9 papers
Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport
Shayan Hundrieser, Insung Kong, Johannes Schmidt-Hieber
We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine the principles of Maxout networ…
On the Universal Representation Property of Spiking Neural Networks
Shayan Hundrieser, Philipp Tuchel, Insung Kong +1
Inspired by biology, spiking neural networks (SNNs) process information via discrete spikes over time, offering an energy-efficient alternative to the classical computing paradigm…
Optimal Transport Based Testing in Factorial Designs
Michel Groppe, Linus Niemöller, Linus Niemöller +6
We introduce a general framework for testing statistical hypotheses in factorial designs for probability measures supported on finite spaces. The suggested methodology is based on…
Sharp Convergence Rates of Empirical Unbalanced Optimal Transport for Spatio-Temporal Point Processes
Marina Struleva, Shayan Hundrieser, Dominic Schuhmacher +1
We statistically analyze empirical plug-in estimators for unbalanced optimal transport (UOT) formalisms, focusing on the Kantorovich-Rubinstein distance, between general intensity…
Local Poisson Deconvolution for Discrete Signals
Shayan Hundrieser, Tudor Manole, Danila Litskevich +1
We analyze the statistical problem of recovering an atomic signal, modeled as a discrete uniform distribution , from a binned Poisson convolution model. This question is motiva…
On the Uniqueness of Kantorovich Potentials
Thomas Staudt, Shayan Hundrieser, Axel Munk
Kantorovich potentials denote the dual solutions of the renowned optimal transportation problem. Uniqueness of these solutions is relevant from both a theoretical and an algorithmi…