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Beyond Lipschitz: Data-Driven Robustness via Discrete Modulus of Continuity
Jürgen Dölz, Michael Multerer, Michele Palma
Robustness of neural networks is commonly quantified via local or global Lipschitz constants. However, Lipschitz continuity can be overly coarse or overly restrictive as global rob…
Observation-specific explanations through scattered data approximation
Valentina Ghidini, Michael Multerer, Jacopo Quizi +1
This work introduces the definition of observation-specific explanations to assign a score to each data point proportional to its importance in the definition of the prediction pro…
Fast Empirical Scenarios
Michael Multerer, Paul Schneider, Rohan Sen
We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenar…
Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraints
Davide Baroli, Helmut Harbrecht, Michael Multerer
We consider scattered data approximation in samplet coordinates with -regularization. The application of an -regularization term enforces sparsity of the coefficien…