11 papers
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…
Low-rank kernel methods for American option pricing
Michael Multerer, Paul Schneider, Chiara Segala
We propose a scalable and theoretically grounded low-rank conditional expectation model for recursive Monte Carlo optimal stopping problems, in particular American option pricing.…
Kernel interpolation on generalized sparse grids
Michael Griebel, Helmut Harbrecht, Michael Multerer
We consider scattered data approximation on product regions of equal and different dimensionality. On each of these regions, we assume quasi-uniform but unstructured data sites and…
Samplet limits and multiwavelets
Gianluca Giacchi, Michael Multerer, Jacopo Quizi
Samplets are data adapted multiresolution analyses of localized discrete signed measures. They can be constructed on scattered data sites in arbitrary dimension such that they exhi…
Tree-Adaptive Multiscale Kernel Lasso in Samplet Coordinates
Sara Avesani, Gaia Fumagalli, Michael Multerer +1
We develop a novel framework for sparse multiscale kernel approximation of large scattered data problems based on a samplet representation. Samplets form a multiresolution analysis…
Ensured Energy: How a Serious Game can Reach and Engage Diverse Societal Groups in Swiss Energy Transition
Toby Simpson, Saara Jones, Gracia Brückmann +6
In support of Switzerland's energy and climate strategy for 2050, researchers investigate scenarios for the transition of energy systems towards a higher share of renewables, asses…