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miscellaneous-11

Prediction Intervals

3260 papers • 126 benchmarks • 313 datasets

A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.

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Use these libraries to find prediction-intervals-11 models and implementations

StatMixedML/XGBoostLSS

2 papers 500

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Friedman1

aGender

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Most implemented papers

Distribution-Free Predictive Inference for Regression

Jing Lei, M. G'Sell, A. Rinaldo, R. Tibshirani, L. Wasserman•Wed Apr 13 2016

A general framework for distribution-free predictive inference in regression, using conformal inference, which allows for the construction of a prediction band for the response variable using any estimator of the regression function, and a model-free notion of variable importance, called leave-one-covariate-out or LOCO inference.

1066

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Introduction Benchmarks Datasets Subtasks Libraries Papers

2 papers 249

0

Conformalized Quantile Regression

E. Candès, Yaniv Romano, Evan Patterson•Tue May 07 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.

801 0

Conformal Inference for Online Prediction with Arbitrary Distribution Shifts

E. Candès, Isaac Gibbs•Tue Aug 16 2022

A novel procedure with provably small regret over all local time intervals of a given width is developed by modifying the adaptive conformal inference (ACI) algorithm to contain an additional step in which the step-size parameter of ACI's gradient descent update is tuned over time.

138 0

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Stefan Bauer, B. Scholkopf, Alexander Neitz, Giambattista Parascandolo•Tue Jul 31 2018

It is shown that there are prediction tasks for which the model can gain both computational efficiency and prediction accuracy by allowing the model to make predictions at a sampling rate which it can choose itself.

38 0

Conformal prediction interval for dynamic time-series

Chen Xu, Yao Xie•Sat Oct 17 2020

A method to build distribution-free prediction intervals for time-series based on conformal inference that wraps around any ensemble estimator to construct sequential prediction intervals is developed, which is easy to implement, scalable to producing arbitrarily many prediction intervals sequentially, and well-suited to a wide range of regression functions.

162 0

RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

Cansu Alakus, Denis Larocque, A. Labbe•Mon Jun 14 2021

A comprehensive R package that integrates 16 methods to build prediction intervals with random forests and boosted forests is developed, which shows that the proposed method is very competitive and, globally, outperforms competing methods.

3 0

Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap

Dan Saattrup Smart, Carlos Mougan•Wed Jan 26 2022

Non-parametric bootstrapped uncertainty estimates and SHAP values are used to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deteri- oration when target labels are not available.

19 0

Adaptive Conformal Predictions for Time Series

Margaux Zaffran, Aymeric Dieuleveut, Olivier F'eron, Y. Goude, J. Josse•Mon Feb 14 2022

This work argues that Adaptive Conformal Inference (ACI), developed for distribution-shift time series, is a good procedure for time series with general dependency, and proposes a parameter-free method, AgACI, that adaptively builds upon ACI based on online expert aggregation.

190 0

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

Michael I. Jordan, T. Alshaabi, Anastasios Nikolas Angelopoulos, Stephen Bates, Yaniv Romano, Amit Kohli, J. Malik, S. Upadhyayula•Wed Feb 09 2022

Uncertainty quantification techniques are developed with rigorous statistical guarantees for image-to-image regression problems that derive uncertainty intervals around each pixel that are guaranteed to contain the true value with a user-specified confidence probability.

110 0

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Prediction Intervals | State-of-the-Art