gaussian-process

The paper proposes Finite-Library Input-Warped Bayesian Optimization (FLIWBO), which selects input warps from a finite library to adapt input geometry for improved optimization efficiency while maintaining high-probability convergence guarantees, outperforming standard GP-UCB in various benchmarks.

This paper proposes a calibration method for product-of-experts Gaussian process models to correct overestimated posterior variances, enhancing uncertainty quantification and scalability for large datasets.

MorphoGP is a Gaussian process framework for predicting equilibrium beach profiles under tidal influence, achieving improved performance over baseline models through category-specific experts.

This paper introduces KappaSharp, a method for condition-number shaping in Preferential Bayesian Optimization that corrects the ill-conditioned Hessian from isolated pairwise comparisons, showing up to +10.9% improvement over the standard PairedGP/EUBO baseline on 11 benchmarks including plasma medicine controller tuning.

This paper proposes a neuro-symbolic closed-loop architecture for laser powder bed fusion, where an in-loop ontology couples symbolic reasoning with statistical learning to control melt pool depth and eliminate overhang dross. Feasibility is demonstrated via a surrogate calibrated to the NIST AM-Bench benchmark.

This paper evaluates four machine learning models for discrete choice modeling in policy preference elicitation, using Monte Carlo experiments and a real energy policy case study to assess performance under individual heterogeneity and choice complexity.

This paper introduces ALAS, a flexible Gaussian Process kernel family that learns the stability parameter from data to adapt smoothness, capturing both smooth trends and sharp irregularities, with a separable variant for higher dimensions and theoretical guarantees on information gain.

This paper presents BattVAE-GP, a hybrid physics-probabilistic framework that combines a Variational Autoencoder with a sparse multitask Gaussian Process to generate and interpolate long-horizon battery degradation trajectories with uncertainty estimates, enabling efficient surrogate modeling for lithium-ion battery health prediction.

This paper presents a sparse Gaussian process framework for quantile regression that uses a Laplace approximation for posterior inference and variance-based mechanisms for adaptive inducing-input placement and data acquisition.

This paper proposes active shot allocation strategies for quantum kernel estimation in Gaussian process regression, deriving pair-level sensitivities to guide non-uniform shot budgets and demonstrating significant improvements in test RMSE over uniform allocation on benchmarks.

This book develops an effective theory for deep neural networks, showing that their predictions are nearly-Gaussian and governed by the depth-to-width ratio, and introduces representation group flow to analyze signal propagation and learning dynamics.

Proposes REEF-GP, a post-hoc uncertainty quantification framework that fits a Gaussian process to the residuals of a frozen neural operator using its internal embeddings, enabling geometry-aware and calibrated uncertainties at low cost.

This paper proposes structure-preserving neural surrogates for partial differential equations that integrate Gaussian process regression to provide tractable uncertainty quantification, enabling real-time simulation with closed-form error estimates.

This paper introduces a general acceleration mechanism for multi-objective Bayesian optimisation that uses Gaussian process predictive gradients as auxiliary signals to augment existing acquisition functions, enabling faster convergence to the global Pareto set under limited evaluation budgets.

Proposes Gaussian process latent factor regression (GPLFR) for low-data, high-dimensional output problems, demonstrating it with a spatially resolved emulator of global climate models for rocky exoplanets.

The article explains how to use Bayesian modeling with Gaussian processes to handle spatial data where the coordinates are observed with error, using a dataset of uranium and vanadium concentrations from Walker Lake as an example.

This paper formalizes trust calibration for agentic tool use as a preference learning problem, using Gaussian processes and Bayesian optimization to decide when an AI agent's actions should be autonomous or require human approval.