ENBIS-26 Conference

Session

frENBIS Session - Advances in machine learning and sensitivity analysis

Sep 9, 2026, 12:00 PM

Conveners

frENBIS Session - Advances in machine learning and sensitivity analysis

• Jean-Michel Poggi (University of Paris-Saclay)

2. Geographically Weighted Regression for Low-Cost Sensors Calibration

Jean-Michel Poggi (University of Paris-Saclay)

9/9/26, 12:00 PM

Other/special session/invited session

Low-cost sensors are a new tool for improving air quality maps, which are of major interest in the current era of high-resolution, urban-scale air quality monitoring. These sensors require calibration using reference analyzers. A variety of strategies can be employed, ranging from individual pointwise calibration models to network calibration models. Here, we propose using geographically...

88. Quantile oriented sensitivity analysis: why and how?

Véronique Maume-Deschamps (Institut Camille Jordan, Université Claude Bernard Lyon 1)

9/9/26, 12:30 PM

Other/special session/invited session

Quantile-oriented sensitivity analysis allows to quantify uncertainty around quantiles, at different levels, while sensitivity analysis is often focused on deviation around mean (as it involves variances). We will consider qunatile-oriented sensitivity indices (QOSA) and quantile-oriented Shapley effects (QOSE). We will present their relevance on some analytical examples, show how to estimate...

91. Shapelet learning via sparse feature selection for interpretable time series classification

Julien Pelamatti (EDF R&D)

9/9/26, 1:00 PM

Other/special session/invited session

Time-series classification faces recurring challenges, including high dimensionality, autocorrelation, and the difficulty of identifying features that capture essential dynamics across temporal scales and phase shifts. We address these issues through shapelet decomposition, a technique that extracts shape-based features from time series while preserving both temporal and frequency information....