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2013


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Reinforcement Learning in Robotics: A Review

Kober, J., Bagnell, D., Peters, J.

International Journal of Robotics Research, 32(11):1238–1274, 2013 (article)

ei

PDF DOI [BibTex]

2013


PDF DOI [BibTex]


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Information-Theoretic Motor Skill Learning

Neumann, G., Kupcsik, A., Deisenroth, M., Peters, J.

In Proceedings of the 27th AAAI 2013, Workshop on Intelligent Robotic Systems (AAAI 2013), 2013 (inproceedings)

ei

[BibTex]

[BibTex]


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Measuring Statistical Dependence via the Mutual Information Dimension

Sugiyama, M., Borgwardt, KM.

In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), pages: 1692-1698, (Editors: Francesca Rossi), AAAI Press, Menlo Park, California, IJCAI, 2013 (inproceedings)

ei

[BibTex]

[BibTex]


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Analytical probabilistic proton dose calculation and range uncertainties

Bangert, M., Hennig, P., Oelfke, U.

In 17th International Conference on the Use of Computers in Radiation Therapy, pages: 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 (inproceedings)

ei pn

[BibTex]

[BibTex]


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Adaptivity to Local Smoothness and Dimension in Kernel Regression

Kpotufe, S., Garg, V.

In Advances in Neural Information Processing Systems 26, pages: 3075-3083, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators

Besserve, M., Logothetis, N., Schölkopf, B.

In Advances in Neural Information Processing Systems 26, pages: 2535-2543, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals

Rakitsch, B., Lippert, C., Borgwardt, KM., Stegle, O.

In Advances in Neural Information Processing Systems 26, pages: 1466-1474, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Multimodal information improves the rapid detection of mental fatigue

Laurent, F., Valderrama, M., Besserve, M., Guillard, M., Lachaux, J., Martinerie, J., Florence, G.

Biomedical Signal Processing and Control, 8(4):400 - 408, 2013 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Animating Samples from Gaussian Distributions

Hennig, P.

(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

ei pn

PDF [BibTex]

PDF [BibTex]


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Interactive Domain Adaptation for the Classification of Remote Sensing Images using Active Learning

Persello, C.

IEEE Geoscience and Remote Sensing Letters, 10(4):736-740, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Learning to Select and Generalize Striking Movements in Robot Table Tennis

Mülling, K., Kober, J., Kroemer, O., Peters, J.

International Journal of Robotics Research, 32(3):263-279, 2013 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Model-based Imitation Learning by Probabilistic Trajectory Matching

Englert, P., Paraschos, A., Peters, J., Deisenroth, M.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), pages: 1922-1927, 2013 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

ei

Web [BibTex]

Web [BibTex]


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HiFiVE: A Hilbert Space Embedding of Fiber Variability Estimates for Uncertainty Modeling and Visualization

Schultz, T., Schlaffke, L., Schölkopf, B., Schmidt-Wilcke, T.

Computer Graphics Forum, 32(3):121-130, (Editors: B Preim, P Rheingans, and H Theisel), Blackwell Publishing, Oxford, UK, Eurographics Conference on Visualization (EuroVis), 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Towards neurofeedback for improving visual attention

Zander, T., Battes, B., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, pages: Article ID: 086, (Editors: J.d.R. Millán, S. Gao, R. Müller-Putz, J.R. Wolpaw, and J.E. Huggins), Verlag der Technischen Universität Graz, 5th International Brain-Computer Interface Meeting, 2013, Article ID: 086 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Detection and attribution of large spatiotemporal extreme events in Earth observation data

Zscheischler, J., Mahecha, M., Harmeling, S., Reichstein, M.

Ecological Informatics, 15, pages: 66-73, 2013 (article)

Abstract
Latest climate projections suggest that both frequency and intensity of climate extremes will be substantially modified over the course of the coming decades. As a consequence, we need to understand to what extent and via which pathways climate extremes affect the state and functionality of terrestrial ecosystems and the associated biogeochemical cycles on a global scale. So far the impacts of climate extremes on the terrestrial biosphere were mainly investigated on the basis of case studies, while global assessments are widely lacking. In order to facilitate global analysis of this kind, we present a methodological framework that firstly detects spatiotemporally contiguous extremes in Earth observations, and secondly infers the likely pathway of the preceding climate anomaly. The approach does not require long time series, is computationally fast, and easily applicable to a variety of data sets with different spatial and temporal resolutions. The key element of our analysis strategy is to directly search in the relevant observations for spatiotemporally connected components exceeding a certain percentile threshold. We also put an emphasis on characterization of extreme event distribution, and scrutinize the attribution issue. We exemplify the analysis strategy by exploring the fraction of absorbed photosynthetically active radiation (fAPAR) from 1982 to 2011. Our results suggest that the hot spots of extremes in fAPAR lie in Northeastern Brazil, Southeastern Australia, Kenya and Tanzania. Moreover, we demonstrate that the size distribution of extremes follow a distinct power law. The attribution framework reveals that extremes in fAPAR are primarily driven by phases of water scarcity.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Simultaneous PET/MR reveals Brain Function in Activated and Resting State on Metabolic, Hemodynamic and Multiple Temporal Scales

Wehrl, H., Hossain, M., Lankes, K., Liu, C., Bezrukov, I., Martirosian, P., Schick, F., Reischl, G., Pichler, B.

Nature Medicine, 19, pages: 1184–1189, 2013 (article)

Abstract
Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) is a new tool to study functional processes in the brain. Here we study brain function in response to a barrel-field stimulus simultaneously using PET, which traces changes in glucose metabolism on a slow time scale, and functional MRI (fMRI), which assesses fast vascular and oxygenation changes during activation. We found spatial and quantitative discrepancies between the PET and the fMRI activation data. The functional connectivity of the rat brain was assessed by both modalities: the fMRI approach determined a total of nine known neural networks, whereas the PET method identified seven glucose metabolism–related networks. These results demonstrate the feasibility of combined PET-MRI for the simultaneous study of the brain at activation and rest, revealing comprehensive and complementary information to further decode brain function and brain networks.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions

Jung, M., Zscheischler, J.

In Proceedings of the International Conference on Computational Science, 18, pages: 2337 - 2346, Procedia Computer Science, (Editors: Alexandrov, V and Lees, M and Krzhizhanovskaya, V and Dongarra, J and Sloot, PMA), Elsevier, Amsterdam, Netherlands, ICCS, 2013 (inproceedings)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Finding Potential Support Vectors in Separable Classification Problems

Varagnolo, D., Del Favero, S., Dinuzzo, F., Schenato, L., Pillonetto, G.

IEEE Transactions on Neural Networks and Learning Systems, 24(11):1799-1813, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Learning responsive robot behavior by imitation

Ben Amor, H., Vogt, D., Ewerton, M., Berger, E., Jung, B., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pages: 3257-3264, IEEE, 2013 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Learning Skills with Motor Primitives

Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G.

In Proceedings of the 16th Yale Workshop on Adaptive and Learning Systems, 2013 (inproceedings)

ei

[BibTex]

[BibTex]


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Scalable Influence Estimation in Continuous-Time Diffusion Networks

Du, N., Song, L., Gomez Rodriguez, M., Zha, H.

In Advances in Neural Information Processing Systems 26, pages: 3147-3155, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Rapid Distance-Based Outlier Detection via Sampling

Sugiyama, M., Borgwardt, KM.

In Advances in Neural Information Processing Systems 26, pages: 467-475, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Probabilistic Movement Primitives

Paraschos, A., Daniel, C., Peters, J., Neumann, G.

In Advances in Neural Information Processing Systems 26, pages: 2616-2624, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Causal Inference on Time Series using Restricted Structural Equation Models

Peters, J., Janzing, D., Schölkopf, B.

In Advances in Neural Information Processing Systems 26, pages: 154-162, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Regression-tree Tuning in a Streaming Setting

Kpotufe, S., Orabona, F.

In Advances in Neural Information Processing Systems 26, pages: 1788-1796, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Density estimation from unweighted k-nearest neighbor graphs: a roadmap

von Luxburg, U., Alamgir, M.

In Advances in Neural Information Processing Systems 26, pages: 225-233, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Open-Box Spectral Clustering: Applications to Medical Image Analysis

Schultz, T., Kindlmann, G.

IEEE Transactions on Visualization and Computer Graphics, 19(12):2100-2108, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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im3shape: a maximum likelihood galaxy shear measurement code for cosmic gravitational lensing

Zuntz, J., Kacprzak, T., Voigt, L., Hirsch, M., Rowe, B., Bridle, S.

Monthly Notices of the Royal Astronomical Society, 434(2):1604-1618, Oxford University Press, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

ei

[BibTex]

[BibTex]


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Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era

Hogg, D. W., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Lang, D., Montet, B. T., Schiminovich, D., Schölkopf, B.

arXiv:1309.0653, 2013 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]


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Accurate detection of differential RNA processing

Drewe, P., Stegle, O., Hartmann, L., Kahles, A., Bohnert, R., Wachter, A., Borgwardt, K. M., Rätsch, G.

Nucleic Acids Research, 41(10):5189-5198, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars

Montet, B. T., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Hogg, D. W., Lang, D., Schiminovich, D., Schölkopf, B.

arXiv:1309.0654, 2013 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]


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Detecting regulatory gene–environment interactions with unmeasured environmental factors

Fusi, N., Lippert, C., Borgwardt, K. M., Lawrence, N. D., Stegle, O.

Bioinformatics, 29(11):1382-1389, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension

Seldin, Y., Schölkopf, B.

In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

ei

[BibTex]

[BibTex]


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Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis

Mülling, K.

Technical University Darmstadt, Germany, 2013 (phdthesis)

ei

[BibTex]

[BibTex]


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Fragmentation of Slow Wave Sleep after Onset of Complete Locked-In State

Soekadar, S. R., Born, J., Birbaumer, N., Bensch, M., Halder, S., Murguialday, A. R., Gharabaghi, A., Nijboer, F., Schölkopf, B., Martens, S.

Journal of Clinical Sleep Medicine, 9(9):951-953, 2013 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Automatic Malaria Diagnosis system

Mehrjou, A., Abbasian, T., Izadi, M.

In First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pages: 205-211, 2013 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Wang, Z.

Technical University Darmstadt, Germany, 2013 (phdthesis)

ei

[BibTex]


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Structural learning

Braun, D

Scholarpedia, 8(10):12312, October 2013 (article)

Abstract
Structural learning in motor control refers to a metalearning process whereby an agent extracts (abstract) invariants from its sensorimotor stream when experiencing a range of environments that share similar structure. Such invariants can then be exploited for faster generalization and learning-to-learn when experiencing novel, but related task environments.

ei

DOI [BibTex]

DOI [BibTex]


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The effect of model uncertainty on cooperation in sensorimotor interactions

Grau-Moya, J, Hez, E, Pezzulo, G, Braun, DA

Journal of the Royal Society Interface, 10(87):1-11, October 2013 (article)

Abstract
Decision-makers have been shown to rely on probabilistic models for perception and action. However, these models can be incorrect or partially wrong in which case the decision-maker has to cope with model uncertainty. Model uncertainty has recently also been shown to be an important determinant of sensorimotor behaviour in humans that can lead to risk-sensitive deviations from Bayes optimal behaviour towards worst-case or best-case outcomes. Here, we investigate the effect of model uncertainty on cooperation in sensorimotor interactions similar to the stag-hunt game, where players develop models about the other player and decide between a pay-off-dominant cooperative solution and a risk-dominant, non-cooperative solution. In simulations, we show that players who allow for optimistic deviations from their opponent model are much more likely to converge to cooperative outcomes. We also implemented this agent model in a virtual reality environment, and let human subjects play against a virtual player. In this game, subjects' pay-offs were experienced as forces opposing their movements. During the experiment, we manipulated the risk sensitivity of the computer player and observed human responses. We found not only that humans adaptively changed their level of cooperation depending on the risk sensitivity of the computer player but also that their initial play exhibited characteristic risk-sensitive biases. Our results suggest that model uncertainty is an important determinant of cooperation in two-player sensorimotor interactions.

ei

DOI [BibTex]

DOI [BibTex]


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Thermodynamics as a theory of decision-making with information-processing costs

Ortega, PA, Braun, DA

Proceedings of the Royal Society of London A, 469(2153):1-18, May 2013 (article)

Abstract
Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here, we propose a thermodynamically inspired formalization of bounded rational decision-making where information processing is modelled as state changes in thermodynamic systems that can be quantified by differences in free energy. By optimizing a free energy, bounded rational decision-makers trade off expected utility gains and information-processing costs measured by the relative entropy. As a result, the bounded rational decision-making problem can be rephrased in terms of well-known variational principles from statistical physics. In the limit when computational costs are ignored, the maximum expected utility principle is recovered. We discuss links to existing decision-making frameworks and applications to human decision-making experiments that are at odds with expected utility theory. Since most of the mathematical machinery can be borrowed from statistical physics, the main contribution is to re-interpret the formalism of thermodynamic free-energy differences in terms of bounded rational decision-making and to discuss its relationship to human decision-making experiments.

ei

DOI [BibTex]

DOI [BibTex]


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Abstraction in Decision-Makers with Limited Information Processing Capabilities

Genewein, T, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artificial systems abstractions are often implemented through computationally costly formations of groups or clusters. In this work we establish the relation between the free-energy framework for decision-making and rate-distortion theory and demonstrate how the application of rate-distortion for decision-making leads to the emergence of abstractions. We argue that abstractions are induced due to a limit in information processing capacity.

ei

link (url) [BibTex]

link (url) [BibTex]


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Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.

ei

link (url) [BibTex]

link (url) [BibTex]

2010


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Computationally efficient algorithms for statistical image processing: Implementation in R

Langovoy, M., Wittich, O.

(2010-053), EURANDOM, Technische Universiteit Eindhoven, December 2010 (techreport)

Abstract
In the series of our earlier papers on the subject, we proposed a novel statistical hy- pothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of un- known distribution. No boundary shape constraints were imposed on the objects, only a weak bulk condition for the object's interior was required. Our algorithms have linear complexity and exponential accuracy. In the present paper, we describe an implementation of our nonparametric hypothesis testing method. We provide a program that can be used for statistical experiments in image processing. This program is written in the statistical programming language R.

ei

PDF [BibTex]

2010


PDF [BibTex]


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Learning Table Tennis with a Mixture of Motor Primitives

Mülling, K., Kober, J., Peters, J.

In Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010), pages: 411-416, IEEE, Piscataway, NJ, USA, 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), December 2010 (inproceedings)

Abstract
Table tennis is a sufficiently complex motor task for studying complete skill learning systems. It consists of several elementary motions and requires fast movements, accurate control, and online adaptation. To represent the elementary movements needed for robot table tennis, we rely on dynamic systems motor primitives (DMP). While such DMPs have been successfully used for learning a variety of simple motor tasks, they only represent single elementary actions. In order to select and generalize among different striking movements, we present a new approach, called Mixture of Motor Primitives that uses a gating network to activate appropriate motor primitives. The resulting policy enables us to select among the appropriate motor primitives as well as to generalize between them. In order to obtain a fully learned robot table tennis setup, we also address the problem of predicting the necessary context information, i.e., the hitting point in time and space where we want to hit the ball. We show that the resulting setup was capable of playing rudimentary table tennis using an anthropomorphic robot arm.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Similarities in resting state and feature-driven activity: Non-parametric evaluation of human fMRI

Shelton, J., Blaschko, M., Gretton, A., Müller, J., Fischer, E., Bartels, A.

NIPS Workshop on Learning and Planning from Batch Time Series Data, December 2010 (poster)

ei

PDF Web [BibTex]

PDF Web [BibTex]