arXiv research

Improves full conformal prediction for stochastic non-conformity measures.

Article addresses practical challenges in conformal prediction.

Study shows how to detect representation extendability using conformal measures.

Measuring supernova neutrinos removes spacetime's conformal freedom.

The paper proves a unique conformal measure for Anosov groups and shows local mixing.

We study several problems concerning conformal transformation on metric measure spaces, including the Sobolev space, the differential structure and the curvature-dimension condition under conformal transformations. This is the first result about preservation of lower curvature bounds under perturbation, which is new ev…

Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execut…

New barycenters defined for hyperbolic balls, differing from spheres.

New curvature measures for 4D manifolds with corners defined and related to Gauss-Bonnet.

A robust conformal method for set estimation using non-conformity scores.

New method improves predictive systems with better theoretical guarantees.

The paper develops a method to compute the conformal barycenter in hyperbolic space.

Conformal Test Martingales can be 'blind' to significant changes in data distribution.

Let Gamma be a non-elementary Kleinian group acting on the closed n-dimensional unit ball and assume that its Poincare series converges at the exponent alpha. Let M_Gamma be the Gamma-quotient of the open unit ball. We consider certain families E = {E_1,...,E_p} of open subsets of M_Gamma such that M_Gamma minus the un…

Spectral Clustering(SC) is a prominent data clustering technique of recent times which has attracted much attention from researchers. It is a highly data-driven method and makes no strict assumptions on the structure of the data to be clustered. One of the central pieces of spectral clustering is the construction of an…

Detect spacetime curvature with event causality measurements.

New method improves conformal prediction for machine learning models.

Synthetic approach to conformal transformations in metric and Lorentzian spaces.

The paper proves rigidity and ergodicity of horospherical foliations.

Anosov groups' measures on limit sets are uniquely determined by their dimension.

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…

CPATTA uses conformal prediction for efficient test-time adaptation.

Study geometrically measures to decide if modular companions are conformally equivalent.

The paper studies proper discontinuity of actions on Weyl chamber flow spaces.

Classifies smooth metric measure spaces with two weighted Einstein representatives.

We introduce a family of conformal invariants associated to a smooth metric measure space which generalize the relationship between the Yamabe constant and the best constant for the Sobolev inequality to the best constants for Gagliardo-Nirenberg-Sobolev inequalities $\|w\r|_q \leq C\|\nabla w\|_2^θ\|w\|_p^{1-θ}$. Thes…

Uniform convergence of metrics on surfaces with bounded curvature measures proved.

General predictive models do not provide a measure of confidence in predictions without Bayesian assumptions. A way to circumvent potential restrictions is to use conformal methods for constructing non-parametric confidence regions, that offer guarantees regarding validity. In this paper we provide a detailed descripti…

Boosted conformal procedure improves prediction intervals.

New scalars measure failure of CC metrics to solve singular Yamabe problem.

Paper constructs new non-Anosov Partially Hyperbolic Geodesic flows using conformal deformations.

Proposes a new selective regression method using conformal prediction.

Study finds conditions for metrics on curved spaces.

Paper addresses travel time tomography stability and statistical inversion.

The set of osculating circles of a given curve in $\SS^3$ forms a curve in the set of oriented circles in $\SS^3$. We show that its "${\frac12}$-dimensional measure" with respect to the pseudo-Riemannian structure of the set of circles is proportional to the conformal arc-length of the original curve, which is a confor…

Random Gaussian fields on 4D Riemannian manifolds with conformal invariance.

New method calibrates classifier probabilities with guaranteed coverage.

Extend CPS to non-exchangeable settings with observation-specific permutation weights

Paper proposes a new method for conditional coverage in conformal prediction.

Develop conformal prediction for dyadic regression under complex missingness.

In this article, we prove several results about the extension to the boundary of conformal immersions from an open subset $Ω$ of a Riemannian manifold $L$, into another Riemannian manifold $N$ of the same dimension. In dimension $n \geq 3$, and when the $(n-1)$-dimensional Hausdorff measure of $\partial Ω$ is zero, we …

New algorithm quantifies uncertainty in regression models for complex data types.

We prove a Pohozaev type identity for non-linear eigenvalue equations of the Dirac operator on Riemannian spin manifolds with boundary. As an application, we obtain that the mean curvature H of a conformal immersion S^{n}-> R^{n+1} satisfies $\int \partial_X H=0$ where X is a conformal vector field on S^{n} and where t…

In this paper we construct compact manifolds with fixed boundary geometry which admit Riemannian metrics of unit volume with arbitrarily large Steklov spectral gap. We also study the effect of localized conformal deformations that fix the boundary geometry. For instance, we prove that it is possible to make the spectra…

Proposes a new method to unlearn from specific data points in conformal predictors.

Efficiently samples conformal boundaries in high dimensions using flows.

Smooth metric measure spaces have been studied from the two different perspectives of Bakry-Émery and Chang-Gursky-Yang, both of which are closely related to work of Perelman on the Ricci flow. These perspectives include a generalization of the Ricci curvature and the associated quasi-Einstein metrics, which include Ei…

New methods for quantifying insurance claim cost uncertainty using LightGBM and GLMs.