Recently I am seeing the topic of Conformal Prediction to be very trendy on social media and research. Awesome Conformal Prediction
But what is the main difference between conformal prediction and uncertainty estimation?
Asked
Active
Viewed 319 times
0
-
It seems to me that conformal prediction is a simple distribution-free form of uncertainty estimation. – Lucas Morin Jan 17 '23 at 15:47
1 Answers
1
Conformal Prediction is a distribution-free statistically rigorous uncertainty uncertainty quantification framework in the form of prediction sets for classification and prediction intervals for regression (Vovk, V.; Gammerman, A.; and Shafer, G. 2005. Algorithmic Learning in a Random World. Berlin, Heidelberg:Springer-Verlag. ISBN 0387001522., and Shafer 2005).
Bayesian method provides a natural and principled way for modeling uncertainty. In Deep learning, epistemic uncertainty concerns the probabilistic estimation of model parameters. There are number of approaches have been developed to measure uncertainty in neural networks, such as approx. Bayesian deep learning techniques such as Monte-Carlo dropout (Gal, Y.; and Ghahramani, Z. 2016), DropWeights (Ghoshal, B.; and Tucker, A. 2019), and an ensemble of deep models (Lakshminarayanan, Pritzel, and Blundell 2017).
Biraja Ghoshal
- 11
- 1
-
I dont see how this answers the question. First part is the definition using both terms. second is unrelated – Carlos Mougan Feb 04 '23 at 08:27
-
Probabilistic approach is considered as the most principled approach to uncertainty estimation. Conformal prediction is a distribution-free uncertainty estimation. In classification task conformal prediction generates predictive set instead of single prediction whereas approx. Bayesian deep learning, UQ estimates aleatoric and epistemic uncertainty in neural network. – Biraja Ghoshal Feb 12 '23 at 10:52
-
This comment is better than the answer. Do you want to update the answer? – Carlos Mougan Feb 13 '23 at 11:05