arXiv research

Unique solutions found for Plateau problems in smooth and continuous calibrations.

We equip many non compact non simply connected surfaces with smooth Riemannian metrics whose isoperimetric profile is smooth, a highly non generic property. The computation of the profile is based on a calibration argument, a rearrangement argument, the Bol-Fiala curvature dependent inequality, together with new result…

Hyperplanes, hyperspheres and hypercylinders in $\Bbb R^n$ with suitable densities are proved to be weighted minimizing by a calibration argument. Also calibration method is used to prove a weighted minimal hypersurface is weighted area-minimizing locally.

Solves Plateau's Problem in Heisenberg group for graphs.

We propose nonparametric methods for individual calibration in regression models.

Simple algorithm achieves distance to calibration error of at most 2√T+1.

Develops forecast hedging for improved calibration of forecasts.

Based on a calibration argument, we prove a Bernstein type theorem for entire minimal graphs over Gauss space $\mathbb{G}^n$ by a simple proof.

Framework calibrates ML models for risk control in various tasks.

Let $(M, \om)$ be a symplectic manifold, endowed with a compatible almost complex structure J and the associated metric g . For any p \in {1, 2, ... (dim M)/2} the form $\Om := \frac{\om^p}{p!}$ is a calibration. More generally, dropping the closedness assumption on $\om$, we get an almost hermitian manifold $(M, \om, …

For product manifolds, cohomologically calibrated affine connections are geometrically irreducible.

The paper addresses decision making with partially calibrated forecasts, offering a robust approach.

The paper studies the distance from calibration in sequential prediction, proving upper and lower bounds.

The paper extends conformal risk control to be valid with high probability over a growing calibration dataset.

Minimal networks minimize length and mass in certain configurations.

The paper studies Pansu spheres in a sub-Riemannian 3-sphere and their area-minimizing properties.

Study optimal healthcare spending under Epstein-Zin preferences for longevity.

Improved non-squeezing theorem for calibrated geometries proved.

New method calibrates false detection rates in sequential change detection.

Alternative perspective on mean-field LIBOR market model, maintaining practicality and applicability.

Curves in higher dimensions are either affine or have super-Euclidean energy growth.

The paper proves monotonicity formulas for minimal connections and their applications.

In this paper we present a new method to compute the first-order approximation of the price of derivatives on futures in the context of multiscale stochastic volatility of Fouque \textit{et al.} (2011, CUP). It provides an alternative method to the singular perturbation technique presented in Hikspoors and Jaimungal (2…

We consider closed-form approximations for European put option prices within the Heston and GARCH diffusion stochastic volatility models with time-dependent parameters. Our methodology involves writing the put option price as an expectation of a Black-Scholes formula and performing a second-order Taylor expansion aroun…

In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and su…

It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as…

CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.

Proposes new method for calibrating treatment effect predictors.

We use variational arguments to introduce a notion of mean curvature for surfaces in the Heisenberg group H^1 endowed with its Carnot-Carathéodory distance. By analyzing the first variation of area, we characterize C^2 stationary surfaces for the area as those with mean curvature zero (or constant if a volume-preservin…

Proposes top-label calibration and M2B framework for multiclass to binary calibration.

Study explores calibration properties in neural architectures.

A new perfectly truthful calibration measure improves prediction reliability.

New truthful calibration errors improve model ranking in multiclass prediction.

New framework for evaluating multiclass classifier calibration.

We propose a new framework to improve the calibration of neural networks.

Certified calibration methods protect model confidence from adversarial attacks.

Meta-Cal improves post-hoc calibration of neural networks.

Post-processing predictors reduces calibration errors for decision-making.

A new calibration metric bridges testability and actionability.

New study on neural network calibration, linking it to generalization gap.

A new method for multiclass calibration using vector quantization.

This post introduces model calibration and evaluation measures, highlighting issues with a common measure.

Unified calibration metrics improve forecast sharpness and accuracy.

New decision-theoretic calibration error metric improves prediction reliability.

Cone structures over minimal products can't be calibrated smoothly.

Post-hoc calibration improves uncertainty under domain shift.

Smooth calibration improves forecast reliability even with leaked information.

Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification methods tailored for extreme events, such as thresholded and weighted …