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18:00
Uncertainty-aware machine learning for the LHC
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Nina Elmer
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18:01
Generative models: their evaluation and their limitations
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Samuele Grossi
(Università degli studi di Genova & INFN sezione di Genova)
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18:02
Limits to classification performance by relating Kullback-Leibler divergence to Cohen’s Kappa
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Stephen Watts
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18:03
Graph neural networks on the test bench in HEP applications
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Emanuel Lorenz Pfeffer
(KIT - Karlsruhe Institute of Technology (DE))
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18:04
Interpolated Likelihoods for Fast Reinterpretations
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Tom Runting
(Imperial College (GB))
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18:05
Efficient machine learning for statistical hypothesis testing
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Marco Letizia
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18:06
Integrating Explainable AI in Data Analyses of ATLAS Experiment at CERN
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Joseph Carmignani
(University of Liverpool (GB))
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18:07
Proximal Nested Sampling with Data-Driven AI Priors
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Henry Aldridge
(UCL)
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18:08
Generative models of astrophysical fields with scattering transforms on the sphere
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Matt Price
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18:09
Advanced techniques for Simulation Based Inference in collider physics
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Giovanni De Crescenzo
(University of Heidelberg)
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18:10
SBI for wide field weak lensing
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Kiyam Lin
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18:11
Exhaustive Symbolic Regression: Learning Astrophysics directly from Data
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Harry Desmond
(University of Portsmouth)
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18:12
Usage of weakly correlated observables for nuisance parameter fits
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Lars Stietz
(Hamburg University of Technology (DE))
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18:13
Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling
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Benjamin Boyd
(University of Cambridge)
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18:14
COmoving Computer Acceleration (COCA): Correcting Emulation Errors for Trustworthy N-Body Simulations
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Deaglan Bartlett
(Institut d'Astrophysique de Paris)
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18:15
Application of Machine Learning Based Top Quark and Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes
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Monika Machalová
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18:16
Accelerating High-Dimensional Cosmological Inference with COSMOPOWER
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Alessio Spurio Mancini
(Royal Holloway, University of London)
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18:17
Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI
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Kai Lehman
(LMU Munich)
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18:18
How to Unfold Top Decays
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Sofia Palacios Schweitzer
(Heidelberg)
Tilman Plehn
(Heidelberg University)
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18:19
Noise injection node regularization for robust learning
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Noam Levi
(Tel Aviv University)
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18:20
Modeling Smooth Backgrounds at Collider Experiments With Log Gaussian Cox Processes
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Yuval Yitzhak Frid
(Tel Aviv University (IL))
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18:21
Precision Machine Learning for the Matrix Element Method
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Nathan Huetsch
(Heidelberg)
Tilman Plehn
(Heidelberg University)
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18:22
The Landscape of Unfolding with Machine Learning
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Tilman Plehn
(Heidelberg University)
Xavier Marino
(Heidelberg)
