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12:30 PM
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Break
(until 2:00 PM)
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2:00 PM
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Talks
-
Sergei Gleyzer
(University of Alabama (US))
(until 3:30 PM)
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2:00 PM
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Interpretable Machine Learning for Particle Physics
-
Jesse Thaler
(MIT/IAIFI)
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2:45 PM
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Understanding and mitigating failures in anomaly detection: a probabilistic perspective
-
Lily Zhang
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3:30 PM
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Break
(until 4:00 PM)
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4:00 PM
|
Talks
-
Robert Cousins Jr
(until 6:00 PM)
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4:00 PM
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Statistical tests for anomaly detection at the LHC
-
Gaia Grosso
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4:45 PM
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Detecting New Physics as data anomalies at the LHC: Transitioning from small-scale toy datasets to millions of complex proton collisions
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Thea Aarrestad
(ETH Zurich (CH))
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5:10 PM
|
Anomaly aware machine learning for dark matter direct detection at the DARWIN experiment
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Andre Joshua Scaffidi
|
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5:35 PM
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Feldman-Cousins’ ML Cousin
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Joshua Villarreal
|
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6:00 PM
|
Social
(until 7:15 PM)
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6:00 PM
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Uncertainty-aware machine learning for the LHC
-
Nina Elmer
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6:01 PM
|
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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6:02 PM
|
Limits to classification performance by relating Kullback-Leibler divergence to Cohen’s Kappa
-
Stephen Watts
|
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6:03 PM
|
Graph neural networks on the test bench in HEP applications
-
Emanuel Lorenz Pfeffer
(KIT - Karlsruhe Institute of Technology (DE))
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6:04 PM
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Interpolated Likelihoods for Fast Reinterpretations
-
Tom Runting
(Imperial College (GB))
|
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6:05 PM
|
Efficient machine learning for statistical hypothesis testing
- Dr
Marco Letizia
|
|
6:06 PM
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Integrating Explainable AI in Data Analyses of ATLAS Experiment at CERN
-
Joseph Carmignani
(University of Liverpool (GB))
|
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6:07 PM
|
Proximal Nested Sampling with Data-Driven AI Priors
-
Henry Aldridge
(UCL)
|
|
6:08 PM
|
Generative models of astrophysical fields with scattering transforms on the sphere
-
Matt Price
|
|
6:09 PM
|
Advanced techniques for Simulation Based Inference in collider physics
-
Giovanni De Crescenzo
(University of Heidelberg)
|
|
6:10 PM
|
SBI for wide field weak lensing
-
Kiyam Lin
|
|
6:11 PM
|
Exhaustive Symbolic Regression: Learning Astrophysics directly from Data
-
Harry Desmond
(University of Portsmouth)
|
|
6:12 PM
|
Usage of weakly correlated observables for nuisance parameter fits
-
Lars Stietz
(Hamburg University of Technology (DE))
|
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6:13 PM
|
Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling
-
Benjamin Boyd
(University of Cambridge)
|
|
6:14 PM
|
COmoving Computer Acceleration (COCA): Correcting Emulation Errors for Trustworthy N-Body Simulations
-
Deaglan Bartlett
(Institut d'Astrophysique de Paris)
|
|
6:15 PM
|
Application of Machine Learning Based Top Quark and Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes
-
Monika Machalová
|
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6:16 PM
|
Accelerating High-Dimensional Cosmological Inference with COSMOPOWER
-
Alessio Spurio Mancini
(Royal Holloway, University of London)
|
|
6:17 PM
|
Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI
-
Kai Lehman
(LMU Munich)
|
|
6:18 PM
|
How to Unfold Top Decays
-
Sofia Palacios Schweitzer
(Heidelberg)
Tilman Plehn
(Heidelberg University)
|
|
6:19 PM
|
Noise injection node regularization for robust learning
-
Noam Levi
(Tel Aviv University)
|
|
6:20 PM
|
Modeling Smooth Backgrounds at Collider Experiments With Log Gaussian Cox Processes
-
Yuval Yitzhak Frid
(Tel Aviv University (IL))
|
|
6:21 PM
|
Precision Machine Learning for the Matrix Element Method
-
Nathan Huetsch
(Heidelberg)
Tilman Plehn
(Heidelberg University)
|
|
6:22 PM
|
The Landscape of Unfolding with Machine Learning
-
Xavier Marino
(Heidelberg)
Tilman Plehn
(Heidelberg University)
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