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2013


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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)

[BibTex]

2013

[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)

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)

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)

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)

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)

PDF [BibTex]

PDF [BibTex]


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Comparative Classifier Evaluation for Web-Scale Taxonomies Using Power Law

Babbar, R., Partalas, I., Metzig, C., Gaussier, E., Amini, M.

In The Semantic Web: ESWC 2013 Satellite Events, Lecture Notes in Computer Science, Vol. 7955 , pages: 310-311, (Editors: P Cimiano and M Fernández and V Lopez and S Schlobach and J Völker), Springer, ESWC, 2013 (inproceedings)

Web [BibTex]

Web [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)

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)

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)

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)

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)

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)

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.

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.

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)

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)

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)

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)

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)

[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)

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)

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)

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)

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)

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)

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)

DOI [BibTex]

DOI [BibTex]


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PAC-Bayes-Empirical-Bernstein Inequality

Tolstikhin, I. O., Seldin, Y.

In Advances in Neural Information Processing Systems 26, pages: 109-117, (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)

link (url) [BibTex]

link (url) [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)

DOI [BibTex]

DOI [BibTex]


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Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

[BibTex]

[BibTex]


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PLAL: Cluster-based active learning

Urner, R., Wulff, S., Ben-David, S.

In Proceedings of the 26th Annual Conference on Learning Theory, 30, pages: 376-397, (Editors: Shalev-Shwartz, S. and Steinwart, I.), JMLR, COLT, 2013 (inproceedings)

link (url) [BibTex]

link (url) [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)

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)

DOI [BibTex]

DOI [BibTex]


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Monochromatic Bi-Clustering

Wulff, S., Urner, R., Ben-David, S.

In Proceedings of the 30th International Conference on Machine Learning, 28, pages: 145-153, (Editors: Dasgupta, S. and McAllester, D.), JMLR, ICML, 2013 (inproceedings)

link (url) [BibTex]

link (url) [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)

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)

DOI [BibTex]

DOI [BibTex]


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Significance of variable height-bandwidth group delay filters in the spectral reconstruction of speech

Devanshu, A., Raj, A., Hegde, R. M.

INTERSPEECH 2013, 14th Annual Conference of the International Speech Communication Association, pages: 1682-1686, 2013 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Generative Multiple-Instance Learning Models For Quantitative Electromyography

Adel, T., Smith, B., Urner, R., Stashuk, D., Lizotte, D. J.

In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, AUAI Press, UAI, 2013 (inproceedings)

link (url) [BibTex]

link (url) [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)

[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)

[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)

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)

DOI [BibTex]

DOI [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.

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.

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.

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.

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.

link (url) [BibTex]

link (url) [BibTex]