2 citations · 3 across the 3 of their papers we have counts for
3 papers
stat.ML2024★ 2 cited
Causal Forecasting for Pricing
Douglas Schultz, Johannes Stephan, Julian Sieber +4
This paper proposes a novel method for demand forecasting in a pricing context. Here, modeling the causal relationship between price as an input variable to demand is crucial becau…
cs.LG2023★ 1 cited
Deep Learning based Forecasting: a case study from the online fashion industry
Manuel Kunz, Stefan Birr, Mones Raslan +13
Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry's set of particular challenges. These…
stat.ML2022
Quantitative Universal Approximation Bounds for Deep Belief Networks
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 o…