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Journal Article (39)

  1. 1.
    Daniel, C.; van Hoof, H.; Peters, J.; Neumann, G.: Probabilistic Inference for Determining Options in Reinforcement Learning. Machine Learning, Special Issue (2016)
  2. 2.
    Rueckert, E.; Čamernik, J.; Peters, J.; Babič, J.: Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control. Scientific Reports 6, 28455 (2016)
  3. 3.
    Rueckert, E.; Kappel, D.; Tanneberg, D.; Pecevski, D.; Peters, J.: Recurrent Spiking Networks Solve Planning Tasks. Scientific Reports 6, 21142 (2016)
  4. 4.
    Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J.: Hierarchical Relative Entropy Policy Search. Journal of Machine Learning Research 17, 93 (2016)
  5. 5.
    Mooij, J.M.; Peters, J.; Janzing, D.; Zscheischler, J.; Schölkopf, B.: Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research 17 (1), pp. 1103 - 1204 (2016)
  6. 6.
    Schölkopf, B.; Hogg, D.; Wang, D.; Foreman-Mackey, D.; Janzing, D.; Simon-Gabriel, C.-J.; Peters, J.: Modeling Confounding by Half-Sibling Regression. Proceedings of the National Academy of Sciences of the United States of America (2016)
  7. 7.
    Mariti, C.; Muscolo, G.G.; Peters, J.; Puig, D.; Recchiuto, C.T.; Sighieri, C.; Solanas, A.; von Stryk, O.: Developing biorobotics for veterinary research into cat movements. Journal of Veterinary Behavoir (2015)
  8. 8.
    Calandra, R.; Seyfarth, A.; Peters, J.; Deisenroth, M.: Bayesian Optimization for Learning Gaits under Uncertainty. Annals of Mathematics and Artificial Intelligence 76 (1), pp. 5 - 23 (2015)
  9. 9.
    Daniel, C.; Kroemer, O.; Viering, M.; Metz, J.; Peters, J.: Active Reward Learning with a Novel Acquisition Function. Autonomous Robots (2015)
  10. 10.
    Daniel, C.; Kroemer, O.; Viering, M.; Metz, J.; Peters, J.: Active Reward Learning with a Novel Acquisition Function. Autonomous Robots (2015)
  11. 11.
    Manschitz, S.; Kober, O.; Gienger, M.; Peters, J.: Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations. Robotics and Autonomous Systems (2015)
  12. 12.
    Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Ai Poh, L.; Vadakkepat, V.; Neumann, G.: Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills. Artificial Intelligence (2014)
  13. 13.
    Wang, Z.; Boularias, A.; Mülling, K.; Schölkopf, B.; Peters, J.: Anticipatory Action Selection for Human-Robot Table Tennis. Artificial Intelligence (2014)
  14. 14.
    Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann , G.: Generalizing Movements with Information-Theoretic Stochastic Optimal Control. Journal of Aerospace Information Systems 11 (9), pp. 579 - 595 (2014)
  15. 15.
    Bocsi, B.; Csato, L.; Peters, J.: Indirect Robot Model Learning for Tracking Control. Advanced Robotics 28 (9), pp. 589 - 599 (2014)
  16. 16.
    Dann, C.; Neumann, G.; Peters, J.: Policy Evaluation with Temporal Differences: A Survey and Comparison. Journal of Machine Learning Research 15, pp. 809 - 883 (2014)
  17. 17.
    Meyer, T.; Peters, J.; Zander, T.; Schölkopf, B.; Grosse-Wentrup, M.: Predicting Motor Learning Performance from Electroencephalographic Data. Journal of NeuroEngineering and Rehabilitation (2014)
  18. 18.
    Mülling, K.; Boularias, A.; Mohler, B.; Schölkopf, B.; Peters, J.: Learning Strategies in Table Tennis using Inverse Reinforcement Learning. Biological Cybernetics 108 (5), pp. 603 - 619 (2014)
  19. 19.
    Peters, J.; Mooij, J.; Janzing, D.; Schölkopf, B.: Causal Discovery with Continuous Additive Noise Models. Journal of Machine Learning Research 15 (1), pp. 2009 - 2053 (2014)
  20. 20.
    Wierstra, D.; Schaul, T.; Glasmachers, T.; Sun, Y.; Peters, J.; Schmidhuber, J.: Natural Evolution Strategies. Journal of Machine Learning Research 15, pp. 949 - 980 (2014)
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