3 papers
stat.ME2026
Hilbert space methods for approximating multi-output latent variable Gaussian processes
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approxim…
stat.ME2025
Latent variable estimation with composite Hilbert space Gaussian processes
Soham Mukherjee, Javier Enrique Aguilar, Marcello Zago +2
We develop a scalable class of models for latent variable estimation using composite Gaussian processes, with a focus on derivative Gaussian processes. We jointly model multiple da…
stat.ME2025
DGP-LVM: Derivative Gaussian process latent variable models
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
We develop a framework for derivative Gaussian process latent variable models (DGP-LVMs) that can handle multi-dimensional output data using modified derivative covariance function…