Past Talks in Joint Analysis Seminar

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Past Talks in Post Graduate Seminar

13.05.2024, 16:00

Adit Radhakrishnan (Harvard University):
How do neural networks learn features from data?

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06.05.2024, 16:00

Dominik Stöger (KU Eichstätt-Ingolstadt):
Breaking the quadratic rank bottleneck in non-convex matrix sensing: Recovery guarantees with (near-)optimal sample complexity

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29.04.2024, 10:00

Massimo Fornasier (TU Munich):
Wassertein Sobolev functions and their numerical approximations

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08.02.2024, 16:15

Laura Paul (RWTH Aachen University):
Covariance Estimation for Massive MIMO

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01.02.2024, 16:15

Arinze Folarin (RWTH Aachen University):
Tensor Recovery: Exploring Hierarchical Tensor Representation in ISLET Algorithm

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25.01.2024, 16:15

Robert Kunsch (RWTH Aachen University):
Monte Carlo quadrature with optimal confidence

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11.01.2024, 16:15

Johannes Müller (RWTH Aachen University):
Natural Gradients for Scientific Machine Learning

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21.12.2023, 16:15

Robert M. Gower (Flatiron Institute):
Analysing stochastic gradient descent with adaptive stepsize and under interpolation

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14.12.2023, 16:15

Maxime Nguegnang (RWTH Aachen University):
Analysis of gradient descent and stochastic gradient descent for learning linear neural networks

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07.12.2023, 16:15

Yaim Cooper (University of Notre Dame):
Tradeoffs in Machine Learning

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30.11.2023, 16:15

Nathan Srebro (Toyota Technological Institute at Chicago):
Interpolation Learning and Overfitting with Linear Predictors and Short Programs

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