Search Results for author: Olivier Sprangers

Found 4 papers, 3 papers with code

Hierarchical Forecasting at Scale

1 code implementation19 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.

Time Series

Parameter Efficient Deep Probabilistic Forecasting

1 code implementation6 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.

Probabilistic Time Series Forecasting Time Series

Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression

1 code implementation3 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.

regression Time Series Analysis

Reinforcement learning for port-Hamiltonian systems

no code implementations21 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.

reinforcement-learning Reinforcement Learning (RL)

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