3 papers
cs.LG2026
Transformed Latent Variable Multi-Output Gaussian Processes
Xiaoyu Jiang, Xinxing Shi, Sokratia Georgaka +2
Multi-Output Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks when applied to datasets with…
q-bio.QM2026
Multimodal Spatial Omics: From Data Acquisition to Computational Integration
Esra Busra Isik, Yusuf Hakan Usta, Haozhe Liu +5
Recent developments in spatial omics technologies have enabled the generation of high dimensional molecular data, such as transcriptomes, proteomes, and epigenomes, within their sp…
cs.LG2025
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
Xiaoyu Jiang, Sokratia Georgaka, Magnus Rattray +1
The Multi-Output Gaussian Process is is a popular tool for modelling data from multiple sources. A typical choice to build a covariance function for a MOGP is the Linear Model of C…