MOGPTK: The Multi-Output Gaussian Process Toolkit

Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects

Gaussian processes (GPs) are widely used as distributions of random effects in linear mixed models, which are fit using the restricted likelihood or the closely-related Bayesian analysis. This article addresses two problems. First, we propose tools for understanding how data determine estimates in these models, using a spectral basis approximation to the GP under which the restricted likelihood is formally identical to the likelihood for a gamma-errors GLM with identity link. Second, to examine the data's support for a covariate and to understand how adding that covariate moves variation in th…

Differentially Private Gaussian Processes

A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the differential privacy framework with the flexibility of Gaussian processes (GPs). We propose a method using GPs to provide differentially private (DP) regression. We then improve this method by crafting the DP noise covariance structure to efficiently protect the training data, while minimising the scale of the added noise. We find that this cloaking method achieves the greatest accuracy, while still providing privacy g…

TDGT：一种支持自适应GPU加速贝叶斯混合模型、扩散模型和潜在空间生成建模的表格数据生成工具包

TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling

提出TDGT工具包，集成自适应贝叶斯混合合成器（ABMS）及其变体VAE-ABMS，支持GPU加速，通过11项统计保真度指标评估，在医疗、社会经济和网络安全数据集上验证了生成质量。

Differentially Private Regression and Classification with Sparse Gaussian Processes

A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private. In this paper we build on the provable privacy guarantees of differential privacy which has been combined with Gaussian processes through the previously published \emph{cloaking method}. In this paper we solve several shortcomings of this method, starting with the problem of predictions in regions with low data density. We experiment with the use of inducing points to provide a sparse approximation and show that these can provide robust differential privacy i…

高斯过程辅助的元学习用于图像分类和目标检测模型

Gaussian Process Assisted Meta-learning for Image Classification and Object Detection Models

本文提出利用高斯过程辅助元学习，通过分析元数据优化数据采集，提升图像分类和目标检测模型性能。

Gaussian process models for periodicity detection

We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples. Our approach is based on Gaussian process regression which provides a flexible non-parametric framework for modelling periodic data. We introduce a novel decomposition of the covariance function as the sum of periodic and aperiodic kernels. This decomposition allows for the creation of sub-models which capture the periodic nature of the signal and its complement. To quantify the periodicity of the signal, we derive a periodicit…