Publications

@article{dave2025learning,

title = {Learning Robust Representations for Visual Reinforcement Learning via Task-Relevant Mask Sampling},

author = {Vedant Dave and Ozan Özdenizci and Elmar Rückert},

year  = {2025},

date = {2025-08-11},

urldate = {2025-08-11},

journal = {Transactions on Machine Learning Research},

keywords = {machine learning, Reinforcement Learning, Representation Learning},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Herzog2022,

title = {Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques},

author = {Rebecca Herzog and Till M Berger and Martje Gesine Pauly and Honghu Xue and Elmar Rueckert and Alexander Munchau and Tobias B{"a}umer and Anne Weissbach},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/4qoooTyjFfBwYEZ},

doi = {10.3389/fnins.2022.987472},

year  = {2022},

date = {2022-09-19},

urldate = {2022-09-19},

journal = {Frontiers in Neuroscience},

keywords = {Human motion analysis, human motor control, machine learning, Medical Robotics},

pubstate = {published},

tppubtype = {article}

}

@article{Rueckert2021,

title = {Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation},

author = {Honghu Xue and Rebecca Herzog and Till M. Berger and Tobias Bäumer and Anne Weissbach and Elmar Rueckert},

editor = {Kensuke Harada},

url = {https://cps.unileoben.ac.at/wp/Frontiers2021Xue.pdf},

issn = {2296-9144},

year  = {2021},

date = {2021-08-14},

urldate = {2021-08-14},

journal = {Frontiers in Robotics and AI },

volume = {8},

abstract = {In medical tasks such as human motion analysis, computer-aided auxiliary systems have become preferred choice for human experts for its high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives(ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with10participants. The results validate ProMPs as a robust and effective feature extractor for human motions.},

keywords = {Finger Tapping Motion, Human motion analysis, machine learning, Probabilistic Movement Primitives, Transcranial current stimulation},

pubstate = {published},

tppubtype = {article}

}

@article{dave2025learning,

title = {Learning Robust Representations for Visual Reinforcement Learning via Task-Relevant Mask Sampling},

author = {Vedant Dave and Ozan Özdenizci and Elmar Rückert},

year  = {2025},

date = {2025-08-11},

urldate = {2025-08-11},

journal = {Transactions on Machine Learning Research},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Herzog2022,

title = {Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques},

author = {Rebecca Herzog and Till M Berger and Martje Gesine Pauly and Honghu Xue and Elmar Rueckert and Alexander Munchau and Tobias B{"a}umer and Anne Weissbach},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/4qoooTyjFfBwYEZ},

doi = {10.3389/fnins.2022.987472},

year  = {2022},

date = {2022-09-19},

urldate = {2022-09-19},

journal = {Frontiers in Neuroscience},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rueckert2021,

title = {Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation},

author = {Honghu Xue and Rebecca Herzog and Till M. Berger and Tobias Bäumer and Anne Weissbach and Elmar Rueckert},

editor = {Kensuke Harada},

url = {https://cps.unileoben.ac.at/wp/Frontiers2021Xue.pdf},

issn = {2296-9144},

year  = {2021},

date = {2021-08-14},

urldate = {2021-08-14},

journal = {Frontiers in Robotics and AI },

volume = {8},

abstract = {In medical tasks such as human motion analysis, computer-aided auxiliary systems have become preferred choice for human experts for its high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives(ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with10participants. The results validate ProMPs as a robust and effective feature extractor for human motions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}