10 citations · 15 across the 35 of their papers we have counts for
41 papers
Greedy multiscale kernel algorithms
Sara Avesani, Armin Iske, Michael Multerer +1
Multiscale kernel approximation and greedy kernel algorithms are two established tools for scattered data approximation. We combine them into greedy multiscale kernel algorithms, i…
-adaptive trees for graph signal approximation
Giacomo Elefante, Wolfgang Erb, Michael Multerer
Tree-encoded partitionings of graphs are fundamental tools for the decomposition and approximation of graph signals. For the efficient approximation of such graph signals, we devel…
Samplet compression for conditionally positive definite kernels and universal Kriging
Sara Avesani, Rüdiger Kempf, Michael Multerer +1
We present a samplet-based framework for the efficient numerical solution of saddle-point systems arising from conditionally positive definite (CPD) kernel approximation in general…
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.…
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…