5 citations · 13 across the 22 of their papers we have counts for
11 papers · 1 filter
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…
Multivariate Latent Recalibration for Conditional Normalizing Flows
Victor Dheur, Souhaib Ben Taieb
Reliably characterizing the full conditional distribution of a multivariate response variable given a set of covariates is crucial for trustworthy decision-making. However, misspec…
Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants
Yorick Estievenart, Sukanya Patra, Souhaib Ben Taieb
Efficient and reliable operation of Concentrated Solar Power (CSP) plants is essential for meeting the growing demand for sustainable energy. However, high-temperature solar receiv…
Preventing Conflicting Gradients in Neural Marked Temporal Point Processes
Tanguy Bosser, Souhaib Ben Taieb
Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex temporal inter-dependencies between labeled events. These models inherently learn two predictiv…
Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images
Sukanya Patra, Nicolas Sournac, Souhaib Ben Taieb
Concentrated Solar Power (CSP) plants store energy by heating a storage medium with an array of mirrors that focus sunlight onto solar receivers atop a central tower. Operating at…
Probabilistic Calibration by Design for Neural Network Regression
Victor Dheur, Souhaib Ben Taieb
Generating calibrated and sharp neural network predictive distributions for regression problems is essential for optimal decision-making in many real-world applications. To address…
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
Victor Dheur, Tanguy Bosser, Rafael Izbicki +1
Sequences of labeled events observed at irregular intervals in continuous time are ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical framewor…
Anomaly detection with semi-supervised classification based on risk estimators
Le Thi Khanh Hien, Sukanya Patra, Souhaib Ben Taieb
A significant limitation of one-class classification anomaly detection methods is their reliance on the assumption that unlabeled training data only contains normal instances. To o…
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data
Tanguy Bosser, Souhaib Ben Taieb
Temporal Point Processes (TPPs) serve as the standard mathematical framework for modeling asynchronous event sequences in continuous time. However, classical TPP models are often c…
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
Victor Dheur, Souhaib Ben Taieb
Accurate probabilistic predictions are essential for optimal decision making. While neural network miscalibration has been studied primarily in classification, we investigate this…