UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning

Session

Data analysis, Time Series, Causal analysis

UQ4ML/20250917/data-an

Sep 17, 2025, 9:30 AM

Amphithéâtre Claude Bloch (IPhT) (CEA Paris-Saclay)

Conveners

Data analysis, Time Series, Causal analysis: Keynote

• Riccardo Finotello (CEA Paris-Saclay)

Data analysis, Time Series, Causal analysis: Talks

• Riccardo Finotello (CEA Paris-Saclay)

46. Introduction to Causality for Time Series and HSIC

Aurore Lomet (CEA Paris-Saclay)

9/17/25, 9:30 AM

Data Analysis, Time series, Causal analysis

Keynote

Causality, in Pearl’s framework, is defined through structural causal models: systems of structural equations with exogenous variables and a directed acyclic graph that encodes cause–effect relations. In contrast, correlation, which often forms the basis of artificial intelligence models, quantifies statistical association and may arise from confounding or indirect paths without implying a...

35. Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models

Aurora Singstad Grefsrud (Western Norway University of Applied Sciences (NO))

9/17/25, 11:00 AM

Deep Learning and Uncertainty Quantification

Short-talk

Rigorous statistical methods, including the estimation of parameter values and their uncertainties, underpins the validity of scientific discovery, and has been especially important in the natural sciences. In the age of data-driven modeling, where the complexity of data and statistical models grow exponentially as computing power increases, uncertainty quantification has become exceedingly...

36. Unsupervised Anomaly Detection in Multivariate Time Series Using Public Benchmarks and Synthetic Data from Lorenzetti

Laura Boggia (Centre National de la Recherche Scientifique (FR))

9/17/25, 11:30 AM

Data Analysis, Time series, Causal analysis

Short-talk

Anomaly detection in multivariate time series is an important problem across various fields such as healthcare, financial services, manufacturing or physics detector monitoring. Accurately identifying the instances when defects occur is essential but challenging, as the types of anomalies are unknown beforehand and reliably labelled data are scarce.
We evaluate unsupervised transformer-based...

39. Bayesian continual learning and forgetting in neural networks

Kellian Cottart (Université Paris Saclay)

9/17/25, 12:00 PM

Deep Learning and Uncertainty Quantification

Short-talk

Biological synapses effortlessly balance memory retention and flexibility, yet artificial neural networks still struggle with the extremes of catastrophic forgetting and catastrophic remembering. Here, we introduce Metaplasticity from Synaptic Uncertainty (MESU), a Bayesian framework that updates network parameters according to their uncertainty. This approach allows a principled combination...