Combining Split Conformal Prediction and Bayesian Deep Learning for Out-of-Distribution Coverage

Grunnleggende konsepter

The author explores how combining split conformal prediction with Bayesian deep learning impacts out-of-distribution coverage, highlighting the importance of model confidence on calibration datasets.

Sammendrag

The study investigates the effects of combining split conformal prediction with Bayesian deep learning on out-of-distribution coverage. It emphasizes the impact of model confidence levels on calibration datasets and provides practical recommendations for improving machine learning system safety. The research evaluates various inference techniques and offers insights into when conformal prediction may enhance or diminish out-of-distribution coverage.

Statistikk

SGD is overconfident at both error tolerances, ENS is overconfident at 0.01 error tolerance, MFV, SGHMC, and LAPLACE are all underconfident.
Average set sizes vary across methods: SGD (1.20), ENS (1.34), MFV (2.97), SGHMC (1.87), LAPLACE (1.59) at 0.05 error tolerance.
For MedMNIST experiment: SGD credible set coverage - 94% at 0.05 error tolerance, 97% at 0.01 error tolerance.

Sitater

"We suggest that application of both methods in certain scenarios may be counterproductive and worsen performance on out-of-distribution examples."
"Understanding the interaction between predictive models and conformal prediction is crucial for safe deployment of machine learning systems."

Viktige innsikter hentet fra

On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning

by Paul Scemama... klokken arxiv.org 03-08-2024

https://arxiv.org/pdf/2311.12688.pdf

Dypere Spørsmål

How can we predict when conformal prediction will enhance or reduce out-of-distribution coverage?

Conformal prediction's impact on out-of-distribution coverage can be predicted based on the calibration dataset and the behavior of the predictive model. If a model is overconfident, meaning its credible sets do not reach the desired coverage on the calibration dataset, then applying conformal prediction is likely to increase out-of-distribution coverage. This is because conformal methods will create larger average set sizes to achieve better marginal coverage. Conversely, if a model is underconfident and its credible sets exceed the desired coverage on the calibration dataset, using conformal prediction may decrease out-of-distribution coverage as it will result in smaller average set sizes.

What are the implications of using uncertainty-aware methods like MFV and SGHMC without conformal prediction?

Using uncertainty-aware methods like Mean-Field Variational Inference (MFV) and Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) without conformal prediction can provide good out-of-distribution coverage. These methods aim to represent epistemic uncertainty accurately, which helps in achieving better calibration and thus improved performance on out-of-distribution examples. However, there might be variations in how these models exhibit uncertainty on such inputs. For instance, while MFV tends to be underconfident by overestimating aleatoric uncertainty, SGHMC provides more calibrated outputs.

How can engineers create safer machine learning systems based on these findings?

Engineers can create safer machine learning systems by understanding when to use Bayesian deep learning models with or without conformal prediction based on their specific requirements. If strong guarantees of coverage are needed for both in-distribution and out-of-distribution data, considering Bayesian deep learning along with conformal prediction could provide those guarantees. By being aware of scenarios where certain modeling approaches may impact out-of-distribution coverage positively or negatively, engineers can make informed decisions about deploying machine learning systems safely.