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09:00
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Introduction
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Olaf Behnke
(Deutsches Elektronen-Synchrotron (DE))
Lydia Brenner
(Nikhef National institute for subatomic physics (NL))
Philipp Windischhofer
(University of Chicago (US))
Kyle Cormier
(CERN)
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09:05
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Tools and methods for interpretable high-dimensional analysis
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Mathieu Markovitch
(Centre National de la Recherche Scientifique (FR))
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10:30
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Interpreting high-dimensional excesses and anomalies
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Jannicke Pearkes
(University of Colorado Boulder (US)) Dr
Purvasha Chakravarti
(University College London)
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09:00
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modelling in small sub-regions
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Mikael Kuusela
(Carnegie Mellon University (US))
Oliver Rieger
(Nikhef National institute for subatomic physics (NL))
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10:30
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validating high-dimensional simulations
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Michelle Ntampaka
Chris Pollard
(University of Warwick (GB))
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09:00
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Scalable inference
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Aliya Nigamova
(Paul Scherrer Institute (CH))
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10:30
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Methods for reducing computational complexity
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Alessandra Rosalba Brazzale
Jonathon Mark Langford
(Imperial College (GB))
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09:00
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Leveraging machine learning and its limits
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Anthony Ozerov
Claire David
(AIMS South Africa)
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10:30
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Uncertainties and Machine Learing
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Davide Valsecchi
(ETH Zurich (CH))
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09:00
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Highlight talk from a physicist
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Philipp Windischhofer
(University of Chicago (US))
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09:30
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Highlight talk from a statistician
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David van Dyk
Alessandra Rosalba Brazzale
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11:00
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Closing
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Kyle Cormier
(CERN)
Lydia Brenner
(Nikhef National institute for subatomic physics (NL))
Olaf Behnke
(Deutsches Elektronen-Synchrotron (DE))
Philipp Windischhofer
(University of Chicago (US))
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13:30
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Data reduction
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Konstantin Leyde
(Flatiron Institute / CCA)
David van Dyk
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15:30
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Validation and goodness-of-fit in high dimensions
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Cristina Alexe
(Scuola Normale Superiore & INFN Pisa (IT)) Prof.
Wolfgang Rolke
(University of Puerto Rico - Mayaguez, Department of Mathematical Sciences)
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13:00
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High-dimensional distributions and Machine Learning
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Lukas Alexander Heinrich
(Technische Universitat Munchen (DE))
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14:00
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Modelling of relationships between variables in high-dimensional data
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Wouter Verkerke
(Nikhef National institute for subatomic physics (NL))
Leigh Preimesberger
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15:30
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Unfolding of high-dimensional observables
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Kyle Cormier
(CERN)
Erik Bensen
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16:30
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Round-table discussion: biases and dependencies
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André David
(CERN)
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13:00
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Information transfer from machine learning to scientists
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Simon Mak
Martina Fusi
(University of Southampton)
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14:00
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interpretation and diagnostics using machine learning
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James Carzon
(Carnegie Mellon University)
Jay Ajitbhai Sandesara
(University of Wisconsin (US))
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15:30
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Complexity scaling of machine learning methods
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Tom Junk
(Fermi National Accelerator Lab. (US))
Maarten Van Veghel
(Nikhef National institute for subatomic physics (NL))
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16:30
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Round-table discussion: future of machine learning
- Dr
Peter Risse
(Southern Methodist University)
Jack Harrison
(Institut de Física d’Altes Energies (IFAE))
Peter Risse
(Southern Methodist University)
Peter Risse
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