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.
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48 results for “Inductive Conformal Prediction”
Study explores efficient data division for ICPs.
problem Efficiently dividing limited development data for ICPs.
method Experiments with training, calibration, and test data divisions.
result Allows overlap between training and calibration sets improves efficiency.
Stacked conformal predictionstat.ML
Stacked conformal prediction simplifies model validation.
problem Validating stacked predictive models efficiently.
method Meta-learner at the top of a stacked ensemble for approximate marginal validity.
result The method achieves approximate marginal validity without a separate calibration sample.
ICP improves text infilling and POS tagging with valid confidence sets.
problem Statistical reliability of machine learning predictions.
method Inductive conformal prediction algorithms for text infilling and POS tagging.
result Valid set-valued predictions with small size for real-world applications.
ICP provides interval predictions with high confidence coverage.
problem High-risk settings where true output must be highly probable.
method ICP is a distribution-free, model-agnostic algorithm.
result ICP outputs prediction intervals with high coverage probability.
The paper proposes a method for valid multi-target regression predictions.
problem Valid multi-variate predictions for multi-target regression.
method Copula functions applied to deep neural networks for inductive conformal prediction.
result The proposed method ensures efficiency and validity for multi-target regression problems.
This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed fi…
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.
This paper explores conformal prediction in the learning under privileged information (LUPI) paradigm. We use the SVM+ realization of LUPI in an inductive conformal predictor, and apply it to the MNIST benchmark dataset and three datasets in drug discovery. The results show that using privileged information produces va…
Study shows NCCP can replace CP for ACI in non-exchangeable data.
problem Ensuring reliable prediction under non-exchangeability.
method Demonstrates NCCP as a valid alternative to CP for ACI.
result NCCP offers computational advantages and comparable predictive efficiency.
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.
The conformalClassification package implements Transductive Conformal Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification problems. Conformal Prediction (CP) is a framework that complements the predictions of machine learning algorithms with reliable measures of confidence. TCP gives results wi…
JANET improves time series prediction with adaptive uncertainty regions.
problem Time series data's lack of exchangeability and multi-step prediction challenges.
method Proposes JANET, a framework for joint adaptive prediction regions with controlled error rates.
result Demonstrates superior performance in multi-step prediction tasks across diverse datasets.
Proposes a method to estimate drug sensitivity uncertainty using deep regression forests.
problem Lack of confidence intervals in deep learning models for critical tasks.
method Uses Deep Regression Forests to estimate variance and uncertainty for drug sensitivity prediction.
result Improves efficiency and coverage of uncertainty estimates for drug sensitivity predictions.
PAC-Bayes theory improves ICP efficiency and coverage.
problem Inefficient and unreliable uncertainty estimates in deep learning models.
method PAC-Bayes theory to optimize model and score function parameters for efficient and reliable prediction sets.
result Generalization bounds on coverage and efficiency of optimized prediction sets.
The paper presents a method to compute trusted confidence bounds for LECs in CPS.
problem Non-transparent predictions of LECs make CPS safety challenging.
method Inductive Conformal Prediction (ICP) and Triplet Network architecture.
result Efficient real-time computation of trusted confidence bounds.
New methods improve anomaly detection with reduced false positives.
problem Effective anomaly detection with controlled error rates.
method Leave-one-out-, bootstrap-, and cross-conformal anomaly detection methods.
result Improved anomaly detection with reduced false positives.
Classification with Reject Option: Distribution-free Error Guarantees via Conformal Predictionstat.ML
A method for making predictions with a reject option using conformal prediction.
problem Uncertainty in machine learning predictions, especially when models are unsure.
method Formalizing ML with reject option, using conformal prediction for distribution-free error guarantees.
result Theoretical guarantees on error rate for prediction sets with distribution-free validity.
Valid prediction sets for dynamic graphs using conformal prediction.
problem Uncertainty quantification in dynamic graph neural networks.
method Dynamic graph unfolding for input to GNNs, combined with conformal prediction.
result Valid prediction sets with minimal assumptions, even in challenging scenarios.
Method cleans noisy training labels for biomedical data.
problem Accurately labeling biomedical data is challenging.
method Reliability-based training data cleaning with inductive conformal prediction.
result Significant enhancements in classification performance across multiple tasks.
We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we p…
Proposes approximating computationally expensive explainability techniques using conformal regression.
problem Computational expense of score-based explainability techniques limits their applicability in time-critical contexts.
method Uses conformal prediction framework to approximate SHAP and TreeSHAP explanations.
result Significantly improves execution time and produces tight validity guarantees.
Extends positive mass theorem to arbitrary dimensions using a new inductive scheme.
problem Overcoming singularities in the Schoen-Yau proof for arbitrary dimensions.
method Inductive scheme combining shielding principle, conformal blow-up, and Cheeger-Naber bound.
result Proof of positive mass theorem in arbitrary dimensions.
The present paper describes a way to relate Martin boundaries on spaces of varying topology. This enables us to approach some detailed inductive analysis of the eigenfunctions of conformal Laplacians on minimal hypersurfaces near their singularities. This can directly be used resp. translated to understand the way how …
Efficient method for multi-label text classification with large label sets.
problem Computational inefficiency in handling a large number of unique labels.
method Proposed efficient LP-ICP approach to reduce computational complexity.
result The contextualised-based classifier outperforms non-contextualised ones and achieves state-of-the-art performance.
DEAL model predicts links for new nodes with only attribute info.
problem Predicting links for new nodes with only attribute info.
method DEAL model with two encoders and alignment mechanism.
result DEAL significantly outperforms existing methods on inductive link prediction.
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not explicitly capture the compositional logical rules underlying the knowledge graph, and they are limited …
The paper detects adversarial examples in LECs for regression in CPS using variational autoencoder.
problem Detecting adversarial examples in learning-enabled cyber-physical systems (CPS).
method Inductive conformal prediction using a variational autoencoder regression model.
result The method effectively detects adversarial examples with a short delay in an emergency braking system simulation.
Novel approach uses inductive biases for semiconductor etching.
problem Significant violations of physics in etching process predictions.
method Introduced deep learning model with inductive biases.
result Fits measurements faster and follows physical behavior.
COMPASS improves uncertainty quantification for medical segmentation metrics.
problem Uncertainty quantification for medical segmentation metrics is crucial for clinical decision-making.
method COMPASS leverages deep neural network inductive biases to generate efficient, metric-based conformal prediction intervals.
result COMPASS produces significantly tighter intervals than traditional conformal prediction methods on medical image segmentation tasks.
A well known result of Da Rios and Levi-Civita says that a closed planar curve is elastic if and only if it is stationary under the localized induction (or smoke ring) equation, where stationary means that the evolution under the localized induction equation is by rigid motions. We prove an analogous result for surface…
OTI extends OTP for inductive semi-supervised learning.
problem Inductive semi-supervised learning for out-of-sample data.
method Optimal transport-based approach extended to inductive tasks.
result OTI outperforms state-of-the-art methods in experiments.
Study shows different trajectory prediction models generalize better under OoD conditions.
problem Comparing trajectory prediction models' robustness across different datasets.
method Training models on Argoverse 2 and testing on Waymo Open Motion, and vice versa, with various augmentation strategies.
result Smallest model with highest inductive bias performs best in OoD generalization.
Paper proposes an inductive RGCN for few-shot link prediction in drug-repurposing.
problem Predicting rare interactions in drug-repurposing for novel diseases.
method Proposes an inductive RGCN to learn relation embeddings for few-shot learning.
result Significantly outperforms state-of-the-art models in few-shot learning tasks.
In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities. Moreover, they are black-box models …
NPGNN improves graph link prediction by adapting to new graphs.
problem Inductive link prediction in graphs with limited training data.
method Meta-learning with graph neural networks (NPGNN).
result NPGNN outperforms state-of-the-art models in real-world graphs.
R2N learns interpretable rules and literals from numerical features.
problem Lack of expressive vocabulary in rule-based decision models.
method Relational Rule Network (R2N) learns literals and rules end-to-end.
result Learned literals improve prediction accuracy and rule conciseness.
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…
Integrative conformal p-values for powerful out-of-distribution testing with labeled outliersstat.ME
Novel conformal methods test out-of-distribution data with labeled outliers.
problem Testing whether new data comes from the same distribution as a reference.
method Integrative conformal p-values re-weight standard p-values using dependent side information.
result The methods outperform standard conformal p-values in simulations and applications.
Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This ma…
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…
Transformers learn to use induction heads or shortcuts based on data diversity.
problem How data diversity influences the behavior of transformers.
method Gradient-based training of a single-layer transformer on a minimal task.
result Data diversity steers transformers toward induction heads or shortcuts.
Study links neural network inductive bias, feature learning, and generalization on Boolean functions.
problem Understanding how neural networks learn and generalize on Boolean data.
method End-to-end analysis of depth-2 discrete fully connected networks and DNF formulas, using Monte Carlo learning.
result Predictable training dynamics and interpretable features emerge, linking inductive bias and generalization.
Enhanced ICM ensemble detects concept drift better with novel betting functions.
problem Addressing Concept Drift in machine learning models.
method Refined ICM approach with improved betting functions and base estimators.
result The ensemble approach outperforms previous methods on benchmark datasets.
Transformers tend to learn more symmetric functions in sequence data.
problem Understanding inductive bias in Transformers with infinitely over-parameterized models.
method Analyzing Transformers in the Gaussian process limit, using representation theory of the symmetric group.
result Transformers are biased towards more permutation symmetric functions, and this can be quantitatively predicted.
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.
We propose an inductive matrix completion model without using side information. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive, since the learned embeddings cannot generalize …
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…