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Number of items at this level (without sub-levels): 82.

A

Alt, B. ; Schultheis, M. ; Koeppl, H. (2020):
POMDPs in Continuous Time and Discrete Spaces.
In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020),
34th Conference on Neural Information Processing Systems, virtual Conference, 06.-12.12.2020, [Conference or Workshop Item]

Araslanov, N. ; Roth, S. (2020):
Single-stage semantic segmentation from image labels.
In: Proceedings : 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR 2020, pp. 4252-4261,
IEEE, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, virtual Conference, 13.-19.06.2020, ISBN 978-1-7281-7168-5,
DOI: 10.1109/CVPR42600.2020.00431,
[Conference or Workshop Item]

Alt, B. ; Šošić, A. ; Koeppl, H. (2019):
Correlation Priors for Reinforcement Learning.
33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Kanada, 09.12.-13.12.2019, [Conference or Workshop Item]

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

Alt, Bastian ; Messer, Michael ; Roeper, Jochen ; Schneider, Gaby ; Koeppl, Heinz (2018):
Non-Parametric Bayesian Inference for Change Point Detection in Neural Spike Trains.
IEEE, 2018 IEEE Statistical Signal Processing Workshop (SSP 2018), Freiburg im Breisgau, Germany, 10.-13.06., ISBN 978-1-5386-1571-3,
DOI: 10.1109/SSP.2018.8450787,
[Conference or Workshop Item]

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

B

Belousov, Boris ; Neumann, Gerhard ; Rothkopf, Constantin A ; Peters, Jan R (2016):
Catching heuristics are optimal control policies.
pp. 1426-1434, 30th Conference on Neural Information Processing Systems, Barcelona, Spain, 05.-10.12.2016, [Conference or Workshop Item]

D

Depeweg, S. ; Rothkopf, C. A. ; Jäkel, F. (2018):
Solving Bongard Problems with a visual language and pragmatic reasoning.
In: arXiv, [Report]

De Raedt, Luc ; Kersting, Kristian ; Natarajan, Sriraam ; 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. ; Neumann, G. ; Kroemer, O. ; Peters, J. (2016):
Hierarchical Relative Entropy Policy Search.
In: Journal of Machine Learning Research, 17 (93), pp. 1-50. [Article]

G

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

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

Galuske, Ralf A. W. ; Munk, Matthias H. J. ; 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. ; Seyfarth, A. (2019):
Neuromuscular Control Models of Human Locomotion.
In: Humanoid Robotics: A Reference., pp. 979-1007, Springer, ISBN 978-94-007-6045-5,
DOI: 10.1007/978-94-007-6046-2_45,
[Book Section]

Grimmer, M. ; Riener, R. ; Walsh, C. J. ; 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, 16 (2), [Article]

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

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

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

Grimmer, Martin ; Holgate, Matthew ; Holgate, Robert ; Boehler, Alexaander ; Ward, Jeffrey ; Hollander, Kevin ; Sugar, Thomas ; Seyfarth, André (2016):
A powered prosthetic ankle joint for walking and running.
In: BioMedical Engineering OnLine, 15 (3), p. 141. [Article]

H

Hur, J. ; Roth, S. (2020):
Optical flow estimation in the deep learning age.
In: Modelling Human Motion, pp. 119-140, Cham, Springer, ISBN 978-3-030-46731-9,
DOI: 10.1007/978-3-030-46732-6_7,
[Book Section]

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

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

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

Hur, J. ; 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, ISBN 978-3-319-46603-3,
[Book Section]

J

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

Jäkel, F. ; 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 ; Stelzner, Karl ; Hussing, Marcel ; Voelcker, Claas ; Kersting, Kristian (2020):
Structured Object-Aware Physics Prediction for Video Modeling and Planning.
In: Proceedings of the International Conference on Learning Reresentations (ICLR),
., [Conference or Workshop Item]

Kollegger, G. ; Ewerton, M. ; Wiemeyer, J. ; 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)",, pp. 151-163, springer international publishing, [Book Section]

Kersting, Kristian ; Mladenov, M. ; Tokmakov, P. (2017):
Relational Linear Programming.
In: Artificial Intelligence Journal (AIJ), 244, pp. 188-216. [Article]

L

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

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

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

Lutter, M. ; Ritter, C. ; Peters, J. (2019):
Deep Lagrangian Networks: Using physics as model prior for Deep Learning.
New Orleans, 7th International Conference on Learning Representations (ICLR), [Conference or Workshop Item]

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

Lioutikov, R. ; Maeda, G. ; Veiga, F. F. ; Kersting, K. ; 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),
2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, May 21-26, 2018, [Conference or Workshop Item]

M

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

Molina, A. ; Vergari, A. ; Di Mauro, F. ; Esposito, F. ; Natarajan, S. ; Kersting, K. (2018):
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains.
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI),
Palo Alto, CA, AAAI Press, The Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018, ISBN 9781577358008,
[Conference or Workshop Item]

O

Osa, T. ; Pajarinen, J. ; Neumann, G. ; Bagnell, J.A. ; Abbeel, P. ; 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),
[Article]

P

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

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

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

Plötz, T. ; Wannenwetsch, A. S. ; 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, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), [Conference or Workshop Item]

R

Rueckert, Elmar ; Kappel, David ; Tanneberg, Daniel ; Pecevski, Dejan ; Peters, Jan (2016):
Recurrent Spiking Networks Solve Planning Tasks.
In: Scientific Reports, 6 (21142), Nature Publ. Group, ISSN 2045-2322,
[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., [Conference or Workshop Item]

Richter, S. R. ; Vineet, V. ; Roth, S. ; 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, pp. 102-118, Cham, Springer, ISBN 978-3-319-46474-9,
[Book Section]

S

Stoilova, V. V. ; Knauer, B. ; Berg, S. ; Rieber, E. ; Jäkel, F. ; 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. ; Peharz, R. ; 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, 36th International Conference on Machine Learning (ICML), June 10th to June 15th, 2019, [Conference or Workshop Item]

Schramowski, Patrick ; TURAN, Cigdem ; Jentzsch, Sophie ; Rothkopf, Constantin ; Kersting, Kristian (2020):
The Moral Choice Machine.
3, In: Frontiers in Artificial Intelligence, p. 36. Frontiers, [Article]

Stock-Homburg, Ruth ; Peters, J. ; Schneider, K. ; Prasad, V. ; 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),
., [Conference or Workshop Item]

Shao, X. ; Molina, A. ; Vergari, A. ; Stelzner, K. ; Peharz, R ; Liebig, T. ; 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), [Report]

Saeedan, Faraz ; Weber, Nicolas ; Goesele, Michael ; 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 ; Botschen, Teresa ; Gurevych, Iryna ; 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), pp. 225-234,
Stroudsburg PA, USA, The Seventh Joint Conference on Lexical and Computational Semantics (*SEM), New Orleans LA, USA, 05.06.2018--06.06.2018, DOI: 10.18653/v1/S18-2027,
[Conference or Workshop Item]

Seyfarth, André ; Sharbafi, M. A. ; Zhao, G. ; 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, pp. 339-343,
Pisa, Italy, Springer, International Symposium on Wearable Robotics, Pisa, Italy, 16-20. October 2018, ISBN 978-3-030-01887-0,
[Conference or Workshop Item]

Seyfarth, André ; Schumacher, Christian (2018):
Teaching locomotion biomechanics-From concepts to applications.
In: European Journal of Physics, [Article]

Schumacher, C. ; Grimmer, M. ; Scherf, A. ; Zhao, G. ; Beckerle, P. ; 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,
[Conference or Workshop Item]

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

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

Sharbafi, Maziar A. ; Seyfarth, Andre ; Zhao, Guoping (2017):
Locomotor Sub-functions for Control of Assistive Wearable Robots.
In: Frontiers in Neurorobotics, 11, Frontiers, ISSN 1662-5218,
DOI: 10.3389/fnbot.2017.00044,
[Article]

Sharbafi, M. A. ; Seyfarth, André (2017):
How locomotion sub-functions can control walking at different speeds?
In: Journal of Biomechanics, 53, pp. 163-170. Elsevier, [Article]

Sharbafi, M. A. ; Rashty, A.M. ; Rode, C. ; 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. ; Ganguly, A. ; Koeppl, H. (2016):
A variational approach to path estimation and parameter inference of hidden diffusion processes.
In: Journal of Machine Learning Research, 17 (190), pp. 1-37. [Article]

Seyfarth, André ; Geyer, Hartmut ; Lipfert, Susanne ; Rummel, J. ; Blum, Yvonne ; Maus, Moritz ; Maykranz, D. (2016):
Whole‐Body Mechanics : How Leg Compliance Shapes the Way We Move.
In: Understanding Mammalian Locomotion: Concepts and Applications, pp. 173-191, Wiley, ISBN 9780470454640,
DOI: 10.1002/9781119113713.ch7,
[Book Section]

T

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

Tanneberg, Daniel ; Peters, Jan ; 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,
[Article]

Tangkaratt, V. ; van Hoof, H. ; Parisi, S. ; Neumann, G. ; Peters, J. ; Sugiyama, M. (2017):
Policy Search with High-Dimensional Context Variables.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), [Conference or Workshop Item]

V

Vinogradska, J. ; Bischoff, .B. ; Koller, T. ; Achterhold, J. ; Peters, J. (2020):
Numerical Quadrature for Probabilistic Policy Search.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 42 (1), [Article]

Vergari, A. ; Molina, A. ; Peharz, R. ; Ghahramani, Z. ; Kersting, K. ; Vlalera, I. (2019):
Automatic Bayesian Density Analysis.
In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI),
., [Conference or Workshop Item]

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

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

W

Wichmann, F. A. ; Jäkel, F. (2018):
Methods in Psychophysics.
In: Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience / ed. by J.T. Wixted ; E.J. Wagenmakers, pp. 265-306, [Book Section]

Wang, Quan ; Rothkopf, Constantin A ; 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, 13 (8), pp. e1005632. Public Library of Science, [Article]

Wang, Z. ; Boularias, A. ; Muelling, K. ; Schoelkopf, B. ; Peters, J. (2017):
Anticipatory Action Selection for Human-Robot Table Tennis.
In: Artificial Intelligence, 247, pp. 399-414. ISSN 0004-3702,
[Article]

Y

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

Yang, S. ; Koeppl, H. (2018):
Dependent Relational Gamma Process Models for Longitudinal Networks.
80, In: Proceedings of Machine Learning Research (PMLR), pp. 5547-5556,
Thirty-fifth International Conference on Machine Learning, Stockholm, Denmark, July 10-15, 2018, [Conference or Workshop Item]

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

Z

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

Zednik, C. ; 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,
[Article]

Š

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

Šošić, A. ; Zoubir, A. M. ; 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,
[Article]

Šošić, A. ; Rueckert, E. ; Peters, J. ; Zoubir, A. M. ; Koeppl, H. (2018):
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling.
In: Journal of Machine Learning Research, 19 (69), pp. 1-45. [Article]

Šošić, A. ; Zoubir, A. M. ; Koeppl, H. (2018):
Inverse Reinforcement Learning via Nonparametric Subgoal Modeling.
In: AAAI Spring Symposium on Data-Efficient Reinforcement Learning, [Conference or Workshop Item]

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

This list was generated on Thu Jun 24 01:48:14 2021 CEST.