4. Physics
  5. High Energy Physics
  6. High Energy Physics - Theory

Matthias Seeger

Physics – High Energy Physics – High Energy Physics - Theory

Scientist

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Scientist

Institut fuer Theoretische Physik, Universitaet Erlangen-Nuernberg
University of Erlangen-Nuernberg

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Computer Science – Learning
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Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Statistics – Machine Learning
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QCD_{1+1} with Static Quarks as Supersymmetric Quantum Mechanics

Physics – High Energy Physics – High Energy Physics - Theory
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The Path Integral for 1+1-dimensional QCD

Physics – High Energy Physics – High Energy Physics - Theory
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Profile ID: LFWR-SCP-P-240821

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