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Found 10 papers, 2 papers with code
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
The Multi-Output Gaussian Process is is a popular tool for modelling data from multiple sources.
Longitudinal prediction of DNA methylation to forecast epigenetic outcomes
Our model is trained on a birth cohort of children with methylation profiled at ages 0-4, and we demonstrated that the status of methylation sites for each child can be accurately predicted at ages 5-7.
Machine Learning for a Low-cost Air Pollution Network
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used.
Differentially Private Regression and Classification with Sparse Gaussian Processes
We experiment with the use of inducing points to provide a sparse approximation and show that these can provide robust differential privacy in outlier areas and at higher dimensions.
Adversarial Vulnerability Bounds for Gaussian Process Classification
To protect against this we devise an adversarial bound (AB) for a Gaussian process classifier, that holds for the entire input domain, bounding the potential for any future adversarial method to cause such misclassification.
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila
Since the post-transcriptional regulation of Drosophila depends on spatiotemporal interactions between mRNAs and gap proteins, proper physically-inspired stochastic models are required to study the link between both quantities.
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging
We test the TDP in 2nd, 4th and 6th rank HOT fields.
Generalized Wishart processes for interpolation over diffusion tensor fields
The aim of this work is to develop a methodology for DTI interpolation that enhance the spatial resolution of DTI fields.
Kernels for Vector-Valued Functions: a Review
Kernel methods are among the most popular techniques in machine learning.
