This paper discusses a counterpart of conformal prediction for e-values, conformal e-prediction. Conformal e-prediction is conceptually simpler and had been developed in the 1990s as a precursor of conformal prediction. When conformal prediction emerged as result of replacing e-values by p-values, it seemed to have imp…
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Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conf…
Proposes feature conformal prediction for broader application in semantic feature spaces.
problem Establishing valid prediction intervals in semantic feature spaces.
method Extends conformal prediction to semantic feature spaces using deep representation learning.
result Feature conformal prediction outperforms regular conformal prediction under mild assumptions.
Article addresses practical challenges in conformal prediction.
problem Challenges in determining, computing, and controlling conformal prediction regions.
method Proposes a quadratic-polynomial non-conformity measure.
result Allows circumventing three challenges in full conformal prediction framework.
Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation of predictive distributions to the test object at hand. In this paper we develop split-conformal and cross-conformal predictive systems tha…
Improves full conformal prediction for stochastic non-conformity measures.
problem Inability of existing conditions to guarantee full conformal prediction validity under stochastic settings.
method Introduces a new sufficient condition: Conditional Independence & Permutation Invariance in Distribution.
result Corrects the insufficient condition and provides a new sufficient condition for full conformal prediction validity.
E-values enhance conformal prediction methods.
problem Distribution-free uncertainty quantification.
method Reformulation of conformal prediction using e-values.
result E-values offer new theoretical and practical capabilities.
New method improves predictive systems with better theoretical guarantees.
problem Constructing predictive systems with out-of-sample calibration guarantees.
method Residual Distribution Predictive Systems (RDPs) that nest conformal predictive systems and offer flexibility.
result Empirically, RDPs perform competitively with conformal predictive systems and can be implemented with various regression methods.
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.
Conformal prediction sets improve human decision making by quantifying model uncertainty.
problem Humans signal uncertainty and offer alternatives when unsure, but machine learning models often lack this feature.
method Conducted a randomized controlled trial with human subjects given conformal prediction sets.
result Human accuracy improves when given conformal prediction sets compared to fixed-size prediction sets.
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.
Proposes a method to improve class-conditional conformal prediction for many classes.
problem Weak guarantees for specific classes in classification problems.
method Clusters classes with similar conformal scores and performs conformal prediction at the cluster level.
result Clustered conformal typically outperforms existing methods in class-conditional coverage and set size metrics.
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.
coverforest speeds up conformal predictions for random forests.
problem Efficient uncertainty quantification for random forest predictions.
method Optimized Python package leveraging random forest's out-of-bag scores for cross-conformal predictions.
result coverforest achieves desired coverage with faster training and prediction times.
The paper reviews exchangeability and its implications for conformal prediction and rank tests.
problem Ensuring distribution-free predictive inference in machine learning and statistics.
method Explains exchangeability and its role in conformal prediction and rank tests.
result Discovers similarities between conformal prediction and rank tests based on exchangeability.
A stability-based method selects the most desirable conformal prediction set.
problem Selecting the most desirable conformal prediction set from multiple valid sets invalidates coverage guarantees.
method A stability-based approach that ensures coverage for the selected prediction set.
result The stability-based approach maintains coverage guarantees for the selected prediction set.
Boosted conformal procedure improves prediction intervals.
problem Enhancing prediction interval properties like coverage and length.
method Gradient boosting to optimize conformity score function.
result Significant improvements in interval length and coverage.
Study robustness of split conformal prediction in data contamination setting.
problem Robustness of split conformal prediction under data contamination.
method Analyze split conformal prediction's performance in a contaminated data setting and propose a new method.
result Demonstrated the impact of corrupted data on prediction intervals' coverage and efficiency.
Paper extends conformal prediction to complex survey data.
problem Applying distribution-free prediction intervals to complex survey data.
method Design-based conformal prediction for non-exchangeable data.
result Empirical guarantees of finite-sample coverage for complex survey data.
HopCPT improves conformal prediction for time series with temporal dependencies.
problem Uncertainty quantification in time series data.
method HopCPT, a novel conformal prediction approach for time series that leverages temporal dependencies.
result HopCPT outperforms state-of-the-art methods on multiple real-world time series datasets.
A new algorithm for time series prediction intervals.
problem Non-exchangeability in time series data.
method Adaptive re-estimation of non-conformity scores.
result Significant reduction in interval width compared to existing methods.
Conformal prediction helps quantify uncertainty but its use by humans is unclear.
problem Uncertainty quantification in predictions for human decision making.
method Decision theoretic framework for evaluating predictive uncertainty.
result Conformal prediction sets and human decision making goals are in tension.
Paper uses GNN and conformal prediction for accurate edge weight prediction.
problem Predicting edge weights on graphs for various applications.
method Graph Neural Network (GNN) with conformal prediction and error reweighting.
result Our method provides better coverage and efficiency than baselines.
Adaptive Quantum Conformal Prediction improves reliability of quantum machine learning predictions.
problem Quantum machine learning lacks robust uncertainty quantification methods.
method Adaptive Conformal Inference applied to quantum conformal prediction to maintain validity over time.
result AQCP achieves target coverage levels and is more stable than standard quantum conformal prediction.
Conformal Prediction Regions match Imprecise Highest Density Regions under consonance.
problem Matching conformal prediction regions with highest density regions.
method Using consonance and the Imprecise Probability theory of clouds.
result Imprecise Highest Density Regions are equivalent to Conformal Prediction Regions under consonance.
Kandinsky conformal prediction expands conditional coverage guarantees.
problem Disparities in coverage guarantees across different subpopulations.
method Flexible handling of overlapping and fractional group memberships.
result Minimax-optimal high-probability conditional coverage bound.
A new method improves efficiency of conformal prediction for ensemble models.
problem Efficiently estimating uncertainty for ensemble models without distributional assumptions.
method Proposes a multivariate score function to merge prediction regions of individual models, reducing conservatism.
result Demonstrates more efficient prediction regions compared to existing methods.
A new conformal prediction framework for graph-valued outputs using Z-Gromov-Wasserstein distances.
problem Lack of principled uncertainty quantification for graph-valued supervised prediction.
method Proposes a conformal prediction framework using Z-Gromov-Wasserstein distances for graph-valued outputs.
result Provides distribution-free coverage guarantees for graph-valued outputs.
Combining Bayesian deep learning and split conformal prediction affects out-of-distribution coverage.
problem Improving out-of-distribution coverage in multiclass image classification.
method Combining Bayesian deep learning with split conformal prediction methods.
result Combining methods can reduce out-of-distribution coverage in some cases.
Conformal Risk Controlstat.ME
Extends conformal prediction for controlling expected risk of monotone loss functions.
problem Controlling expected risk of monotone loss functions.
method Generalizes split conformal prediction with coverage guarantee, extending to distribution shift, quantile risk, multiple, adversarial, and expectations of U-statistics.
result Tight up to an factor, with worked examples in computer vision and natural language processing.
Efficiently samples conformal boundaries in high dimensions using flows.
problem Difficulty in interpreting and using prediction sets in high-dimensional or structured output spaces.
method Flow-based approach using differentiable nonconformity scores to induce deterministic flows on the output space.
result Sampling conformal boundaries in arbitrary dimensions becomes computationally efficient and training-free.
Develops an efficient approximation for full conformal prediction regions.
problem Computing exact full conformal prediction regions is computationally infeasible.
method Generates an approximate confidence region that can be efficiently computed.
result Introduces a new notion of thickness to quantify approximation tightness.
Improved conformal prediction for better conditional coverage of classifier predictions.
problem Achieving exact conditional coverage in finite samples for prediction sets.
method Developed a variant of conformal prediction targeting coverage conditional on confidence and trust score.
result Empirically improved conditional coverage properties compared to standard conformal prediction.
This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.
problem Efficiently combining multiple conformity scores to reduce prediction set size.
method Introduces COnfidence-Level Allocation (COLA) to optimally allocate confidence levels across sets.
result COLA achieves smaller prediction sets than state-of-the-art methods while maintaining valid coverage.
New method for time series prediction with uncertainty quantification.
problem Uncertainty quantification for multi-dimensional time series predictions.
method Flow-based conformal prediction for time series.
result Significantly smaller prediction sets with target coverage.
Develop conformal prediction for dyadic regression under complex missingness.
problem Conformal prediction for dyadic regression under complex missingness mechanisms.
method Developing general technical tools and conformal prediction procedures for dyadic regression under complex missingness.
result Establishing asymptotic validity of weighted conformal prediction under a nonparametric graphon model for missingness mechanism.
The paper develops distribution-free methods for ordinal classification.
problem Constructing valid prediction sets for ordinal classification problems.
method Leveraging conformal prediction and multiple testing with FWER control.
result The proposed methods achieve satisfactory levels of marginal and class-specific conditional coverages.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
The paper extends conformal prediction to MDP trajectories for autonomous systems.
problem Ensuring reliability of autonomous systems by providing probabilistic guarantees.
method Applying conformal corrections to quantile regression prediction intervals.
result Conformal prediction intervals ensure the observed trajectory lies inside with high probability.
Improved conformalized quantile regression for adaptive prediction intervals.
problem Lack of adaptiveness in the conformal step of conformalized quantile regression.
method Cluster explanatory variables by permutation importance and apply k conformal steps.
result Improved prediction intervals are more adaptive to heteroscedasticity.
Improves GNN prediction sets with conformal prediction for node classification.
problem Lack of predictive uncertainty for GNN models.
method Adapted conformal prediction for graph data, weighting scores based on graph structure.
result Better calibrated and tighter prediction sets than naive conformal prediction.
Cross-conformal predictorsstat.ML
This note introduces the method of cross-conformal prediction, which is a hybrid of the methods of inductive conformal prediction and cross-validation, and studies its validity and predictive efficiency empirically.
Proposes a new method to minimize non-singleton predictions in conformal prediction.
problem Large prediction sets in conformal prediction are costly and inefficient.
method Introduces a new nonconformity score to minimize non-singleton sets and provides an algorithm to compute it efficiently.
result The proposed Singleton-Optimized Conformal Prediction (SOCOP) method increases singleton frequency by over 20% compared to standard scores, with minimal impact on average set size.
Max-rank improves multiple testing in conformal prediction.
problem Simultaneous testing of multiple hypotheses in scientific inquiries.
method Introduces max-rank, a novel correction for positive dependencies in simultaneous testing.
result Max-rank efficiently controls family-wise error rate and improves predictive uncertainty estimates.
COMA combines prediction sets from multiple models for online, adaptive prediction.
problem Combining multiple prediction models with uncertainty guarantees.
method Online model aggregation using weighted voting of conformal prediction sets.
result COMA retains coverage guarantees under negative correlation assumptions.
This work connects conformal prediction to information theory for uncertainty estimation.
problem Uncertainty estimation in machine learning models.
method Information theory applied to conformal prediction.
result Proven ways to upper bound intrinsic uncertainty using information theory.
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.
Unified review of Conformal Prediction theory and applications.
problem Distribution-free, non-parametric forecasting method for valid prediction sets.
method Minimal assumptions, straightforward predictions sets valid in finite sample cases.
result Unified review of Conformal Prediction theory and applications.