Entropy Reweighted Conformal Classification

Rui Luo, Nicolo Colombo
7/24/2024
cs.LG

No confident code implementation found for this paper.

Abstract

Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Does confidence calibration improve conformal prediction?

90% match1 repo2024

Conformal Correction for Efficiency May be at Odds with Entropy

89% match2025

An Information Theoretic Perspective on Conformal Prediction

86% match2024

Sparse Activations as Conformal Predictors

85% match1 repo22025

Trustworthy Classification through Rank-Based Conformal Prediction Sets

85% match2024

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels

85% match1 repo32025

Cite this paper

@article{luo2024entropy,
  title  = {Entropy Reweighted Conformal Classification},
  author = {Rui Luo and Nicolo Colombo},
  year   = {2024},
  eprint = {2407.17377},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.17377v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2407.17377.svg)](https://codeofpaper.com/paper/2407.17377)

Discussion