Author

Thomas D. Nielsen

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#machine learning Preprint Oct 2026

Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores

While contrastive learning is widely used to learn deterministic representations, it does not inherently provide estimates of uncertainty. Post-hoc uncertainty estimation methods, such as the Linearized Laplace approximation (LLA), offer a way to quantify uncertainty without retraining the encoder. However, we show tha...

F. J. Sáez-Maldonado, Luis A. Ortega, Andrés R. Masegosa et al. · 0 citations

#machine learning Preprint Oct 2026

Generalized Matheron Variational Implicit Processes

Implicit-process priors specify distributions over functions through sample-forward mechanisms such as Bayesian neural networks and stochastic simulators, but their function-space densities are typically unavailable. We introduce Generalized Matheron Variational Implicit Processes (GMVIP), a pathwise variational family...

Luis A. Ortega, Andrés R. Masegosa, Thomas D. Nielsen · 0 citations