UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation
- URL: http://arxiv.org/abs/2306.16549v2
- Date: Sun, 14 Jul 2024 03:50:02 GMT
- Title: UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation
- Authors: Jianqing Fan, Jiawei Ge, Debarghya Mukherjee,
- Abstract summary: We introduce a novel strategy called Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA)
This technique excels in efficiently aggregating multiple prediction intervals while maintaining a small average width of the prediction band and ensuring coverage.
It is validated through its application to synthetic data and two real-world datasets in finance and macroeconomics.
- Score: 9.387706860375461
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Uncertainty quantification in prediction presents a compelling challenge with vast applications across various domains, including biomedical science, economics, and weather forecasting. There exists a wide array of methods for constructing prediction intervals, such as quantile regression and conformal prediction. However, practitioners often face the challenge of selecting the most suitable method for a specific real-world data problem. In response to this dilemma, we introduce a novel and universally applicable strategy called Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA). This technique excels in efficiently aggregating multiple prediction intervals while maintaining a small average width of the prediction band and ensuring coverage. UTOPIA is grounded in linear or convex programming, making it straightforward to train and implement. In the specific case where the prediction methods are elementary basis functions, as in kernel and spline bases, our method becomes the construction of a prediction band. Our proposed methodologies are supported by theoretical guarantees on the coverage probability and the average width of the aggregated prediction interval, which are detailed in this paper. The practicality and effectiveness of UTOPIA are further validated through its application to synthetic data and two real-world datasets in finance and macroeconomics.
Related papers
- Multi-model Ensemble Conformal Prediction in Dynamic Environments [14.188004615463742]
We introduce a novel adaptive conformal prediction framework, where the model used for creating prediction sets is selected on the fly from multiple candidate models.
The proposed algorithm is proven to achieve strongly adaptive regret over all intervals while maintaining valid coverage.
arXiv Detail & Related papers (2024-11-06T05:57:28Z) - High-dimensional prediction for count response via sparse exponential weights [0.0]
This paper introduces a novel probabilistic machine learning framework for high-dimensional count data prediction.
A key contribution is a novel risk measure tailored to count data prediction, with theoretical guarantees for prediction risk using PAC-Bayesian bounds.
Our results include non-asymptotic oracle inequalities, demonstrating rate-optimal prediction error without prior knowledge of sparsity.
arXiv Detail & Related papers (2024-10-20T12:45:42Z) - Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution.
Our method consistently outperforms existing approaches in terms of conditional coverage.
arXiv Detail & Related papers (2024-07-01T20:44:48Z) - Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift [9.387706860375461]
A distribution shift occurs when the underlying data-generating process changes, leading to a deviation in the model's performance.
The prediction interval serves as a crucial tool for characterizing uncertainties induced by their underlying distribution.
We propose methodologies for aggregating prediction intervals to obtain one with minimal width and adequate coverage on the target domain.
arXiv Detail & Related papers (2024-05-16T17:55:42Z) - Regression Trees for Fast and Adaptive Prediction Intervals [2.6763498831034043]
We present a family of methods to calibrate prediction intervals for regression problems with local coverage guarantees.
We create a partition by training regression trees and Random Forests on conformity scores.
Our proposal is versatile, as it applies to various conformity scores and prediction settings.
arXiv Detail & Related papers (2024-02-12T01:17:09Z) - Prediction-Powered Inference [68.97619568620709]
Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.
The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients.
Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning.
arXiv Detail & Related papers (2023-01-23T18:59:28Z) - Efficient and Differentiable Conformal Prediction with General Function
Classes [96.74055810115456]
We propose a generalization of conformal prediction to multiple learnable parameters.
We show that it achieves approximate valid population coverage and near-optimal efficiency within class.
Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly.
arXiv Detail & Related papers (2022-02-22T18:37:23Z) - Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
We describe two types of forecasting problems: regular grid-based and graph-based.
We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory.
Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
arXiv Detail & Related papers (2021-05-25T14:35:46Z) - Private Prediction Sets [72.75711776601973]
Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy.
We present a framework that treats these two desiderata jointly.
We evaluate the method on large-scale computer vision datasets.
arXiv Detail & Related papers (2021-02-11T18:59:11Z) - Robust Validation: Confident Predictions Even When Distributions Shift [19.327409270934474]
We describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions.
We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population.
An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it.
arXiv Detail & Related papers (2020-08-10T17:09:16Z) - AutoCP: Automated Pipelines for Accurate Prediction Intervals [84.16181066107984]
This paper proposes an AutoML framework called Automatic Machine Learning for Conformal Prediction (AutoCP)
Unlike the familiar AutoML frameworks that attempt to select the best prediction model, AutoCP constructs prediction intervals that achieve the user-specified target coverage rate.
We tested AutoCP on a variety of datasets and found that it significantly outperforms benchmark algorithms.
arXiv Detail & Related papers (2020-06-24T23:13:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.