5.7k citations · 6.2k across the 9 of their papers we have counts for
9 papers
Variational Bayesian Last Layers
James Harrison, John Willes, Jasper Snoek
We introduce a deterministic variational formulation for training Bayesian last layer neural networks. This yields a sampling-free, single-pass model and loss that effectively impr…
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples
Ben Adlam, Jaehoon Lee, Shreyas Padhy +2
Neural kernels have drastically increased performance on diverse and nonstandard data modalities but require significantly more compute, which previously limited their application…
Plex: Towards Reliability using Pretrained Large Model Extensions
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry +23
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. P…
Pre-training helps Bayesian optimization too
Zi Wang, George E. Dahl, Kevin Swersky +6
Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive real-world functions. Contrary to a common belief that BO is suited to optimizing…
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Setareh Ariafar, Justin Gilmer, Zachary Nado +3
Black box optimization requires specifying a search space to explore for solutions, e.g. a d-dimensional compact space, and this choice is critical for getting the best results at…
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
Kevin Swersky, David Duvenaud, Jasper Snoek +2
In practical Bayesian optimization, we must often search over structures with differing numbers of parameters. For instance, we may wish to search over neural network architectures…