Conformal Prediction with Partially Labeled Data

2023/06/01 by Alireza Javanmardi, Javanmardi, Alireza, Yusuf Sale +5 · 2 citations
Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.2306.01191

openalex publication_date 2023/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise. In the setting of superset learning or learning from partial labels, a variant of weakly supervised learning, it is exactly the other way around: training data is possibly imprecise (set-valued), but the model induced from this data yields precise predictions. In this paper, we combine the two settings by making conformal prediction amenable to set-valued training data. We propose a generalization of the conformal prediction procedure that can be applied to set-valued training and calibration data. We prove the validity of the proposed method and present experimental studies in which it compares favorably to natural baselines.

Cited by

• Multivariate Conformal Prediction via Conformalized Gaussian Scoring
• Beyond Predicting Responses: Conformal Inference for Latent Distributional Parameters

Related

• Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds
• Conformal Prediction with Learned Features
• Conformal prediction without knowledge of labeled calibration data
• Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction
• A tutorial on conformal prediction