no code implementations • 7 Nov 2023 • Direnc Pekaslan, Jose Maria Alonso-Moral, Kasun Bandara, Christoph Bergmeir, Juan Bernabe-Moreno, Robert Eigenmann, Nils Einecke, Selvi Ergen, Rakshitha Godahewa, Hansika Hewamalage, Jesus Lago, Steffen Limmer, Sven Rebhan, Boris Rabinovich, Dilini Rajapasksha, Heda Song, Christian Wagner, Wenlong Wu, Luis Magdalena, Isaac Triguero
These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors.
no code implementations • 21 Dec 2022 • Christoph Bergmeir, Frits de Nijs, Abishek Sriramulu, Mahdi Abolghasemi, Richard Bean, John Betts, Quang Bui, Nam Trong Dinh, Nils Einecke, Rasul Esmaeilbeigi, Scott Ferraro, Priya Galketiya, Evgenii Genov, Robert Glasgow, Rakshitha Godahewa, Yanfei Kang, Steffen Limmer, Luis Magdalena, Pablo Montero-Manso, Daniel Peralta, Yogesh Pipada Sunil Kumar, Alejandro Rosales-Pérez, Julian Ruddick, Akylas Stratigakos, Peter Stuckey, Guido Tack, Isaac Triguero, Rui Yuan
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
no code implementations • 23 Nov 2022 • Steffen Limmer, Alberto Martinez Alba, Nicola Michailow
This paper introduces a physics-informed machine learning approach for pathloss prediction.
no code implementations • 23 Jan 2021 • Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Baeck
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region.
no code implementations • 20 Jul 2020 • Daniel Hein, Steffen Limmer, Thomas A. Runkler
In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark.
1 code implementation • 4 Sep 2017 • Steffen Limmer, Slawomir Stanczak
Owing to the specific structure, it is shown that the centroid can be computed analytically by extending a recent result that facilitates the volume computation of polytopes via Laplace transformations.
1 code implementation • 26 May 2016 • Steffen Limmer, Sławomir Stańczak
In this context, we analyze the Bayesian mean-square-error (MSE) for two types of estimators: (i) a linear estimator and (ii) a structured estimator composed of a linear operator followed by a Cartesian product of univariate nonlinear mappings.