# Discretized conformal prediction for efficient distribution‐free inference

@article{Chen2017DiscretizedCP, title={Discretized conformal prediction for efficient distribution‐free inference}, author={Wenyu Chen and Kelli-Jean Chun and Rina Foygel Barber}, journal={Stat}, year={2017}, volume={7} }

In regression problems where there is no known true underlying model, conformal prediction methods enable prediction intervals to be constructed without any assumptions on the distribution of the underlying data, except that the training and test data are assumed to be exchangeable. However, these methods bear a heavy computational cost—and, to be carried out exactly, the regression algorithm would need to be fitted infinitely many times. In practice, the conformal prediction method is run by…

## 16 Citations

### Root-finding approaches for computing conformal prediction set

- Computer ScienceMachine Learning
- 2022

This work exploits the fact that conformal prediction sets are intervals whose boundaries can be efficiently approximated by classical root-finding algorithms and investigates how this approach can overcome many limitations of formerly used strategies.

### Conformalized Quantile Regression

- Computer Science, MathematicsNeurIPS
- 2019

This paper proposes a new method that is fully adaptive to heteroscedasticity, which combines conformal prediction with classical quantile regression, inheriting the advantages of both.

### Computing Full Conformal Prediction Set with Approximate Homotopy

- Computer ScienceNeurIPS
- 2019

This work proposes efficient algorithms to compute conformal prediction set using approximated solution of (convex) regularized empirical risk minimization using a new homotopy continuation technique for tracking the solution path with respect to sequential changes of the observations.

### Conformal Bayesian Computation

- Computer ScienceNeurIPS
- 2021

Using ‘add-one-in’ importance sampling, it is shown that conformal Bayesian predictive intervals are efﬁciently obtained from re-weighted posterior samples of model parameters.

### Exchangeability, Conformal Prediction, and Rank Tests

- Computer Science
- 2020

The main message of the paper is to show that similar to conformal prediction, rank tests can also be used as a wrapper around any dimension reduction algorithm.

### Training-conditional coverage for distribution-free predictive inference

- Computer Science
- 2022

This work examines the training-conditional coverage properties of several distribution-free predictive inference methods and concludes that training-Conditional coverage is achieved by some methods but is impossible to guarantee without further assumptions for others.

### Conformal predictive distributions: an approach to nonparametric fiducial prediction

- Mathematics
- 2020

The subject of this chapter is conformal predictive distributions, which represent the only approach to nonparametric fiducial prediction that is available at this time. It starts from reviewing…

### Distribution-free conditional median inference

- Mathematics, Computer ScienceElectronic Journal of Statistics
- 2021

A method is proposed based upon ideas from conformal prediction and a theoretical guarantee of coverage is established while also going over particular distributions where its performance is sharp, resulting in a lower bound on the length of any possible conditional median confidence interval.

### Conditional predictive inference for high-dimensional stable algorithms

- Computer Science, Mathematics
- 2018

These results show that despite the serious problems of resampling procedures for inference on the unknown parameters, leave-one-out methods can be successfully applied to obtain reliable predictive inference even in high dimensions.

### Conditional predictive inference for stable algorithms

- Computer Science
- 2018

These results show that despite the serious problems of resampling procedures for inference on the unknown parameters, cross validation methods can be successfully applied to obtain reliable predictive inference even in high dimensions and conditionally on the training data.

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