5 citations · 13 across the 22 of their papers we have counts for
23 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…
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…
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…
A Gentle Introduction to Conformal Time Series Forecasting
M. Stocker, W. Małgorzewicz, 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…
RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion
Thao Nguyen, Jiaqi Ma, Fahad Shahbaz Khan +2
Precipitation nowcasting, predicting future radar echo sequences from current observations, is a critical yet challenging task due to the inherently chaotic and tightly coupled spa…
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…
Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds
David Vallmanya Poch, Yorick Estievenart, Elnura Zhalieva +3
Accurate dose calculations in proton therapy rely on high-quality CT images. While planning CTs (pCTs) serve as a reference for dosimetric planning, Cone Beam CT (CBCT) is used thr…
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…
A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression
Victor Dheur, Matteo Fontana, Yorick Estievenart +2
Conformal prediction provides a powerful framework for constructing distribution-free prediction regions with finite-sample coverage guarantees. While extensively studied in univar…
Rectifying Conformity Scores for Better Conditional Coverage
Vincent Plassier, Alexander Fishkov, Victor Dheur +4
We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score…
Large Language Models Are Human-Like Internally
Tatsuki Kuribayashi, Yohei Oseki, Souhaib Ben Taieb +2
Recent cognitive modeling studies have reported that larger language models (LMs) exhibit a poorer fit to human reading behavior (Oh and Schuler, 2023b; Shain et al., 2024; Kuribay…
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…
Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
Sukanya Patra, Souhaib Ben Taieb
Industrial anomaly detection is crucial for quality control and predictive maintenance, but it presents challenges due to limited training data, diverse anomaly types, and external…
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…
Scores for Multivariate Distributions and Level Sets
Xiaochun Meng, James W. Taylor, Souhaib Ben Taieb +1
Forecasts of multivariate probability distributions are required for a variety of applications. Scoring rules enable the evaluation of forecast accuracy, and comparison between for…
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
Souhaib Ben Taieb, Gianluca Bontempi, Amir Atiya +1
Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but…