47 citations · 111 across the 25 of their papers we have counts for
3 papers · 1 filter
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)…
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…
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
Shangda Yang, Vitaly Zankin, Maximilian Balandat +4
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.…