Probabilistic Conformal Prediction Using Conditional Random Samples

2022-06-14 · Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei

This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples from an estimated generative model. It is efficient and compatible with either explicit or implicit conditional generative models. Theoretically, we show that PCP guarantees correct marginal coverage with finite samples. Empirically, we study PCP on a variety of simulated and real datasets. Compared to existing methods for conformal inference, PCP provides sharper predictive sets.

📄 PDF Abstract BibTeX arXiv:2206.06584

Code (1)

Zhendong-Wang/Probabilistic-Conformal-Prediction 공식 구현 pytorch

Tasks

Conformal PredictionPredictionPrediction Intervals

Criteria of efficiency for conformal prediction

2016-03-14 · Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova, Ivan Petej 외

We study optimal conformity measures for various criteria of efficiency of classification in an idealised setting. This leads to an important class of criteria of efficiency that we call probabilistic; it turns out that …

Classification Conformal Prediction General Classification Prediction

Probabilistic Conformal Prediction with Approximate Conditional Validity

2024-07-01 · Vincent Plassier, Alexander Fishkov, Mohsen Guizani, Maxim Panov 외

We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution $P_{Y \mid X}$. Existing methods, such as conformalized quantile …

Conformal Prediction Prediction quantile regression valid

Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series

2024-11-26 · Eshant English, Christoph Lippert

Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models, enabling the construction of prediction sets with finite-sample validity guarantees. While easily adaptable to non-…

Conformal Prediction Normalising Flows Prediction Time Series+1

Probabilistic Conformal Coverage Guarantees in Small-Data Settings

2025-09-18 · Petrus H. Zwart arxiv

Conformal prediction provides distribution-free prediction sets with guaranteed marginal coverage. However, in split conformal prediction this guarantee is training-conditional only in expectation: across many calibratio…

Conformal Predictive Programming for Chance Constrained Optimization

2024-02-12 · Yiqi Zhao, Xinyi Yu, Matteo Sesia, Jyotirmoy V. Deshmukh 외

We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.e., optimization problems with constraints that are functions of random variables. CPP utilizes samples …

Bilevel Optimization Conformal Prediction LEMMA