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Found 5 papers, 3 papers with code
Bayesian Optimization with Conformal Prediction Sets
Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization.
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization
Physical simulation-based optimization is a common task in science and engineering.
Rethinking Parameter Counting: Effective Dimensionality Revisited
Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity.
A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.
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