3 papers
cs.LG2026
Neural Operator Processes for Probabilistic Operator Learning under Partial Observations
Jose Miguel Lara-Rangel, Serge Guillas
Neural operators learn mappings between function spaces, but are typically developed with dense input-output training fields and fully observed inputs at inference. Many scientific…
stat.CO2026
Deep Gaussian Process Emulation with gradient Information and Sequential Design for Simulators with Sharp Variations
Yiming Yang, Deyu Ming, Serge Guillas
Deep Gaussian Processes (DGPs) compose GP layers to warp inputs, enabling improved emulation of computer models with nonstationary input-output behavior compared with ordinary GPs.…
cs.LG2025
Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
Murray Cutforth, Yiming Yang, Tiffany Fan +2
Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study eff…