gaussian-processes

提出一种结构感知的核函数设计方法，用于高斯过程分布式端口哈密顿系统（GP-dPHS），通过张量积协方差解耦状态与空间依赖关系并融入空间异质性先验知识，实验表明在力场与轨迹重建上显著提升精度。

提出了 ELF-BO 算法，利用目标函数评估的等待时间提前从超参数后验中采样，仅在观测到目标值后重新加权，从而以标准 BO 的决策延迟实现完全贝叶斯优化。在合成函数和真实应用中，ELF-BO 匹配完全贝叶斯方法的性能且延迟更低。

Researchers at Jefferson Lab and PNNL combine Gaussian process surrogate models with reinforcement learning to automate microwave frequency tuning for dynamically polarized targets, reporting nearly 2x improvement over expert operator actions.

This paper develops two resource-efficient distributed recursive Gaussian process algorithms for multi-output regression in multi-agent systems, reducing communication overhead while maintaining estimation accuracy.

The paper investigates uncertainty quantification methods for neural surrogates in computer-aided engineering, comparing Gaussian processes, Monte Carlo dropout, and deep ensembles on industrial datasets to evaluate their performance in various engineering contexts.

This paper introduces HACK GPs, a method that uses online learning with expert advice for kernel selection in Gaussian Processes, enhancing robustness in sequential decision-making tasks such as Bayesian optimization and active learning.

This paper proposes a Bayesian decision formulation for multiobjective optimization under uncertainties, emphasizing expected hypervolume maximization with Gaussian Processes and active learning strategies.

This paper proposes an online method for warped Gaussian processes that jointly updates latent GP moments and optimizes warping parameters using exact recursive gradient computation.

This paper introduces GRAPE, a two-stage framework for query-efficient high-dimensional Bayesian optimization that refines gradient posteriors and selects update directions based on expected progress, demonstrating superior performance in adversarial attacks and large language model prompt optimization.

This paper presents methodological contributions for physics-informed machine learning under small-data constraints, using an abrasive waterjet milling dataset of 155 points. It shows that data curation choices, evaluation design, and physics integration form matter significantly, with Gaussian Process variants outperforming other models.

This paper presents PC-MCMC-CIGP, a gray-box workflow that combines spike-and-slab topology sampling with physical constraints and a Chemical-Informed Gaussian Process for reaction network discovery. The method demonstrates improved yield on styrene epoxidation and distinguishes elementary pathways from deceptive fits on a hydrogen-bromine benchmark.

This paper introduces a non-parametric multi-view Gaussian process framework for detecting machine-generated text that is robust to adversarial manipulations like paraphrasing. By combining complementary features and providing calibrated uncertainty, it outperforms existing detectors on held-out attacks.

This paper identifies the geometric mechanism behind boundary-induced acquisition bias in Gaussian processes on bounded domains, showing how kernel truncation inflates posterior variance and distorts acquisition functions independently of the objective function. The authors introduce a function-free diagnostic to quantify this bias across different acquisition classes.

The paper proposes Neural Tangent Kernel-based uncertainty quantification for deterministic deep learning weather models, achieving sharper adaptive prediction intervals during extreme events without retraining.

This paper introduces a framework for active timepoint selection to infer probability paths from sparse snapshots, using linearized optimal transport to map distributions into a tangent space for Gaussian Process modeling, thereby enabling uncertainty-aware acquisition policies.

This paper theoretically characterizes the representational capacity of Neural Process (NP) architectures, proving a strict hierarchy among Conditional, Attentive, Convolutional, and Transformer NPs, and showing that finite-dimensional latent variables do not expand representational capacity beyond the encoder.

This paper decomposes the predictive KL divergence between Gaussian process and latent neural process posteriors into three terms, providing upper bounds that characterize approximation errors and connecting representation dimension to kernel smoothness.

The paper introduces Kernel Discovery, an LLM-driven evolutionary framework for high-dimensional Bayesian optimization that searches a broader kernel space and achieves state-of-the-art results on benchmarks.

This paper provides a unified theoretical framework for pseudo observation batch Bayesian optimization, proving that Gaussian processes produce distinct batch points and that common methods like Constant Liar and Kriging Believer are instances of a single conditioning mechanism. It introduces the Structural Diversity Diagnostic (SDD) for testing surrogate compatibility and validates predictions across multiple benchmark functions and hyperparameter tuning.