UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning

Contribution List

1. Welcome to UQ4ML

Alessandra Cappati (Universite Catholique de Louvain (UCL) (BE)), Dr Marco Letizia, Dr Riccardo Finotello (CEA Paris-Saclay), Shamik Ghosh (Centre National de la Recherche Scientifique (FR)), Karolos Potamianos (University of Warwick (GB)), Dr Claudius Krause (HEPHY Vienna (ÖAW))

15/09/2025, 14:00

Organisation

50. Quantifying uncertainties in computer models: an overview

Merlin Keller (EDF)

15/09/2025, 14:15

Simulations and Coding

Keynote

Simulations and Coding

Predictions from computer models are now extensively in industrial studies, to complement, or even sometimes replace, field experiments. Such numerical experiments have key advantages, such as reduced costs, and added flexibility. However, they raise the question of assessing the validity of computer model predictions, with respect to the physical phenomena they seek to reproduce. This is the...

42. Uncertainty in AI-driven Monte Carlo simulations

Dimitrios Tzivrailis (CEA Paris-Saclay)

15/09/2025, 15:45

Simulations and Coding

Short-talk

Simulations and Coding

In the study of complex systems, evaluating physical observables often requires sampling representative configurations via Monte Carlo techniques. These methods rely on repeated evaluations of the system's energy and force fields, which can become computationally expensive. To accelerate these simulations, deep learning models are increasingly employed as surrogate functions to approximate the...

38. Parameter Estimation with Neural Simulation-Based Inference in ATLAS

David Rousseau (IJCLab-Orsay)

15/09/2025, 16:15

HEP - Experiment

Short-talk

Simulations and Coding

Neural Simulation-Based Inference (NSBI) is a powerful class of machine learning (ML)-based methods for statistical inference that naturally handle high dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements at the Large Hadron Collider, where no single observable may be optimal to scan over...

41. Machine learning for optimal BSM sensitivity

Giuliano Panico (University of Florence and INFN Florence)

16/09/2025, 09:30

HEP - Theory

Keynote

HEP - Theory

I will review the parametrized classifiers for optimizing the sensitivity to EFT operators and some the machine-learning approaches for general anomaly detection. Particular attention will be devoted to validation procedures and ways to treat uncertainties.

31. Determination and validation of modeling and theory uncertaintie

Stefano Forte (Università degli Studi e INFN Milano (IT))

16/09/2025, 11:00

HEP - Theory

Short-talk

HEP - Theory

I discuss how uncertainties related to machine learning modeling of a regression problem, as well as those related to missing theoretical information, can be estimated and subsequently validated. Even though these uncertainties are intrinsically Bayesian, given that there is only one underlying true theory and true model, they can be determined both in a Bayesian and frequentist framework. I...

32. Parton Distributions from Neural Networks: Analytical Results

Amedeo Chiefa

16/09/2025, 11:30

HEP - Theory

Short-talk

HEP - Theory

Parton Distribution Functions (PDFs) play a crucial role in describing experimental data at hadron colliders and provide insight into proton structure. As the LHC enters an era of high-precision measurements, a robust PDF determination with a reliable uncertainty quantification has become increasingly important to match the experimental precision. The NNPDF collaboration has pioneered the use...

33. Energy Flow Networks for Jet Quenching Studies

João A. Gonçalves (LIP - IST)

16/09/2025, 12:00

HEP - Theory

Short-talk

HEP - Theory

The phenomena of Jet Quenching, a key signature of the Quark-Gluon Plasma (QGP) formed in Heavy-Ion (HI) collisions, provides a window of insight into the properties of the primordial liquid. In this study, we evaluate the discriminating power of Energy Flow Networks (EFNs), enhanced with substructure observables, in distinguishing between jets stemming from proton-proton (pp) and jets...

47. Uncertainty quantification for deep learning in astroparticle physics

Christian Glaser (Uppsala University)

16/09/2025, 14:00

HEP - Experiment

Keynote

HEP - Experiment

In this contribution I will review the use cases of uncertainty quantification with deep learning in high-energy astroparticle physics. Among other things, I will present the combination of neural networks with conditional normalizing flows to predict the Posterior for all quantities of interest. This Ansatz can be further expanded with the snowstorm method developed by the IceCube...

40. Interdisciplinary Digital Twin Engine InterTwin for calorimeter simulation

Vera Maiboroda (CNRS, IJCLab)

16/09/2025, 15:30

HEP - Experiment

Short-talk

HEP - Experiment

The interTwin project develops an open-source Digital Twin Engine to integrate application-specific Digital Twins (DTs) across scientific domains. Its framework for the development of DTs supports interoperability, performance, portability and accuracy. As part of this initiative, we implemented the CaloINN normalizing-flow model for calorimeter simulations within the interTwin framework....

45. Scaling laws for amplitude surrogates

Joaquin Iturriza Ramirez (Centre National de la Recherche Scientifique (FR))

16/09/2025, 16:00

Deep Learning and Uncertainty Quantification

Short-talk

HEP - Experiment

Fast and precise evaluations of scattering amplitudes even in the case of precision calculations is essential for event generation tools at the HL-LHC. We explore the scaling behavior of the achievable precision of neural networks in this regression problem for multiple architectures, including a Lorentz symmetry aware multilayer perceptron and the L-GATr architecture. L-GATr is equivariant...

37. Fair Universe HiggsML Uncertainty Challenge

Ragansu Chakkappai (IJCLab-Orsay)

16/09/2025, 16:30

HEP - Experiment

Short-talk

HEP - Experiment

The Fair Universe project organised the HiggsML Uncertainty Challenge, which took place from 12th September 2024, to 14th March 2025. This groundbreaking competition in high-energy physics (HEP) and machine learning was the first to strongly emphasis on uncertainties, focusing on mastering both the uncertainties in the input training data and providing credible confidence intervals in the...

46. Introduction to Causality for Time Series and HSIC

Aurore Lomet (CEA Paris-Saclay)

17/09/2025, 09:30

Data Analysis, Time series, Causal analysis

Keynote

Data analysis, Time Series, Causal analysis

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))

17/09/2025, 11:00

Deep Learning and Uncertainty Quantification

Short-talk

Data analysis, Time Series, Causal analysis

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))

17/09/2025, 11:30

Data Analysis, Time series, Causal analysis

Short-talk

Data analysis, Time Series, Causal analysis

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)

17/09/2025, 12:00

Deep Learning and Uncertainty Quantification

Short-talk

Data analysis, Time Series, Causal analysis

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...

48. Uncertainty Quantification for Neural Networks in Particle Physics

Anja Butter (Centre National de la Recherche Scientifique (FR))

17/09/2025, 14:00

Deep Learning and Uncertainty Quantification

Keynote

Deep Learning and Uncertainty Quantification

Correctly calibrated uncertainties have always been a fundamental pillar of particle physics. As machine learning becomes increasingly integrated into both experimental and theoretical workflows, it is essential that neural network predictions include robust and reliable uncertainty estimates.

This talk will review current approaches to uncertainty estimation in neural networks, focusing on...

34. Uncertainty Quantification in an ML Pattern Recognition Pipeline

Lukas Péron

17/09/2025, 15:30

Deep Learning and Uncertainty Quantification

Short-talk

Deep Learning and Uncertainty Quantification

Geometric learning pipelines have achieved state-of-the-art performance in High-Energy and Nuclear Physics reconstruction tasks like flavor tagging and particle tracking [1]. Starting from a point cloud of detector or particle-level measurements, a graph can be built where the measurements are nodes, and where the edges represent all possible physics relationships between the nodes. Depending...

44. Uncertainty quantification for machine learning: a new approach for the Critical Heat Flux application

Michele Cazzola (CEA Paris-Saclay)

17/09/2025, 16:00

Deep Learning and Uncertainty Quantification

Short-talk

Deep Learning and Uncertainty Quantification

Critical Heat Flux (CHF) represents a concern for the nuclear safety, as it leads to a rapid drop down in the heat transfer between a heated surface and the liquid coolant in the core of nuclear reactors. This could cause several issues to the system, including structural damage and release of radioactive material.

The main challenge related to CHF prediction is the highly non-linear...

49. Machine learning of biological sequences

Jorge Fernández de Cossío Díaz (CEA Paris-Saclay)

18/09/2025, 09:00

Keynote

Spotlight

Over the last decade machine learning has had tremendous impact on biological sequence data analysis. In this talk, I will begin by introducing general issues related to biological sequence modeling. I will then review a selection of recent works on this topic, including: i) generative models for sequence design, ii) sampling of evolutionary paths between natural sequences of different...

43. Interaction reconstruction in scintillator detectors for PET imaging - Deep Learning approach with uncertainty quantification

Geoffrey Daniel

18/09/2025, 10:30

Deep Learning and Uncertainty Quantification

Short-talk

Spotlight

Positron Emission Tomography (PET) is a medical imaging modality that is powerful to follow biological processes. Nevertheless, it is of importance to increase its sensitivity to improve the contrast on the PET images and to decrease the patient exposure to radiation. One promising way is the use of the Time-of-Flight (ToF) of coincident gamma ray photons to get a more precise information of...

30. Wrap Up

Dr Riccardo Finotello (CEA Paris-Saclay), Dr Claudius Krause (HEPHY Vienna (ÖAW)), Karolos Potamianos (University of Warwick (GB)), Shamik Ghosh (Centre National de la Recherche Scientifique (FR)), Dr Marco Letizia, Alessandra Cappati (Universite Catholique de Louvain (UCL) (BE))

18/09/2025, 11:00

Organisation