World PulseNow
Newsletter

Multi-view Bayesian optimisation in an input-output reduced space for engineering design

arXiv — stat.ML•Thursday, December 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • A recent study introduces a multi-view Bayesian optimisation approach that enhances the efficiency of Gaussian process models in engineering design by identifying a low-dimensional latent subspace from input and output data. This method utilizes probabilistic partial least squares (PPLS) to improve the scalability of Bayesian optimisation techniques in complex design scenarios.
  • This development is significant as it addresses the limitations of traditional Gaussian processes, which struggle with high-dimensional design variables, thereby potentially transforming engineering design processes and making them more efficient and cost-effective.
  • The advancement aligns with ongoing efforts to enhance Bayesian optimisation methods, particularly in high-dimensional spaces, as seen in various studies exploring scalable neural network-based approaches and adaptive strategies for Gaussian processes. These developments reflect a growing recognition of the need for robust optimisation techniques in diverse applications.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps

LucidQuery AI

Combines diffusion reasoning with autoregressive LLM for advanced AI analysis.

AI & DataTry the app

Hypertune

Optimize machine learning models with automated hyperparameter tuning and experiment tracking.

Business & ProductivityTry the app

Augmeta

AI peers for collaborative problem-solving and enhanced team productivity.

AI & DataTry the app
Continue Readings
Fast Gaussian Process Approximations for Autocorrelated Data
arXiv — stat.ML3 days ago
Fast Gaussian Process Approximations for Autocorrelated Data
PositiveArtificial Intelligence
A new paper has been published addressing the computational challenges of Gaussian process models when applied to autocorrelated data, highlighting the risk of temporal overfitting if autocorrelation is ignored. The authors propose modifications to existing fast Gaussian process approximations to work effectively with blocked data, which helps mitigate these issues.
Read full article
via arXiv — stat.ML
Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
arXiv — cs.LG3 days ago
Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
PositiveArtificial Intelligence
Recent advancements in Gaussian process (GP) regression have led to the development of an adaptive pruning strategy aimed at enhancing the robustness and efficiency of saddle point searches in high-dimensional energy landscapes. This approach utilizes geometry-aware optimal transport measures and a permutation-invariant metric to optimize computational overhead and improve stability during hyperparameter optimization.
Read full article
via arXiv — cs.LG
Laplace Approximation For Tensor Train Kernel Machines In System Identification
arXiv — stat.ML3 days ago
Laplace Approximation For Tensor Train Kernel Machines In System Identification
PositiveArtificial Intelligence
A new study introduces a Bayesian tensor train kernel machine that utilizes Laplace approximation to enhance Gaussian process regression in system identification. This method addresses scalability issues by estimating the posterior distribution over a selected tensor train core while employing variational inference for hyperparameter precision. Experiments indicate that core selection is largely independent of tensor train ranks and feature structures, achieving significantly faster training times.
Read full article
via arXiv — stat.ML