4 citations · 11 across the 6 of their papers we have counts for
9 papers
Predict+Optimize Problem in Renewable Energy Scheduling
Christoph Bergmeir, Frits de Nijs, Evgenii Genov +25
Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are criti…
Physics-informed neural networks for pathloss prediction
Steffen Limmer, Alberto Martinez Alba, Nicola Michailow
This paper introduces a physics-informed machine learning approach for pathloss prediction. This is achieved by including in the training phase simultaneously (i) physical dependen…
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization
Yali Wang, Steffen Limmer, Markus Olhofer +2
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region. It is named Automatic Preference base…
Interpretable Control by Reinforcement Learning
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. The novel RL m…
Optimal deep neural networks for sparse recovery via Laplace techniques
Steffen Limmer, Slawomir Stanczak
This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we…
Towards optimal nonlinearities for sparse recovery using higher-order statistics
Steffen Limmer, Sławomir Stańczak
We consider machine learning techniques to develop low-latency approximate solutions to a class of inverse problems. More precisely, we use a probabilistic approach for the problem…
A Simple Algorithm for Approximation by Nomographic Functions
Steffen Limmer, Jafar Mohammadi, Slawomir Stanczak
This paper introduces a novel algorithmic solution for the approximation of a given multivariate function by a nomographic function that is composed of a one-dimensional continuous…
On -norm Computation over Multiple-Access Channels
Steffen Limmer, Slawomir Stanczak
This paper addresses some aspects of the general problem of information transfer and distributed function computation in wireless networks. Many applications of wireless technology…
Exploiting Interference for Efficient Distributed Computation in Cluster-based Wireless Sensor Networks
Steffen Limmer, Slawomir Stanczak, Mario Goldenbaum +1
This invited paper presents some novel ideas on how to enhance the performance of consensus algorithms in distributed wireless sensor networks, when communication costs are conside…