Bayesian Optimization with Conformal Coverage Guarantees

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.

Active Learning Conformal Prediction +3

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

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.

Denoising

Optimizing High-Dimensional Physics Simulations via Composite Bayesian 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.

Rethinking Parameter Counting: Effective Dimensionality Revisited

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.

Model Selection

A Simple Baseline for Bayesian Uncertainty in Deep Learning

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.

Bayesian Inference Transfer Learning