14 papers
Set-Preserving Calibration from Conformal P-Values to E-Values
Nabil Alami, Jad Zakharia, Souhaib Ben Taieb
Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependen…
Symmetric Aggregation of Conformity Scores for Efficient Uncertainty Sets
Nabil Alami, Jad Zakharia, Souhaib Ben Taieb
Access to multiple predictive models trained for the same task, whether in regression or classification, is increasingly common in many applications. Aggregating their predictive u…
Calibrated Multivariate Distributional Regression with Pre-Rank Regularization
Aya Laajil, Elnura Zhalieva, Naomi Desobry +1
The goal of probabilistic prediction is to issue predictive distributions that are as informative as possible, subject to being calibrated. Despite substantial progress in the univ…
An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination
Sukanya Patra, Souhaib Ben Taieb
Unsupervised anomaly detection (AD) methods typically assume clean training data, yet real-world datasets often contain undetected or mislabeled anomalies, leading to significant p…
A Gentle Introduction to Conformal Time Series Forecasting
M. Stocker, W. MaÅgorzewicz, M. Fontana +1
Conformal prediction is a powerful post-hoc framework for uncertainty quantification that provides distribution-free coverage guarantees. However, these guarantees crucially rely o…
Enforcing Calibration in Multi-Output Probabilistic Regression with Pre-rank Regularization
Naomi Desobry, Elnura Zhalieva, Souhaib Ben Taieb
Probabilistic models must be well calibrated to support reliable decision-making. While calibration in single-output regression is well studied, defining and achieving multivariate…