Choisissez le fuseau horaire

Le fuseau horaire de votre profil:

Utiliser le fuseau horaire basé sur:

Evénement/catégorie Personnalisé

Veuillez choisir un fuseau horaire personnalisé

S'authentifier

Artificial Intelligence and the Uncertainty challenge in Fundamental Physics

27 novembre 2023 à 1 décembre 2023

Fuseau horaire Europe/Paris

Liste des orateurs

Mathias Josef Backes (Kirchhoff Institut für Physik)
- Orateur à An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
Wahid Bhimji (Lawrence Berkeley National Laboratory)
- Orateur à Introduction
- Orateur à Wrap up and next steps
- Orateur à Fair Universe Challenge Paris 2023
Sebastian Bieringer (Hamburg University, Institute for experimental physics)
- Orateur à Efficient Sampling from Bayesian Network Posteriors for Optimal Uncertainties
Luca Biferale (U Roma Tor Vergata)
- Orateur à Machine-learning and equations-informed tools for generation and augmentation of turbulent data.
Jerome Bobin (CEA)
- Orateur à Data frugal machine learning approaches for unmixing problems in Physics
Vincent Chabridon (EDF)
- Orateur à Uncertainty Quantification in Industry
- Orateur à Uncertainty Quantification and Machine Learning in Industry: Current Practices and Challenges in Industrial Applications for Low-Carbon Electricity Production
Ragansu Chakkappai
- Orateur à Overview of HiggsML Uncertainty Challenge
Yuan-Tang Chou (University of Washington, Seattle (US))
- Orateur à Neuroscience ML challenges using CodaBench: Decoding multi-limb trajectories from two-photon calcium imaging
Vincent Alexander Croft (LIACS)
- Orateur à Unfolding in High Energy Physics
Sascha Diefenbacher (Lawrence Berkeley National Laboratory)
- Orateur à Metrics for Uncertainty-Aware ML Methods
Sascha Diefenbacher (Lawrence Berkeley National Laboratory)
- Orateur à Scoring and Baseline Systematic Aware method
tommaso dorigo (INFN Sezione di Padova)
- Orateur à Robustness to Uncertainties in Machine Learning Applications for HEP
Marylou Gabrié (Ecole Polytechnique)
- Orateur à Machine Learning Assisted Sampling: Applications in Physics
Julien Girard-Satabin (CEA)
- Orateur à xAI in practice: current state of the art, limitations and perspectives
Ekaterina Govorkova (MIT)
- Orateur à Exploring Data Challenges and Leveraging Codabench: A Practical Journey with unsupervised New Physics detection at 40 MHz
Nathan Huetsch (Heidelberg University)
- Orateur à Precision-Machine Learning for the Matrix Element Method
Gregor Kasieczka (Universität Hamburg)
- Orateur à Potential and challenges of highly dimensional generative models
Eiji Kawasaki (CEA)
- Orateur à Data Subsampling for Bayesian Neural Networks
Elham E Khoda (University of Washington)
- Orateur à Fair Universe hackathon : outcome and plans
- Orateur à Fair Universe HiggsML Uncertainty Challenge: Lessons Learned and plans
Sabine Kraml (LPSC Granoble)
- Orateur à Publication and reuse of ML models in LHC analyses
Mikael Kuusela (Carnegie Mellon University)
- Orateur à SBI
- Orateur à Simulation-Based Inference: Where Classical Statistics Meets Machine Learning
Alessandro Leite (INRIA)
- Orateur à Highlights from the 1st CNRS AISSAI Thematic Quarter on Causality
Gilles Louppe (University of Liège)
- Orateur à SBI Introduction
- Orateur à An introduction to simulation-based inference
Mark Neubauer (University of Illinois at Urbana-Champaign)
- Orateur à Explainable AI for Interpretability of Deep Neural Networks : the High Energy Physics perspective
- Orateur à Uncertainty Quantification and Anomaly Detection with Evidential Deep Learning
Sofia Palacios Schweitzer (ITP, Heidelberg University)
- Orateur à Uncertainty-aware diffusion models for LHC Event Generation
Harrison Prosper (Florida State University)
- Orateur à Simulation Based Inference: The Frequentist Perspective Abstract:
Radi Radev (CERN)
- Orateur à Can contrastive learning de-bias my model?
David Rousseau (IJCLab, CNRS/IN2P3, Université Paris-Saclay)
- Orateur à Farewell
- Orateur à Welcome
Mehmet Ozgur Sahin (CEA-Saclay)
- Orateur à Data driven background estimation in HEP using generative adversarial networks
Simone Scardapane (Sapienza University of Rome, Roma, Italy)
- Orateur à Advancing Explainable AI: Testing and Enhancing Techniques Across Multidisciplinary Use-Cases
Laurens Sluijterman (Radboud University)
- Orateur à Uncertainty Quantification in Neural Networks: Methods and Considerations
Ihsan Ullah (ChaLearn)
- Orateur à Walkthrough of CodaBench and submissions
Gael VAROQUAUX
- Orateur à Revisiting models and uncertainty with AI
Emmanuel Vazquez (L2S Paris-Saclay)
- Orateur à Bayesian optimisation
- Orateur à An Introduction to Bayesian Optimization
Wouter Verkerke (NIKHEF)
- Orateur à Uncertainty Quantification in High Energy Physics
- Orateur à Uncertainty modeling in particle physics
Gordon Watts (University of Washington)
- Orateur à Using an adversary trained on a control sample to control systematic errors
Christoph Weniger (University of Amsterdam)
- Orateur à Swyft: Direct marginal inference for large simulation models
Philipp Windischhofer (University of Chicago)
- Orateur à Optimal dataset-wide inference in the presence of systematic uncertainties
Burak Yelmen (University of Tartu)
- Orateur à Generative modeling in genomics and a perspective on uncertainty quantification
mohsen zargarani (CentraleSupelec, Institute GEEPs)
- Orateur à A Data-centric AI based diagnosis method using unsupervised labeling and MLP