99 citations · 219 across the 24 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
cs.LG2024
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat +1
A key task in AutoML is to model learning curves of machine learning models jointly as a function of model hyper-parameters and training progression. While Gaussian processes (GPs)…
cs.LG2024
Robust Gaussian Processes via Relevance Pursuit
Sebastian Ament, Elizabeth Santorella, David Eriksson +3
Gaussian processes (GPs) are non-parametric probabilistic regression models that are popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates. H…