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