Proposes a method to create prediction intervals for neural networks using cross-validation.
problem Lack of prediction intervals for neural networks.
method k-fold cross-validation to construct conformal prediction intervals.
result Proposed method produces narrower intervals with similar coverage compared to SC method.
Self-calibrating conformal prediction improves interval efficiency and offers a practical alternative.
problem Improving the reliability and uncertainty quantification of machine learning predictions.
method Combines Venn-Abers calibration and conformal prediction for binary and regression problems.
result Improves interval efficiency through model calibration and offers practical alternatives.
Valid inference from data and predictions.
problem Valid statistical inference with machine learning predictions.
method Framework for valid inference using machine learning predictions.
result Valid confidence intervals without assumptions on predictions.
This paper improves prediction intervals for heteroskedastic regression.
problem Adaptive prediction intervals for heteroskedastic regression.
method Normalized and Mondrian conformal prediction methods.
result Conditional validity of chosen conformal predictors related to data-generating assumptions.
CPTD improves prediction intervals in time series regression with cross-sectional data.
problem Constructing valid prediction intervals in time series regression with a cross-section.
method Conformal Prediction with Temporal Dependence (CPTD) for post-hoc, light-weight approach.
result CPTD maintains cross-sectional validity while improving longitudinal coverage.
New method uses interval-based metric to validate prediction uncertainty in machine learning.
problem Validation of prediction uncertainty in machine learning regression tasks is unreliable due to heavy-tailed distributions.
method Shift from variance-based metrics to interval-based Prediction Interval Coverage Probability (PICP).
result PICP method more quickly and reliably tests prediction intervals than variance-based metrics.
AutoCP automates the construction of accurate prediction intervals.
problem Creating valid and accurate prediction intervals for machine learning models.
method AutoML framework that optimizes prediction interval length for better accuracy and less conservatism.
result AutoCP significantly outperforms benchmark algorithms in constructing accurate prediction intervals.
Paper develops new conformal prediction methods for sum or average of unknown labels.
problem Uncertainty quantification in joint distributions of random variables.
method Introduces novel conformal prediction methods for sum or average of unknown labels.
result Validates the proposed method for sum or average of unknown labels under permutation invariant assumptions.
New method narrows prediction intervals for individual treatment effects.
problem Insufficiently conservative prediction intervals for individual treatment effects.
method Conformal inference using conditional density estimates.
result Narrower prediction intervals compared to existing methods.
Posterior conformal prediction improves prediction interval validity for subgroups.
problem Marginal and conditional prediction interval validity for subgroups.
method Modeling conditional nonconformity score distribution as a mixture of cluster distributions.
result PCP produces tighter prediction intervals, especially for well-represented clusters.
Recent studies have shown that tuning prediction models increases prediction accuracy and that Random Forest can be used to construct prediction intervals. However, to our best knowledge, no study has investigated the need to, and the manner in which one can, tune Random Forest for optimizing prediction intervals { thi…
Proposes a method to create shorter, more accurate prediction intervals.
problem Challenges in achieving both conditional validity and interval efficiency in complex settings.
method Uses a conformal-style calibration method for neural network responses, adjusting to empirical PIT distribution.
result Demonstrates better conditional calibration and shorter intervals than existing methods.
Locally Valid and Discriminative prediction intervals for deep learning models.
problem Efficient and theoretically sound uncertainty quantification for deep learning models.
method Locally Valid and Discriminative prediction intervals (LVD) using kernel regression.
result Locally Valid and Discriminative prediction intervals (LVD) offer better performance and scalability compared to existing methods.
Normalizing Flows improve prediction interval efficiency in CP.
problem Inefficient prediction intervals in CP due to non-uniform error distribution.
method Train a Normalizing Flow to optimize the distance metric between errors and inputs.
result Optimized prediction intervals are more efficient and valid.
SPACR trains uncertainty-aware regressors directly within a single pass, improving efficiency and validity.
problem Training uncertainty-aware regressors while maintaining efficiency and validity.
method Joint optimization of efficiency and validity during training.
result SPACR consistently provides tighter intervals and better coverage-efficiency trade-offs compared to standard CP and DOICR.
Cross-validation estimates model performance on unseen data, not training data.
problem Understanding how cross-validation estimates prediction error and its limitations.
method Analyzing linear models and popular prediction error estimates, introducing nested cross-validation.
result Cross-validation estimates the average prediction error of models fit on other unseen training sets, not the model at hand.
A new AMP-based method speeds up conformal prediction intervals.
problem Computational cost in building high-dimensional prediction intervals.
method Approximate Message Passing (AMP) to accelerate full conformal prediction.
result AMP-based method produces valid prediction intervals orders of magnitude faster.
LPCI provides valid prediction intervals for longitudinal data.
problem Current conformal prediction methods for time series data lack cross-sectional coverage when applied to longitudinal datasets.
method Modeling residual data as a quantile fixed-effects regression problem, constructing prediction intervals with a trained quantile regressor.
result LPCI achieves valid cross-sectional coverage and outperforms existing benchmarks in terms of longitudinal coverage rates.
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions. Despite this appeal, existing conformal methods can be unnecessarily conservative because they form intervals of constant or weakly varying length across the…
This work reviews and evaluates methods for predicting prediction intervals in regression problems.
problem Calibration of prediction intervals in regression problems.
method Four classes of methods: Bayesian, ensemble, direct interval estimation, and conformal prediction.
result Conformal prediction can be used as a general calibration procedure.
New method makes CP intervals locally adaptive using trainable transformations.
problem Making Conformal Prediction intervals locally adaptive.
method Defining a trainable change of variables φX(A) that depends on object attributes X. result Locally adaptive prediction intervals with guaranteed marginal validity and variable sizes.
The paper improves off-policy evaluation in contextual bandits using conformal prediction.
problem Quantifying the performance of a target policy using data from a different behavior policy.
method Proposes a novel algorithm based on a PAC-valid conformal prediction framework to construct probably approximately correct prediction intervals.
result Establishes PAC-type bounds on coverage, improving theoretical guarantees.
Random Forests provide interpretable prediction intervals with theoretical guarantees.
problem Lack of uncertainty estimates in machine learning point predictions.
method Out-of-Bag procedure for generating parametric and non-parametric prediction intervals.
result Proposed prediction intervals deliver correct coverage rates and narrow lengths.
Constructs tail-specific prediction intervals for financial applications
problem Financial applications require strict control on the left tail
method Extends classical conformal frameworks to provide explicit tail-specific guarantees
result Improved directional calibration in skewed data
Two-step conformal prediction method for adaptive bounding box uncertainties in multi-object detection.
problem Quantifying predictive uncertainty for multi-object detection in safety-critical applications.
method Developed a two-step conformal prediction approach to propagate uncertainty in predicted class labels into bounding box uncertainties, ensuring coverage for incorrectly classified objects.
result Desired coverage levels are satisfied with practically tight predictive uncertainty intervals on real-world datasets.
New method combines HQR and WACI for better time series prediction intervals.
problem Challenges in creating reliable prediction intervals for time series forecasting.
method Combining Heteroscedastic Quantile Regression (HQR) with Width-Adaptive Conformal Inference (WACI).
result Combined approach meets or surpasses typical benchmarks for validity and efficiency.
This work improves adaptive conformal prediction using self-supervised learning.
problem Improving the adaptability of conformal prediction intervals.
method Train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores.
result Empirically demonstrates the benefit of additional information in improving the efficiency (width), deficit, and excess of conformal prediction intervals.
New online conformal prediction methods minimize strongly adaptive regret and achieve near-optimal coverage.
problem Uncertainty quantification in online settings with changing data distributions.
method Developed new online conformal prediction methods that minimize strongly adaptive regret.
result Achieve near-optimal strongly adaptive regret and approximately valid coverage.
Develops conformalized prediction intervals for bounded continuous outcomes.
problem Predicting continuous outcomes within bounded ranges, especially when models are misspecified.
method Conformal prediction intervals based on transformation regression models, accounting for heteroscedasticity and asymmetry.
result Valid finite-sample coverage confirmed in simulations and real data applications.
Novel U-learning method for predicting continuous outcomes from high-dimensional data.
problem Challenges in making valid inferences on predictions from high-dimensional inputs.
method U-learning via combinatory multi-subsampling for ensemble predictions and confidence intervals.
result Valid inferences on predictions from Lasso and neural networks.
Skew-adaptive method improves prediction intervals for regression.
problem Improving prediction intervals for regression models, especially in cases of skewness and varying scales.
method Develops a skew-adaptive extension of split conformal prediction using an asymmetric interval family and gauge approach.
result Preserves marginal validity and adapts to local scale and skewness, with efficiency gains over existing methods.
CoCP optimizes prediction intervals by jointly learning center and radius, improving efficiency and coverage.
problem Inefficient conformal prediction intervals under heteroscedasticity and skewness.
method Co-optimization framework that learns center and radius through alternating optimization steps.
result CoCP yields consistently shorter intervals and state-of-the-art conditional coverage diagnostics.
New method for valid prediction intervals in counterfactual outcomes with runtime confounding.
problem Valid prediction intervals for counterfactual outcomes under runtime confounding.
method Debiased machine learning framework grounded in semiparametric efficiency theory.
result Prediction intervals achieve desired coverage rates with faster convergence compared to standard methods.
ICP improves prediction intervals for continuous outcomes at lower computational cost.
problem Systematic bias in point predictions that undermines their use in decision-making.
method Develops Isotonic Conformal Prediction (ICP) framework to decouple calibration from prediction-set construction.
result SICP and TICP procedures match SC-CP coverage at lower computational cost.
This work challenges the assumption that shorter conformal prediction intervals are always better.
problem The conventional evaluation of conformal prediction metrics (coverage and interval length) may not fully capture the quality of predictions.
method The Prejudicial Trick (PT) is introduced, which probabilistically returns either a null interval or a longer one to maintain valid coverage while potentially reducing interval length.
result The Prejudicial Trick can yield deceptively shorter intervals without compromising coverage, but introduces practical vulnerabilities.
SCIENCE improves prediction intervals for individual causal effects.
problem Wide prediction intervals limit practical utility of causal inference.
method Surrogate-assisted conformal inference for efficient individual causal effects.
result SCIENCE produces more efficient prediction intervals for individual causal effects.
Develops confidence intervals for ECE, a measure of model calibration.
problem Ensuring the calibration of probabilistic predictions in machine learning models.
method Develops confidence intervals for the ℓ2 Expected Calibration Error (ECE), considering top-1-to-k calibration. result Shows asymptotic normality and different convergence rates for calibrated and miscalibrated models, developing methods to construct valid confidence intervals.
UTOPIA aggregates multiple prediction intervals efficiently.
problem Constructing optimal prediction intervals for various real-world data problems.
method UTOPIA is a universally trainable strategy using linear or convex programming.
result UTOPIA constructs prediction intervals with small average width and high coverage probability.
COP improves online conformal prediction by incorporating data patterns, leading to tighter prediction sets.
problem Overly conservative prediction sets in online conformal prediction methods when data distribution shifts.
method Conformal Optimistic Prediction (COP) incorporating estimated cumulative distribution function of non-conformity scores.
result COP produces tighter prediction sets with valid coverage guarantees, outperforming other methods.
New method for accurate uncertainty estimation in deep learning predictions.
problem Insufficient methods for assessing prediction uncertainty in deep learning.
method Valid non-parametric bootstrap method for deep neural networks.
result Accurate confidence intervals and simultaneous confidence bands for survival data.
Improves random forest quantile estimation and prediction intervals.
problem Excessive bias in quantile estimates from random forests.
method Minimizes quantile coverage loss (QCL) by adjusting RF parameters.
result QCL-tuned RFs produce more accurate and narrower prediction intervals.
CONTINA provides adaptive confidence intervals for traffic demand prediction.
problem Uncertainty in future traffic demand predictions and the need for valid confidence intervals.
method Adaptive confidence interval method that adjusts based on deployment errors.
result Valid confidence intervals with shorter lengths and theoretical coverage guarantee.
Improves statistical inference using machine learning predictions with imputed data.
problem Invalid statistical inference due to machine learning prediction errors.
method Bootstrap confidence intervals for nonuniform samples and arbitrary imputed features.
result Valid confidence intervals without assumptions on machine learning model quality.
Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot be pooled. We here consider the regression case and propose a method …
The paper introduces a method to assess machine translation quality with confidence intervals.
problem Evaluating the uncertainty and quality of machine translation.
method Utilizes conformal predictive distributions to produce prediction intervals with guaranteed coverage.
result The method outperforms a baseline on six language pairs in terms of coverage and sharpness.
Neural network learns kernel functions for survival analysis and prediction intervals.
problem Predicting survival times for individuals based on similar training subjects.
method Develops a neural network framework to learn kernel functions for kernel survival analysis and uses these to construct valid prediction intervals.
result Neural network survival estimators are competitive with existing methods and provide valid prediction intervals.
LCMQR improves prediction intervals by adapting to local heteroscedasticity.
problem Efficient and adaptive prediction intervals for local heteroscedasticity.
method LCMQR combines multi-quantile information with kernel-based localization.
result LCMQR constructs tighter intervals than prior methods, especially in heterogeneous environments.
A method for constructing tight prediction intervals for multiple numerical outputs.
problem Constructing tight prediction intervals for multiple related numerical outputs.
method A novel coordinate-wise standardization procedure that makes residuals comparable across output dimensions, estimating suitable scaling parameters using calibration data.
result The method produces tighter prediction intervals than existing baselines while maintaining valid simultaneous coverage.