arXiv Machine Learning
Aug 13

A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression

arXiv:2608. 11917v1 Announce Type: new Abstract: Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outputs are observed at different inputs.

By Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko, \.Ismail \c{S}en\"oz, Wouter M. Kouw
Hugging Face Trending Papers
Jul 20

Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS

This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments. The joint distribution of the covariates may vary across environments, whereas the latent structure is decomposed into invariant factors with shared loadings and heterogeneous factors with environment-specific loadings.