quadratic Approximations
by: Jiménez, Pedro, et al.
Published: (2026)

Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
by: Toor, Aafan Ahmad, et al.
Published: (2025)

Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
by: Ziomek, Juliusz, et al.
Published: (2024)

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

Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs
by: Yuan, Qiwei, et al.
Published: (2025)

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

Reinforcement Learning with Function Approximation: From Linear to Nonlinear
by: Long, Jihao, et al.
Published: (2023)

Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters
by: Chernobrovkina, Daryna, et al.
Published: (2025)

Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning
by: Salt, Llewyn, et al.
Published: (2025)

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