1 code implementation • 22 Oct 2022 • Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson
Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization.
1 code implementation • 23 Mar 2022 • Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.
no code implementations • 29 Nov 2021 • Wesley Maddox, Qing Feng, Max Balandat
Physical simulation-based optimization is a common task in science and engineering.
no code implementations • 1 Jan 2021 • Gregory Benton, Wesley Maddox, Andrew Gordon Wilson
Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity.
7 code implementations • NeurIPS 2019 • Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.