Print  Close

Posterior conformal prediction

Abstract Number:

2760 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Yao Zhang (1), Emmanuel Candes (1)

Institutions:

(1) Stanford University, N/A

Co-Author:

Emmanuel Candes  
Stanford University

First Author:

Yao Zhang  
Stanford University

Presenting Author:

Yao Zhang  
N/A

Abstract Text:

Conformal prediction is a distribution-free method to quantify uncertainties in machine learning predictions. It can transform point predictions into marginally valid prediction intervals under the assumption of data exchangeability. However, many predictive tasks require conditional coverage, which is known to be unachievable unless we use intervals with infinite expected length. In this article, we propose a new data-adaptive weighting method to approximate the conditional coverage guarantee. Our method can improve the conditional coverage rate of conformal prediction without increasing the interval length excessively. Furthermore, we extend our method to other applications in predictive inference.

Keywords:

Conformal Prediction|Predictive Inference|Distribution-free inference| | |

Sponsors:

Section on Statistical Learning and Data Science

Tracks:

Machine Learning

Can this be considered for alternate subtype?

No

Are you interested in volunteering to serve as a session chair?

No

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand