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pro vyhledávání: '"Scholl, Philipp"'
Recent advancements in machine learning have transformed the discovery of physical laws, moving from manual derivation to data-driven methods that simultaneously learn both the structure and parameters of governing equations. This shift introduces ne
Externí odkaz:
http://arxiv.org/abs/2410.09938
Autor:
Bieker, Katharina, Kussaba, Hugo Tadashi, Scholl, Philipp, Jung, Jaesug, Swikir, Abdalla, Haddadin, Sami, Kutyniok, Gitta
Many systems occurring in real-world applications, such as controlling the motions of robots or modeling the spread of diseases, are switched impulsive systems. To ensure that the system state stays in a safe region (e.g., to avoid collisions with ob
Externí odkaz:
http://arxiv.org/abs/2407.20084
Autor:
Scholl, Philipp, Iskandar, Maged, Wolf, Sebastian, Lee, Jinoh, Bacho, Aras, Dietrich, Alexander, Albu-Schäffer, Alin, Kutyniok, Gitta
In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with hu
Externí odkaz:
http://arxiv.org/abs/2310.16688
The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of financial markets from given data. Various methods exist to address the pro
Externí odkaz:
http://arxiv.org/abs/2310.05537
Autor:
Chua, Yvonne, Cooray, Sankha, Cortes, Juan Pablo Forero, Denny, Paul, Dupuch, Sonia, Garbett, Dawn L, Nassani, Alaeddin, Cao, Jiashuo, Qiao, Hannah, Reis, Andrew, Reis, Deviana, Scholl, Philipp M., Sridhar, Priyashri Kamlesh, Suriyaarachchi, Hussel, Taimana, Fiona, Tanga, Vanessa, Weerasinghe, Chamod, Wen, Elliott, Wu, Michelle, Wu, Qin, Zhang, Haimo, Nanayakkara, Suranga
Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often c
Externí odkaz:
http://arxiv.org/abs/2304.03450
Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations with the use of machine learning techniques. In contrast to classical methods which assume the structure of the equation to be kn
Externí odkaz:
http://arxiv.org/abs/2210.08342
Safe Policy Improvement (SPI) is an important technique for offline reinforcement learning in safety critical applications as it improves the behavior policy with a high probability. We classify various SPI approaches from the literature into two gro
Externí odkaz:
http://arxiv.org/abs/2208.00724
Autor:
Scholl, Philipp, Iskandar, Maged, Wolf, Sebastian, Lee, Jinoh, Bacho, Aras, Dietrich, Alexander, Albu-Schäffer, Alin, Kutyniok, Gitta
Publikováno v:
In Robotics and Computer-Integrated Manufacturing October 2024 89
Safe Policy Improvement (SPI) aims at provable guarantees that a learned policy is at least approximately as good as a given baseline policy. Building on SPI with Soft Baseline Bootstrapping (Soft-SPIBB) by Nadjahi et al., we identify theoretical iss
Externí odkaz:
http://arxiv.org/abs/2201.12175
Autor:
Ouattara, Aurélien, Bulté, Matthieu, Lin, Wan-Ju, Scholl, Philipp, Veit, Benedikt, Ziakas, Christos, Felice, Florian, Virlogeux, Julien, Dikos, George
Extra-large datasets are becoming increasingly accessible, and computing tools designed to handle huge amount of data efficiently are democratizing rapidly. However, conventional statistical and econometric tools are still lacking fluency when dealin
Externí odkaz:
http://arxiv.org/abs/2106.10341