1.7k citations · 3.2k across the 31 of their papers we have counts for
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Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Jonathan Wenger, Kaiwen Wu, Philipp Hennig +3
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable app…
Theoretical Limitations of Ensembles in the Age of Overparameterization
Niclas Dern, John P. Cunningham, Geoff Pleiss
Classic ensembles generalize better than any single component model. In contrast, recent empirical studies find that modern ensembles of (overparameterized) neural networks may not…
Approximation-Aware Bayesian Optimization
Natalie Maus, Kyurae Kim, Geoff Pleiss +3
High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse…