Develops methods for computing conformal invariants of submanifolds.
problem Computing conformal invariants of submanifolds.
method Direct construction of extrinsic ambient space, global invariants of conformally compact minimal submanifolds, introduction of conformal submanifold scalars.
result Derives an explicit Gauss--Bonnet--Chern-type formula and proves a rigidity result.
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.
Two novel algorithms for conformal parameterization of multiply-connected surfaces.
problem Parameterization of surfaces with holes.
method Developed efficient methods using quasi-conformal theory.
result Efficient conformal parameterization of multiply-connected surfaces.
New method improves conditional coverage of conformal prediction.
problem Improving conditional coverage in conformal prediction.
method Trainable transformation of conformity scores to improve conditional coverage.
result Highly adaptive to local data structure, outperforming existing methods.
The conformal method is a technique for finding Cauchy data in general relativity solving the Einstein constraint equations, and its parameters include a conformal class, a conformal momentum (as measured by a densitized lapse), and a mean curvature. Although the conformal method is successful in generating constant me…
Study biharmonic conformal immersions into a 3D flat space, finding new examples and classifications.
problem Characterize and classify biharmonic conformal immersions into a conformally flat 3-space.
method Characterization of totally umbilical surfaces, method to produce biharmonic immersions, classification of maps, construction of examples.
result Construct many examples of biharmonic conformal immersions, including proper immersions and isometric ones.
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.
Characterizes Kerr spacetimes using conformal methods.
problem Understanding Kerr spacetimes through conformal transformations.
method Conformally covariant characterization approach.
result Ideal characterization of Kerr spacetimes.
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 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…
Paper proposes a new method for conditional coverage in conformal prediction.
problem Lack of strong conditional coverage guarantees in existing conformal prediction methods.
method Modified non-conformity score using local approximation of conditional distribution.
result Unified framework and empirical evaluations show advantage of the new method.
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…
A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functi…
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.
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 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.
A robust conformal method for set estimation using non-conformity scores.
problem Lack of robustness in standard conformal prediction methods for outliers or heavy tails.
method Robust conformal method based on non-conformity score defined as half-mass radius.
result Empirical conformal regions converge to robust population central set.
Proposes a new method to unlearn from specific data points in conformal predictors.
problem Challenges of existing unlearning methods in conformal predictors.
method Formalizes conformal unlearning, introduces practical metrics, and presents an optimization algorithm.
result Demonstrates effective removal of targeted information while preserving utility.
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.
The paper constructs new bimetric conformal invariants using metric perturbations.
problem Developing new conformal invariants in Riemannian geometry.
method Using linear metric perturbations and conformal invariants.
result New bimetric conformal invariants on 4D manifolds are derived.
The study analyzes and benchmarks graph conformal prediction methods.
problem Uncertainty quantification in graph node classification.
method Analysis and scaling of existing graph conformal prediction methods.
result Justified recommendations for future graph conformal prediction research.
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.
Enhanced conformal methods improve validity of LLM outputs.
problem Lack of conditional validity and high false rejection rates in LLM validity guarantees.
method Adaptive conditional conformal procedure and improved scoring function differentiation.
result Demonstrated improved validity and utility on real datasets.
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.
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.
ECP method improves image classifier uncertainty sets.
problem Generating reliable uncertainty sets for deep classifiers.
method Evidential Conformal Prediction (ECP) based on EDL.
result ECP outperforms state-of-the-art methods in set size and adaptivity.
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.
Unified study of nine multi-output conformal methods with generalized scores.
problem Challenges in extending conformal prediction to multi-output problems.
method Nine conformal methods with generalized multi-output conformity scores.
result Generalized scores ensure asymptotic conditional coverage and exact finite-sample marginal coverage.
The paper extends a method for numerical conformal mappings to surfaces using Laplace-Beltrami equations.
problem Computing conformal mappings between Riemannian surfaces.
method Adapting the conjugate function method to Riemannian surfaces using hp-adaptive finite element methods. result Highly accurate numerical computations of conformal mappings on surfaces, including complex geometries.
Paper accelerates conformal prediction by using approximate leave-one-out estimators.
problem Limited computational cost for conformal prediction.
method Incorporates approximate leave-one-out estimators to accelerate conformal prediction.
result ALO-based methods achieve comparable coverage and efficiency to exact methods but with significantly reduced runtime.
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.
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.
CIR method constructs efficient prediction intervals with guaranteed coverage.
problem Efficiently constructing near-minimal prediction intervals with guaranteed coverage.
method Conditional Interquantile Regression (CIR) and CIR+ (enhanced version).
result Optimal balance between predictive accuracy and computational efficiency.
The paper studies conformal and projective structures using 2-frame bundles.
problem Understanding connections between conformal and projective structures.
method Using the dressing field method to obtain local, gauge invariant connections.
result A projective tractor bundle can be defined using the same construction as for conformal structures.
New federated conformal prediction method addresses label shift for uncertainty quantification.
problem Label shift in federated learning and its impact on uncertainty quantification.
method Quantile regression-based federated conformal prediction method with privacy constraints.
result Method provides valid coverage of prediction sets and differential privacy guarantees.
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.
Study on spin-zero rest-mass fields using conformal geometric method.
problem Wellposedness of Cauchy and Goursat problems for spin-n/2 zero rest-mass equations. method Conformal geometric method, energy equalities, partial conformal compactification.
result Proves wellposedness of Cauchy and Goursat problems and establishes field decays.
LOO-StabCP speeds up CP for multiple predictions.
problem Balancing computational efficiency and prediction accuracy in CP.
method Leave-One-Out Stable Conformal Prediction (LOO-StabCP) using algorithmic stability.
result LOO-StabCP is faster and more accurate than RO-StabCP.
Develops anytime-valid conformal and PAC prediction for streaming data.
problem Lack of guarantee in traditional conformal methods for sequential settings.
method Extends conformal and PAC prediction frameworks to handle streaming data.
result Provides anytime-valid prediction sets for sequential settings.
Develops methods to control risk in ordinal classification tasks.
problem Controlling risk in ordinal classification tasks.
method Formulated ordinal classification in conformal risk control framework, proposed loss functions and algorithms.
result Demonstrated effectiveness and analyzed differences in risk control methods.
We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme. As a result, the proposed method retains the exact, model-free validity when the da…
New method provides reliable high-confidence prediction intervals for high-impact events.
problem High-impact events require very high confidence prediction intervals, but classical methods provide uninformative intervals.
method Bridge extreme value statistics and conformal prediction to provide reliable and informative prediction intervals.
result Provides reliable and informative prediction intervals with high-confidence coverage.
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.
We study some conformally invariant integral equations using the method of moving spheres.
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.
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.
Active Kriging Monte Carlo simulation method with conformal certification for failure probability estimation
problem Failure probability estimation in structural reliability analysis
method Active learning framework with conformal prediction
result Improved uncertainty quantification and reliability of failure probability estimates
Conformal surface parameterization is useful in graphics, imaging and visualization, with applications to texture mapping, atlas construction, registration, remeshing and so on. With the increasing capability in scanning and storing data, dense 3D surface meshes are common nowadays. While meshes with higher resolution …