1 code implementation • 19 Oct 2023 • Olivier Sprangers, Wander Wadman, Sebastian Schelter, Maarten de Rijke
We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform, resulting in an improved forecasting performance of 2% at the product level.
1 code implementation • 6 Dec 2021 • Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models.
1 code implementation • 3 Jun 2021 • Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
We propose Probabilistic Gradient Boosting Machines (PGBM), a method to create probabilistic predictions with a single ensemble of decision trees in a computationally efficient manner.
no code implementations • 21 Dec 2012 • Olivier Sprangers, Gabriel A. D. Lopes, Robert Babuska
The parameters of the control law are found using actor-critic reinforcement learning, enabling learning near-optimal control policies satisfying a desired closed-loop energy landscape.