no code implementations • 23 Dec 2023 • Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh, Manuel Kunz, Patrick Doupe, Tim Januschowski
This paper proposes a novel method for demand forecasting in a pricing context.
no code implementations • 23 May 2023 • Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adele Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Kekić, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski
These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption.
no code implementations • 18 Aug 2022 • Julian Sieber, Johann Gehringer
We show that deep belief networks with binary hidden units can approximate any multivariate probability density under very mild integrability requirements on the parental density of the visible nodes.
no code implementations • 3 Dec 2020 • Xue-Mei Li, Julian Sieber
We prove a fractional averaging principle for interacting slow-fast systems.
Probability 60G22, 60H10, 37A25