Cover Image

Robustness quantification: a new method for assessing the reliability of the predictions of a classifier

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Detavernier, Adrián, De Bock, Jasper
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier
by: Detavernier, Adrián, et al.
Published: (2025)

Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions
by: Detavernier, Adrián, et al.
Published: (2026)

Active operator learning with predictive uncertainty quantification for partial differential equations
by: Winovich, Nick, et al.
Published: (2025)

Computing lower expectations with respect to total variation distance and chi-squared divergence balls
by: De Bock, Jasper
Published: (2026)

The Certainty Ratio $C_ρ$: a novel metric for assessing the reliability of classifier predictions
by: Aguilar-Ruiz, Jesus S.
Published: (2024)

Machine learning-based system reliability analysis with Gaussian Process Regression
by: Zhou, Lisang, et al.
Published: (2024)

Tunable correlation retention: A statistical method for generating synthetic data
by: Jävergård, Nicklas, et al.
Published: (2024)

Comparative study of machine learning and statistical methods for automatic identification and quantification in γ-ray spectrometry
by: Phan, Dinh Triem, et al.
Published: (2025)

Reference-state System Reliability method for scalable uncertainty quantification of coherent systems
by: Byun, Ji-Eun, et al.
Published: (2026)

Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection
by: Lassance, Rodrigo F. L., et al.
Published: (2026)

Negative Dependence as a toolbox for machine learning : review and new developments
by: Tran, Hoang-Son, et al.
Published: (2025)

Self-learning signal classifier for decameter coherent scatter radars
by: Berngardt, Oleg, et al.
Published: (2025)

Extending choice assessments to choice functions: An algorithm for computing the natural extension
by: Decadt, Arne, et al.
Published: (2024)

Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
by: Sharma, Himanshu, et al.
Published: (2024)

A convenient characterisation of convergent upper transition operators
by: De Bock, Jasper, et al.
Published: (2025)

A hierarchical entropy method for the delocalization of bias in high-dimensional Langevin Monte Carlo
by: Lacker, Daniel, et al.
Published: (2025)

Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
by: Labarthe, Aldric
Published: (2026)

On quantitative Laplace-type convergence results for some exponential probability measures, with two applications
by: De Bortoli, Valentin, et al.
Published: (2021)

GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction
by: Lou, Xiayin, et al.
Published: (2024)

Large deviation principles for convolutional Bayesian neural networks
by: Bassetti, Federico, et al.
Published: (2026)

Off-the-grid prediction and testing for linear combination of translated features
by: Butucea, Cristina, et al.
Published: (2022)

A new approach for imprecise probabilities
by: Basili, Marcello, et al.
Published: (2024)

Low-power multi-mode fiber projector overcomes shallow neural networks classifiers
by: Ancora, Daniele, et al.
Published: (2022)

Diffusion annealed Langevin dynamics: a theoretical study
by: Cattiaux, Patrick, et al.
Published: (2025)

Generative diffusion models from a PDE perspective
by: Cao, Fei, et al.
Published: (2025)

A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
by: Papapantoleon, Antonis, et al.
Published: (2024)

Concept activation vectors: a unifying view and adversarial attacks
by: Schnoor, Ekkehard, et al.
Published: (2025)

Effective continuous equations for adaptive SGD: a stochastic analysis view
by: Callisti, Luca, et al.
Published: (2025)

Deep Neural Networks as Iterated Function Systems and a Generalization Bound
by: Vacher, Jonathan
Published: (2026)

Divergence-kernel method for linear responses of densities and generative models
by: Ni, Angxiu
Published: (2025)

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
by: Gao, Xuefeng, et al.
Published: (2023)

A Review of the Receiver Operating Characteristic Curve and a Proof About the Area Beneath It
by: Redolfi, Steven
Published: (2026)

The generator gradient estimator is an adjoint state method for stochastic differential equations
by: Badolle, Quentin, et al.
Published: (2024)

Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
by: Caprio, Michele, et al.
Published: (2026)

Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance
by: Gao, Rui, et al.
Published: (2024)

Uncertainty quantification and posterior sampling for network reconstruction
by: Peixoto, Tiago P.
Published: (2025)

Exploring applications of topological data analysis in stock index movement prediction
by: Huang, Dazhi, et al.
Published: (2024)

Assessing high-order effects in feature importance via predictability decomposition
by: Ontivero-Ortega, Marlis, et al.
Published: (2024)

Gaussian random field approximation via Stein's method with applications to wide random neural networks
by: Balasubramanian, Krishnakumar, et al.
Published: (2023)

Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
by: Chandrasekher, Kabir Aladin, et al.
Published: (2022)