The paper defines flexible domains for minimal surfaces in Euclidean spaces and explores their properties.
problem Understanding the flexibility of domains in Euclidean spaces for minimal surfaces.
method Investigates the concept of flexibility in terms of minimal surfaces contained in domains.
result Defines flexible domains and shows how they can be approximated by minimal immersions.
Infinite-dimensional geometry: completeness and geodesics in Hilbert manifolds.
problem Failure of Hopf--Rinow theorem in Hilbert manifolds.
method Investigates conformal flexibility and completeness properties in infinite-dimensional settings.
result Conformal class of metrics on Hilbert manifolds contains complete representatives.
TorsionNet uses reinforcement learning to efficiently generate conformers of flexible molecules.
problem Efficiently generating diverse and representative conformer sets for flexible molecules.
method Sequential conformer search technique based on reinforcement learning under the rigid rotor approximation, trained via curriculum learning.
result TorsionNet outperforms chemoinformatics methods by 4x on large branched alkanes and several orders of magnitude on biopolymer lignin.
Two conformal methods create flexible conditional predictive bands without strong assumptions.
problem Achieving asymptotic conditional coverage without strong dependence assumptions.
method Flexible distribution-free conditional predictive bands using density estimators.
result Methods achieve better control of conditional coverage and smaller length than previous methods.
VSPS creates flexible prediction regions for multi-target regression with guaranteed coverage.
problem Uncertainty quantification in multi-target regression with complex distributions.
method Conditional normalizing flows with conformal calibration to identify dense regions.
result VSPS produces smaller, more informative prediction regions with robust coverage guarantees.
Flexible method for estimating frequencies in large datasets using sketching.
problem Estimating frequencies in very large datasets efficiently and accurately.
method Data-adaptive conformal inference method based on a smaller sketch of data.
result Proves validity of frequentist confidence intervals under data exchangeability.
Backward Conformal Prediction offers flexible control over prediction set sizes while ensuring coverage guarantees.
problem Providing reliable prediction sets with controlled sizes in applications like medical diagnosis.
method Defines a rule that constrains prediction set sizes based on observed data, adapting coverage levels.
result Maintains computable coverage guarantees while ensuring interpretable, well-controlled prediction set sizes.
New method converts p-values to e-values for more efficient CP and aggregation.
problem Limitations of existing p-to-e calibrators in CP setting.
method Proposes a novel P2E calibrator for set-preserving calibration.
result Significant efficiency gains over existing p-to-e calibrators.
MOPI optimizes flexible set-valued mappings to achieve superior shape adaptivity in conformal prediction.
problem Challenges in achieving valid conditional coverage in conformal prediction.
method Minimax Optimization Predictive Inference (MOPI) framework that optimizes over a flexible class of set-valued mappings.
result MOPI achieves superior shape adaptivity and maintains a principled connection to mean squared coverage error.
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.
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.
We show the flexibility of the metric entropy and obtain additional restrictions on the topological entropy of geodesic flow on closed surfaces of negative Euler characteristic with smooth non-positively curved Riemannian metrics with fixed total area in a fixed conformal class. Moreover, we obtain a collar lemma, a th…
Conf-Gen applies uncertainty quantification to generative models.
problem Uncertainty quantification for unsupervised generative models.
method Adapts CRC to generative tasks, relaxing theoretical assumptions.
result Demonstrates flexibility and correctness of AI agent outputs.
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.
Researchers prove spaces of positive scalar curvature metrics are contractible with symmetry.
problem Contractibility of spaces of invariant positive scalar curvature metrics.
method Combining equivariant Morse theory with conformal deformations and local flexibility properties.
result Spaces of invariant positive scalar curvature metrics are contractible.
Proposes a method to apply conformal prediction to probabilistic time series forecasting models.
problem Obtaining accurate prediction regions for multi-step time series forecasting with probabilistic models.
method Conformalises conditional normalising flows to generate potentially disjoint prediction regions.
result Improves predictive efficiency in time series forecasting with multimodal distributions.
The article explores constructing biharmonic and conformal biharmonic maps to spheres.
problem Constructing biharmonic and conformal biharmonic maps to spheres.
method Geometric algorithm to render harmonic maps biharmonic or conformally biharmonic.
result Explicit critical points for conformal-biharmonic maps between spheres are found.
Consider a smooth closed surface M of fixed genus ⩾2 with a hyperbolic metric σ of total area A. In this article, we study the behavior of geometric and dynamical characteristics (e.g., diameter, Laplace spectrum, Gaussian curvature and entropies) of nonpositively curved smooth metrics with total area …
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.
New method for probabilistic prediction sets with conditional validity.
problem Marginal coverage guarantee of existing methods.
method Combines conformal methods with approximate conditional validity.
result Consistently outperforms existing approaches in conditional coverage.
Differentially private conformal prediction improves statistical efficiency.
problem Quantifying uncertainty in private data analysis.
method Introducing differential conformal prediction and developing Differentially Private Conformal Prediction (DPCP).
result DPCP produces tighter prediction sets than existing private split conformal approaches.
This paper proves a deformation circle pattern theorem, which gives a complete description of those circle patterns with interstices in terms of the combinatorial type, the exterior intersections angles and the conformal structures of interstices. As results, the surface version of Rivin's theorem and the approximation…
ECI improves time series prediction uncertainty quantification by smoothing miscoverage error.
problem Challenges in uncertainty quantification for time series prediction due to temporal dependence and distribution shift.
method Error-quantified Conformal Inference (ECI) by smoothing quantile loss function and introducing adaptive feedback scale.
result ECI achieves valid miscoverage control and tighter prediction sets than existing methods.
Paper approximates continuous functions on Jordan arcs using conformal minimal immersions.
problem Approximating continuous functions on Jordan arcs using minimal immersions.
method Conformal minimal immersions and directed holomorphic curves.
result Continuous functions on Jordan arcs can be approximated by conformal minimal immersions.
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.
Optimizes minimum-volume prediction sets for multivariate regression.
problem Lack of efficient methods for multivariate conformal prediction.
method Optimization-driven framework for minimum-volume covering sets.
result Efficient and informative prediction sets with tight coverage.
Proposes a new model for estimating individual treatment effects.
problem Estimating individual treatment effects from observational data is challenging.
method Integrates diffusion modeling and conformal inference with propensity score and covariate approximation.
result Establishes rigorous theoretical guarantees and demonstrates competitive performance.
A neural network method tackles high-dimensional diffeomorphic mapping problems.
problem High-dimensional diffeomorphic mapping struggles with the curse of dimensionality.
method Combines variational principles with quasi-conformal theory for accurate, bijective mappings.
result Validated accuracy, robustness, and effectiveness in complex registration scenarios.
Systematic review of conformal inference for treatment effect estimation.
problem Uncertainty quantification in treatment effect estimation.
method Conformal prediction methods for treatment effect estimation.
result Current state-of-the-art conformal prediction methods identified and described.
Adaptive coverage policies improve conformal prediction accuracy.
problem Fixed coverage levels in traditional conformal prediction lead to uninformative predictions.
method Optimizes adaptive coverage policy using a neural network trained on leave-one-out calibration.
result Adaptive coverage policies produce more informative and flexible prediction sets.
MLCC clusters data at multiple significance levels, detecting anomalies without distributional assumptions.
problem Clustering and anomaly detection in data with unknown distributions.
method Hierarchical, conformal prediction-based clustering.
result MLCC automatically selects cluster number and detects anomalies robustly.
Paper introduces conformal prediction for reliable uncertainty quantification in landmark localization.
problem Systematic underestimation of total predictive uncertainty in landmark localization.
method Conformal prediction framework for multi-output regression, generating flexible prediction regions.
result Methods outperform existing approaches in validity and efficiency across 2D and 3D datasets.
JAPAN uses flow-based models to create adaptive prediction areas with better coverage guarantees.
problem Inadequate prediction areas from existing conformal prediction methods, especially for multimodal distributions.
method JAPAN employs density-based conformity scores using flow-based models to construct context-adaptive prediction areas.
result JAPAN produces more accurate and context-adaptive prediction areas compared to existing methods.
CPPS selects portfolios using conformal prediction for better returns.
problem Optimizing portfolio returns with predictive models and uncertainty.
method Conformal prediction framework for portfolio selection.
result CPPS outperforms simpler strategies in delivering superior returns.
Smoothly approximates embeddings in Lorentzian manifolds.
problem Approximating embeddings in Lorentzian manifolds.
method C^0 approximation of embeddings.
result Approximated embeddings can be made smooth.
Proposes non-exchangeable conformal risk control for better uncertainty bounds.
problem Handling non-exchangeable data in black-box models for better risk control.
method Leverages and extends split conformal prediction and monotone loss function approaches.
result Allows controlling expected value of any monotone loss function for non-exchangeable data.
The paper analyzes how conformal prediction works with contaminated reference data.
problem The impact of contamination on the validity and power of conformal prediction methods.
method The paper analyzes the impact of contamination on the validity of conformal methods and proposes a data-cleaning framework to enhance power.
result The proposed data-cleaning framework can effectively enhance power while maintaining type-I error control.
The paper provides high-probability bounds on false discovery proportions in conformal inference.
problem Existing methods fail to provide high-probability bounds on the realized false discovery proportion.
method Constructing a high-probability envelope for the empirical distribution function of null conformal p-values by sampling from their joint distribution.
result Establishes finite-sample, distribution-free upper bounds on the FDP that hold simultaneously over all possible rejection thresholds.
The paper proposes a method to construct well-calibrated prediction sets for correlated target variables.
problem Constructing well-calibrated prediction sets for correlated target variables.
method The method uses vine copulas to estimate the joint cumulative distribution function of non-conformity scores and improves the asymptotic efficiency of the quantile estimate.
result The method guarantees asymptotically exact coverage and competitive efficiency on real-world regression problems.
We study quasi-isometry invariants of Gromov hyperbolic spaces, focussing on the l_p-cohomology and closely related invariants such as the conformal dimension, combinatorial modulus, and the Combinatorial Loewner Property. We give new constructions of continuous l_p-cohomology, thereby obtaining information about the l…
SACP aggregates nonconformity scores from multiple predictors to create more efficient uncertainty sets.
problem Combining predictive uncertainties from multiple models for efficient and reliable uncertainty quantification.
method SACP (Symmetric Aggregated Conformal Prediction) aggregates nonconformity scores using a flexible symmetric aggregation function.
result SACP consistently improves efficiency and often outperforms state-of-the-art model aggregation baselines.
OptCS optimizes model selection after conformal inference, controlling FDR and power loss.
problem Challenges in model selection for conformal inference, especially when limited labeled data and many model choices are available.
method OptCS framework that allows valid statistical testing after flexible data-driven model optimization, using novel multiple testing procedures.
result Valid conformal p-values constructed despite substantial data reuse, maintaining FDR control.
A new method for time-series data provides guaranteed coverage and adapts to non-exchangeable data.
problem Guaranteed coverage for time-series data prediction intervals.
method Sequential Conformalized Density Regions (SCDR) using quantile random forest.
result SCDR achieves guaranteed asymptotic coverage and outperforms existing methods in simulations.
Deep neural networks improve free energy calculations for peptide conformations.
problem Challenges in developing suitable mappings for free energy perturbation.
method Adapted machine learning approach to train deep neural networks for mapping between Boltzmann distributions.
result Accurate free energy differences calculated between thermodynamic states with spring centers separated by 1 Å and sometimes 2 Å.
New method attributes feature uncertainty in ML models using cooperative game theory.
problem Lack of feature-level uncertainty attribution in explainable AI.
method Proposes a novel, model-agnostic uncertainty attribution method using cooperative game theory and conformal prediction.
result Demonstrates improved runtime efficiency and practical utility in real-world applications.
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.
The paper introduces algorithms for uncertainty quantification in metric spaces.
problem Uncertainty quantification in regression models defined on metric spaces.
method Proposes conformal and kNN prediction algorithms for metric spaces.
result Both algorithms provide finite-sample guarantees and improve local coverage calibration.
This study compares two methods for uncertainty estimation in CNNs, finding Conformal Prediction more reliable.
problem CNNs often overestimate uncertainty, leading to unreliable predictions.
method Bayesian approximation via Monte Carlo Dropout and Conformal Prediction.
result Conformal Prediction produces more reliable uncertainty estimates than Monte Carlo Dropout.