research
∙
06/20/2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Gaussian processes are a powerful framework for quantifying uncertainty ...
share
research
∙
03/09/2023
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples
Neural kernels have drastically increased performance on diverse and non...
research
∙
10/10/2022
Sampling-based inference for large linear models, with application to linearised Laplace
Large-scale linear models are ubiquitous throughout machine learning, wi...
research
∙
05/01/2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learnin...
research
∙
06/16/2021
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Mahalanobis distance (MD) is a simple and popular post-processing method...
research
∙
06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
research
∙
07/10/2020
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
Accurate estimation of predictive uncertainty in modern neural networks ...
research
∙
06/19/2020
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Covariate shift has been shown to sharply degrade both predictive accura...
research
∙
06/17/2020