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Number of items at this level: 81.

A

Araslanov, N. and Roth, S. (2020):
Single-stage semantic segmentation from image labels.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),, June, [Online-Edition: https://ieeexplore.ieee.org/abstract/document/9156636],
[Conference or Workshop Item]

Alt, B. and Šošić, A. and Koeppl, H. (2019):
Correlation Priors for Reinforcement Learning.
In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Kanada, 09.12.-13.12.2019, [Online-Edition: https://papers.nips.cc/paper/9564-correlation-priors-for-rei...],
[Conference or Workshop Item]

Araslanov, Nikita and Rothkopf, Constantin A and Roth, Stefan (2019):
Actor-Critic Instance Segmentation.
Long Beach, California, United States, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8237-8246, [Conference or Workshop Item]

Alt, Bastian and Messer, Michael and Roeper, Jochen and Schneider, Gaby and Koeppl, Heinz (2018):
Non-Parametric Bayesian Inference for Change Point Detection in Neural Spike Trains.
In: 2018 IEEE Statistical Signal Processing Workshop (SSP 2018), Freiburg im Breisgau, Germany, 10.-13.06., [Conference or Workshop Item]

Ahmad Sharbafi, Maziar and Shin, Hirofumi and Zhao, Guoping and Hosoda, Koh and Seyfarth, Andre (2017):
Electric-Pneumatic Actuator: A New Muscle for Locomotion.
In: Actuators, 6 (4), MDPI, ISSN 2076-0825,
DOI: 10.3390/act6040030,
[Online-Edition: https://doi.org/10.3390/act6040030],
[Article]

B

Belousov, Boris and Neumann, Gerhard and Rothkopf, Constantin A and Peters, Jan R (2016):
Catching heuristics are optimal control policies.
In: 30th Conference on Neural Information Processing Systems, Barcelona, Spain, 05.-10.12.2016, pp. 1426-1434, [Online-Edition: http://www.ausy.tu-darmstadt.de/uploads/Site/EditPublication...],
[Conference or Workshop Item]

D

Depeweg, S. and Rothkopf, C. A. and Jäkel, F. (2018):
Solving Bongard Problems with a visual language and pragmatic reasoning.
In: arXiv, [Online-Edition: https://arxiv.org/abs/1804.04452],
[Report]

De Raedt, Luc and Kersting, Kristian and Natarajan, Sriraam and Poole, David (2016):
Statistical Relational Artificial Intelligence: Logic, Probability and Computation.
In: Synthesis Lectures on Artificial Intelligence and Machine Learning, San Rafael, California, USA, Morgan and Claypoo Publishers, ISBN 9781627058414,
[Book]

Daniel, C. and Neumann, G. and Kroemer, O. and Peters, J. (2016):
Hierarchical Relative Entropy Policy Search.
In: Journal of Machine Learning Research, (93), 17. pp. 1-50, [Online-Edition: http://jmlr.org/papers/v17/15-188.html],
[Article]

G

Galassi, Andrea and Kersting, Kristian and Lippi, Marco and Shao, Xiaoting and Torroni, Paolo (2020):
Neural-Symbolic Argumentation Mining : An Argument in Favor of Deep Learning and Reasoning.
In: Frontiers in Big Data, (2), 52. DOI: 10.3389/fdata.2019.00052,
[Online-Edition: https://www.frontiersin.org/articles/10.3389/fdata.2019.0005...],
[Article]

Gomez-Gonzalez, S. and Prokudin, S. and Schölkopf, B. and Peters, J. (2020):
Real Time Trajectory Prediction Using Deep Conditional Generative Models.
In: IEEE Robotics and Automation Letters (RA-L), (2), 5. pp. 970-976, DOI: 10.1109/LRA.2020.2966390,
[Article]

Galuske, Ralf A. W. and Munk, Matthias H. J. and Singer, Wolf (2019):
Relation between gamma oscillations and neuronal plasticity in the visual cortex.
In: Proceedings of the National Academy of Sciences of the United States of America, 116 (46), pp. 23317-23325, ISSN 1091-6490,
DOI: 10.1073/pnas.1901277116,
[Article]

Geyer, H. and Seyfarth, A. (2019):
Neuromuscular Control Models of Human Locomotion.
In: Humanoid Robotics: A Reference., Springer, pp. 979-1007, DOI: 10.1007/978-94-007-6046-2_45,
[Book Section]

Grimmer, M. and Riener, R. and Walsh, C. J. and Seyfarth, A. (2019):
Mobility related physical and functional losses due to aging and disease - a motivation for lower limb exoskeletons.
In: Journal of neuroengineering and rehabilitation, (2), 16. [Online-Edition: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s1...],
[Article]

Gast, J. and Roth, S. (2018):
Lightweight probabilistic deep networks.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), [Conference or Workshop Item]

Gurevych, Iryna and Meyer, Christian M and Binnig, Carsten and Fürnkranz, Johannes and Kersting, Kristian and Roth, Stefan and Simpson, Edwin Gelbukh, Alexander (ed.) (2017):
Interactive Data Analytics for the Humanities.
In: Computational Linguistics and Intelligent Text Processing, Budapest, Hungary, Springer International Publishing, In: International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017, Budapest, Hungary, 17-23 April, 2017, pp. 527-549, ISBN 978-3-319-77113-7,
[Conference or Workshop Item]

Gershman, S. J. and Tenenbaum, J. and Jäkel, F. (2016):
Discovering hierarchical motion structure.
In: Vision Research, (126), pp. 232-241, DOI: 10.1016/j.visres.2015.03.004,
[Online-Edition: http://dx.doi.org/10.1016/j.visres.2015.03.004],
[Article]

Grimmer, Martin and Holgate, Matthew and Holgate, Robert and Boehler, Alexaander and Ward, Jeffrey and Hollander, Kevin and Sugar, Thomas and Seyfarth, André (2016):
A powered prosthetic ankle joint for walking and running.
In: BioMedical Engineering OnLine, (3), 15. p. 141, [Online-Edition: https://biomedical-engineering-online.biomedcentral.com/arti...],
[Article]

H

Hur, J. and Roth, S. (2020):
Optical flow estimation in the deep learning age.
In: Modelling Human Motion, Cham, Springer, pp. 119-140, DOI: 10.1007/978-3-030-46732-6_7,
[Online-Edition: https://link.springer.com/chapter/10.1007/978-3-030-46732-6_...],
[Book Section]

Hoppe, David and Rothkopf, Constantin A (2019):
Multi-step planning of eye movements in visual search.
In: Scientific reports, 9 (1), Nature Publishing Group, pp. 1-12, [Article]

Hoppe, David and Helfmann, Stefan and Rothkopf, Constantin A (2018):
Humans quickly learn to blink strategically in response to environmental task demands.
In: Proceedings of the National Academy of Sciences, (9), 115. National Academy of the Sciences, pp. 2246-2251, [Article]

Hoppe, David and Rothkopf, Constantin A (2016):
Learning rational temporal eye movement strategies.
In: Proceedings of the National Academy of Sciences, (29), 113. National Acad Sciences, pp. 8332-8337, [Article]

Hur, J. and Roth, S (2016):
Joint optical flow and temporally consistent semantic segmentation.
In: Computer Vision – ECCV 2016 Workshops Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part I, Springer International Publishing, [Book Section]

J

Jentzsch, Sophie and Schramowski, Patrick and Rothkopf, Constantin and Kersting, Kristian (2019):
Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices.
Honolulu, HI, USA, [Conference or Workshop Item]

Jäkel, Frank and Wichmann, F. A. (2019):
Spatial four-alternative forced-choice method is the preferred psychophysical method for naive observers.
In: Journal of Vision, (6), pp. 1307-1322, [Article]

K

Kossen, Jannik and Stelzner, Karl and Hussing, Marcel and Voelcker, Claas and Kersting, Kristian (2020):
Structured Object-Aware Physics Prediction for Video Modeling and Planning.
In: Proceedings of the International Conference on Learning Reresentations (ICLR), ., [Online-Edition: https://openreview.net/forum?id=B1e-kxSKDH],
[Conference or Workshop Item]

Kollegger, G. and Ewerton, M. and Wiemeyer, J. and Peters, J. (2017):
BIMROB — Bidirectional Interaction Between Human and Robot for the Learning of Movements.
In: Proceedings of the International Symposium on Experimental Robotics (ISER)",, springer international publishing, pp. 151-163, [Online-Edition: https://doi.org/10.1007/978-3-319-67846-7_15],
[Book Section]

Kersting, Kristian and Mladenov, M. and Tokmakov, P. (2017):
Relational Linear Programming.
In: Artificial Intelligence Journal (AIJ), 244. pp. 188-216, [Online-Edition: https://www.sciencedirect.com/science/article/abs/pii/S00043...],
[Article]

L

Linzner, D. and Heinz, K. (2020):
A Variational Perturbative Approach to Planning in Graph-based Markov Decision Processes.
In: AAAI-20 - Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, USA, February 7-12, 2020, [Conference or Workshop Item]

Lioutikov, Rudolf and Maeda, Guilherme and Veiga, Filipe and Kersting, Kristian and Peters, Jan (2020):
Learning Attribute Grammars for Movement Primitive Sequencing.
In: International Journal of Robotics Research (IJRR), (1), 39. DOI: 10.1177/0278364919868279,
[Article]

Loeckel, S. and Peters, J. and Van Vliet, P. (2020):
A Probabilistic Framework for Imitating Human Race Driver Behavior.
In: IEEE Robotics and Automation Letters (RA-L), (2), 5. pp. 2086-2093, DOI: 10.1109/LRA.2020.2970620 Ci,
[Article]

Lutter, M. and Ritter, C. and Peters, J. (2019):
Deep Lagrangian Networks: Using physics as model prior for Deep Learning.
New Orleans, In: 7th International Conference on Learning Representations (ICLR), [Online-Edition: https://arxiv.org/abs/1907.04490],
[Conference or Workshop Item]

Linzner, D. and Koeppl, H. (2018):
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data.
In: 32. Conference on Neural Information Processing Systems, Montreal, Canada, December 3-8, 2018, [Conference or Workshop Item]

Lioutikov, R. and Maeda, G. and Veiga, F. F. and Kersting, K. and Peters, J. (2018):
Inducing Probabilistic Context-Free Grammars for the Sequencing of Robot Movement Primitives.
In: Proceedings of the International Conference on Robotics and Automation (ICRA), In: 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, May 21-26, 2018, [Online-Edition: http://www.ausy.tu-darmstadt.de/uploads/Team/RudolfLioutikov...],
[Conference or Workshop Item]

M

Melnik, Andrew and Schüler, Felix and Rothkopf, Constantin A and König, Peter (2018):
The world as an external memory: the price of saccades in a sensorimotor task.
In: Frontiers in behavioral neuroscience, 12Frontiers, p. 253, [Article]

Molina, A. and Vergari, N. and Di Mauro, F. and Esposito, S. and Natarajan, K. and Kersting, K. (2018):
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains.
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), o.A., [Online-Edition: https://www.aaai.org/Conferences/AAAI/aaai.php],
[Conference or Workshop Item]

O

Osa, T. and Pajarinen, J. and Neumann, G. and Bagnell, J.A. and Abbeel, P. and Peters, J. (2018):
An Algorithmic Perspective on Imitation Learning.
In: Foundations and Trends in Robotics, 7 (1-2), pp. 1-179, ISSN 1935-8253, 1935-8261 (elektronisch),
[Online-Edition: https://www.nowpublishers.com/article/DownloadSummary/ROB-05...],
[Article]

P

Peharz, R and Vergari, A. and Stelzner, K. and Molina, A. and Shao, X. and Trapp, M. and Kersting, K. and Ghahramani, Z. (2019):
Random Sum-Product Networks: A Simple but Effective Approach to Probabilistic Deep Learning.
Tel Aviv, Israel, In: Proceedings of the Conference on Uncertainty in AI (UAI), July 22-25, 2019, [Online-Edition: http://www.auai.org/],
[Conference or Workshop Item]

Parmas, P. and Doya, K. and Rasmussen, C. and Peters, J. (2018):
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos.
In: International Conference on Machine Learning, [Article]

Plötz, T. and Roth, S. (2018):
Neural nearest neighbors networks.
In: Advances in Neural Information Processing Systems (NeurIPS), 31. [Article]

Plötz, T. and Wannenwetsch, A. S. and Roth, S. (2018):
Stochastic variational inference with gradient linearization.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), [Conference or Workshop Item]

R

Rueckert, Elmar and Kappel, David and Tanneberg, Daniel and Pecevski, Dejan and Peters, Jan (2016):
Recurrent Spiking Networks Solve Planning Tasks.
In: Scientific Reports, 6 (21142), Nature Publ. Group, ISSN 2045-2322,
[Online-Edition: http://dx.doi.org/10.1038/srep21142],
[Article]

Rothkopf, Constantin A. (2016):
Minimal Sequential Gaze Models for Inferring Walkers’ Tasks.
In: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. ACM, o.A., [Online-Edition: https://dl.acm.org/doi/abs/10.1145/2957265.2965015],
[Conference or Workshop Item]

Richter, S. R. and Vineet, V. and Roth, S. and Koltun, V. (2016):
Playing for Data: Ground Truth from Computer Games.
In: Computer Vision – ECCV 2016 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II, Cham, Springer, pp. 102-118, [Online-Edition: https://link.springer.com/chapter/10.1007/978-3-319-46475-6_...],
[Book Section]

S

Stoilova, V. V. and Knauer, B. and Berg, S. and Rieber, E. and Jäkel, F. and Stüttgen, M. C. (2020):
Auditory cortex reflects goal-directed movement but is not necessary for behavioral adaptation in sound-cued reward tracking.
In: Journal of Neurophysiology, American Physiological Society, ISSN 0022-3077,
DOI: 10.1152/jn.00736.2019,
[Article]

Stelzer, K. and Peharz, R. and Kersting, K. (2020):
Faster Attend-Infer-Repeat with Tractable Probabilistic Models.
In: Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, In: 36th International Conference on Machine Learning (ICML), June 10th to June 15th, 2019, [Conference or Workshop Item]

Schramowski, Patrick and TURAN, Cigdem and Jentzsch, Sophie and Rothkopf, Constantin and Kersting, Kristian (2020):
The Moral Choice Machine.
In: Frontiers in Artificial Intelligence, 3Frontiers, p. 36, [Online-Edition: https://www.frontiersin.org/articles/10.3389/frai.2020.00036...],
[Article]

Stock-Homburg, Ruth and Peters, J. and Schneider, K. and Prasad, V. and Nukovic, L. (2020):
Evaluation of the Handshake Turing Test for anthropomorphic Robots.
In: Proceedings of the ACM/IEEE International Conference on Human Robot Interaction (HRI), ., [Online-Edition: https://arxiv.org/abs/2001.10464],
[Conference or Workshop Item]

Shao, X. and Molina, A. and Vergari, A. and Stelzner, K. and Peharz, R and Liebig, T. and Kersting, K. (2019):
Conditional sum-product networks: Imposing structure on deep probabilistic architectures.
In: Working Notes of the ICML 2019 Workshop on Tractable Probabilistic Models (TPM), [Online-Edition: https://arxiv.org/abs/1905.08550],
[Report]

Saeedan, Faraz and Weber, Nicolas and Goesele, Michael and Roth, Stefan (2018):
Detail-preserving pooling in deep networks.
In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, [Conference or Workshop Item]

Sergieh, Hatem Mousselly and Botschen, Teresa and Gurevych, Iryna and Roth, Stefan (2018):
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning.
In: Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics (*SEM), Stroudsburg PA, USA, In: The Seventh Joint Conference on Lexical and Computational Semantics (*SEM), New Orleans LA, USA, 05.06.2018--06.06.2018, pp. 225-234, DOI: 10.18653/v1/S18-2027,
[Online-Edition: http://aclweb.org/anthology/S18-2027],
[Conference or Workshop Item]

Seyfarth, André and Sharbafi, M. A. and Zhao, G. and Schumacher, C. (2018):
Modular composition of human gaits through locomotor subfunctions and sensor-motor-maps.
In: Wearable Robotics: Challenges and Trends Proceedings of the 4th International Symposium on Wearable Robotics, WeRob2018, October 16-20, 2018, Pisa, Italy, Pisa, Italy, Springer, In: International Symposium on Wearable Robotics, Pisa, Italy, 16-20. October 2018, pp. 339-343, ISBN 978-3-030-01887-0,
[Online-Edition: https://link.springer.com/chapter/10.1007%2F978-3-030-01887-...],
[Conference or Workshop Item]

Seyfarth, André and Schumacher, Christian (2018):
Teaching locomotion biomechanics-From concepts to applications.
In: European Journal of Physics, [Online-Edition: https://iopscience.iop.org/article/10.1088/1361-6404/aaf136],
[Article]

Schumacher, C. and Grimmer, M. and Scherf, A. and Zhao, G. and Beckerle, P. and Seyfarth, André (2018):
A Movement Manipulator to Introduce Temporary and Local Perturbations in Human Hopping.
In: 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Enschede, Netherlands, 26-29 Aug. 2018, DOI: 10.1109/BIOROB.2018.8487900,
[Online-Edition: https://ieeexplore.ieee.org/document/8487900],
[Conference or Workshop Item]

Schmitt, Felix and Bieg, Hans-Joachim and Herman, Michael and Rothkopf, Constantin A (2017):
I see what you see: Inferring sensor and policy models of human real-world motor behavior.
San Francisco, California USA, In: Thirty-First AAAI Conference on Artificial Intelligence, [Conference or Workshop Item]

Schumacher, Christian and Seyfarth, André (2017):
Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping.
In: Frontiers in Computational Neuroscience, 11ISSN 1662-5188,
DOI: 10.3389/fncom.2017.00108,
[Online-Edition: https://doi.org/10.3389/fncom.2017.00108],
[Article]

Sharbafi, Maziar A. and Seyfarth, Andre and Zhao, Guoping (2017):
Locomotor Sub-functions for Control of Assistive Wearable Robots.
In: Frontiers in Neurorobotics, 11Frontiers, ISSN 1662-5218,
DOI: 10.3389/fnbot.2017.00044,
[Online-Edition: http://journal.frontiersin.org/article/10.3389/fnbot.2017.00...],
[Article]

Sharbafi, M. A. and Seyfarth, André (2017):
How locomotion sub-functions can control walking at different speeds?
In: Journal of Biomechanics, 53. Elsevier, pp. 163-170, [Online-Edition: https://www.sciencedirect.com/science/article/abs/pii/S00219...],
[Article]

Sharbafi, M. A. and Rashty, A.M. and Rode, C. and Seyfarth, André (2017):
Reconstruction of human swing leg motion with passive biarticular muscle models.
In: Human movement science, 52. pp. 96-107, [Article]

Sutter, T. and Ganguly, A. and Koeppl, H. (2016):
A variational approach to path estimation and parameter inference of hidden diffusion processes.
In: Journal of Machine Learning Research, (190), 17. pp. 1-37, [Online-Edition: http://jmlr.org/papers/v17/16-075.html],
[Article]

Seyfarth, André and Geyer, Hartmut and Lipfert, Susanne and Rummel, J. and Blum, Yvonne and Maus, Moritz and Maykranz, D. (2016):
Whole‐Body Mechanics : How Leg Compliance Shapes the Way We Move.
In: Understanding Mammalian Locomotion: Concepts and Applications, Wiley, pp. 173-191, DOI: 10.1002/9781119113713.ch7,
[Online-Edition: https://onlinelibrary.wiley.com/doi/10.1002/9781119113713.ch...],
[Book Section]

T

Teso, S and Kersting, K. (2019):
Explanatory interactive machine learning.
In: Proceedings of the 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES)., Honolulu, HI, USA, In: 2nd AAAI/ACM Conference on AI, Ethics, and Society, January 27-28, 2019, [Online-Edition: https://dl.acm.org/doi/10.1145/3306618.3314293],
[Conference or Workshop Item]

Tanneberg, Daniel and Peters, Jan and Rueckert, Elmar (2018):
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks.
In: Neural Networks, Elsevier, ISSN 0893-6080,
DOI: 10.1016/j.neunet.2018.10.005,
[Online-Edition: https://arxiv.org/pdf/1802.08013.pdf],
[Article]

Tangkaratt, V. and van Hoof, H. and Parisi, S. and Neumann, G. and Peters, J. and Sugiyama, M. (2017):
Policy Search with High-Dimensional Context Variables.
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), [Online-Edition: http://www.ausy.tu-darmstadt.de/uploads/Site/EditPublication...],
[Conference or Workshop Item]

V

Vinogradska, J. and Bischoff, .B. and Koller, T. and Achterhold, J. and Peters, J. (2020):
Numerical Quadrature for Probabilistic Policy Search.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, (1), 42. [Online-Edition: https://ieeexplore.ieee.org/document/8520758],
[Article]

Vergari, A. and Molina, A. and Peharz, R. and Ghahramani, Z. and Kersting, K. and Vlalera, I. (2019):
Automatic Bayesian Density Analysis.
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), ., [Online-Edition: https://arxiv.org/abs/1807.09306],
[Conference or Workshop Item]

Veeravasarapu, VSR and Rothkopf, Constantin and Visvanathan, Ramesh (2017):
Model-driven simulations for computer vision.
Santa Rosa, California, In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1063-1071, [Conference or Workshop Item]

Veeravasarapu, VSR and Rothkopf, Constantin and Visvanathan, Ramesh (2017):
Adversarially tuned scene generation.
Honolulu, HI, USA, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2587-2595, [Conference or Workshop Item]

W

Wichmann, F. A. and Jäkel, F. Wixted, J. T. and Wagenmakers, E. J. (eds.) (2018):
Methods in Psychophysics.
In: Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, pp. 265-306, [Online-Edition: https://www.wiley.com/en-us/Stevens%27+Handbook+of+Experimen...],
[Book Section]

Wang, Quan and Rothkopf, Constantin A and Triesch, Jochen (2017):
A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.
In: PLoS computational biology, (8), 13. Public Library of Science, pp. e1005632, [Article]

Wang, Z. and Boularias, A. and Muelling, K. and Schoelkopf, B. and Peters, J. (2017):
Anticipatory Action Selection for Human-Robot Table Tennis.
In: Artificial Intelligence, 247pp. 399-414, ISSN 0004-3702,
[Online-Edition: http://www.sciencedirect.com/science/article/pii/S0004370214...],
[Article]

Y

Yang, S. and Koeppl, H. (2018):
Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis.
In: IEEE International Conference on Data Mining (ICDM'18), Singapore, 17.-20. November 2018, [Conference or Workshop Item]

Yang, S. and Koeppl, H. (2018):
Dependent Relational Gamma Process Models for Longitudinal Networks.
80In: Proceedings of Machine Learning Research (PMLR), In: Thirty-fifth International Conference on Machine Learning, Stockholm, Denmark, July 10-15, 2018, pp. 5547-5556, [Online-Edition: https://icml.cc/Conferences/2018/Schedule?showEvent=1942],
[Conference or Workshop Item]

Yang, S. and Koeppl, H. (2018):
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks.
In: AAAI 2018, Association for the Advancement of Artificial Intelligence, New Orleans, 2018, [Conference or Workshop Item]

Z

Zhang, Ruohan and Zhang, Shun and Tong, Matthew H and Cui, Yuchen and Rothkopf, Constantin A and Ballard, Dana H and Hayhoe, Mary M (2018):
Modeling sensory-motor decisions in natural behavior.
In: PLoS computational biology, 14 (10), Public Library of Science, pp. e1006518, [Article]

Zednik, C. and Jäkel, F. (2016):
Bayesian reverse-engineering considered as a research strategy for cognitive science.
In: Synthese, pp. 3951-3985, DOI: 10.1007/s11229-016-1180-3,
[Online-Edition: https://doi.org/10.1007/s11229-016-1180-3],
[Article]

Š

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2018):
A Bayesian Approach to Policy Recognition and State Representation Learning.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, (6), 40. pp. 1295-1308, DOI: 10.1109/TPAMI.2017.2711024,
[Online-Edition: https://ieeexplore.ieee.org/document/7937852],
[Article]

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2018):
Reinforcement Learning in a Continuum of Agents.
In: Swarm Intelligence, 12 (1), pp. 23-51, DOI: 10.1007/s11721-017-0142-9,
[Online-Edition: http://rdcu.be/wKay],
[Article]

Šošić, A. and Rueckert, E. and Peters, J. and Zoubir, A. M. and Koeppl, H. (2018):
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling.
In: Journal of Machine Learning Research, 19 (69), pp. 1-45, [Online-Edition: http://www.jmlr.org/papers/volume19/18-113/18-113.pdf],
[Article]

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2018):
Inverse Reinforcement Learning via Nonparametric Subgoal Modeling.
In: AAAI Spring Symposium on Data-Efficient Reinforcement Learning, [Online-Edition: https://aaai.org/ocs/index.php/SSS/SSS18/paper/view/17531/15...],
[Conference or Workshop Item]

Šošić, A. and Zoubir, A. M. and Koeppl, H. (2016):
Policy Recognition via Expectation Maximization.
In: IEEE International Conference on Acoustics, Speech and Signal Processing, DOI: 10.1109/ICASSP.2016.7472589,
[Online-Edition: https://doi.org/10.1109/icassp.2016.7472589],
[Conference or Workshop Item]

This list was generated on Thu Sep 17 01:50:00 2020 CEST.