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187 results (View BibTeX file of all listed publications)

2015

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Quantifying changes in climate variability and extremes: Pitfalls and their overcoming

Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J., Mahecha, M. D.

Geophysical Research Letters, 42(22):9990-9998, November 2015 (article)

DOI [BibTex]

2015

Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J., Mahecha, M. D. Quantifying changes in climate variability and extremes: Pitfalls and their overcoming Geophysical Research Letters, 42(22):9990-9998, November 2015 (article)

DOI [BibTex]

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Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events

Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.

Proceedings of the National Academy of Sciences U.S.A, 112(46):E6379-E6387, November 2015 (article)

DOI [BibTex]

Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M. Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events Proceedings of the National Academy of Sciences U.S.A, 112(46):E6379-E6387, November 2015 (article)

DOI [BibTex]

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Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M.

Journal of Vision, 15(14):1-17, October 2015 (article)

DOI [BibTex]

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M. Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception Journal of Vision, 15(14):1-17, October 2015 (article)

DOI [BibTex]

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Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S.

PLOS Biology, 13(9):e1002257, September 2015 (article)

DOI [BibTex]

Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S. Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer PLOS Biology, 13(9):e1002257, September 2015 (article)

DOI [BibTex]

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Semi-Supervised Interpolation in an Anticausal Learning Scenario

Janzing, D., Schölkopf, B.

Journal of Machine Learning Research, 16, pages: 1923-1948, September 2015 (article)

link (url) [BibTex]

Janzing, D., Schölkopf, B. Semi-Supervised Interpolation in an Anticausal Learning Scenario Journal of Machine Learning Research, 16, pages: 1923-1948, September 2015 (article)

link (url) [BibTex]

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Testing the role of luminance edges in White’s illusion with contour adaptation

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M.

Journal of Vision, 15(11):1-16, August 2015 (article)

DOI [BibTex]

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M. Testing the role of luminance edges in White’s illusion with contour adaptation Journal of Vision, 15(11):1-16, August 2015 (article)

DOI [BibTex]

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Blind multirigid retrospective motion correction of MR images

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine, 73(4):1457-1468, April 2015 (article)

DOI [BibTex]

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B. Blind multirigid retrospective motion correction of MR images Magnetic Resonance in Medicine, 73(4):1457-1468, April 2015 (article)

DOI [BibTex]

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A quantum advantage for inferring causal structure

Ried, K., Agnew, M., Vermeyden, L., Janzing, D., Spekkens, R. W., Resch, K. J.

Nature Physics, 11(5):414-420, March 2015 (article)

Abstract
The problem of inferring causal relations from observed correlations is relevant to a wide variety of scientific disciplines. Yet given the correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both. Only a randomized trial can settle the issue. Here we consider the problem of causal inference for quantum variables. We show that the analogue of a randomized trial, causal tomography, yields a complete solution. We also show that, in contrast to the classical case, one can sometimes infer the causal structure from observations alone. We implement a quantum-optical experiment wherein we control the causal relation between two optical modes, and two measurement schemes—with and without randomization—that extract this relation from the observed correlations. Our results show that entanglement and quantum coherence provide an advantage for causal inference.

DOI [BibTex]

Ried, K., Agnew, M., Vermeyden, L., Janzing, D., Spekkens, R. W., Resch, K. J. A quantum advantage for inferring causal structure Nature Physics, 11(5):414-420, March 2015 (article)

DOI [BibTex]

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Positive definite matrices and the S-divergence

Sra, S.

Proceedings of the American Mathematical Society, 2015, Published electronically: October 22, 2015 (article)

DOI [BibTex]

Sra, S. Positive definite matrices and the S-divergence Proceedings of the American Mathematical Society, 2015, Published electronically: October 22, 2015 (article)

DOI [BibTex]

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Structural Intervention Distance (SID) for Evaluating Causal Graphs

Peters, J., Bühlmann, P.

Neural Computation , 27(3):771-799, 2015 (article)

DOI [BibTex]

Peters, J., Bühlmann, P. Structural Intervention Distance (SID) for Evaluating Causal Graphs Neural Computation , 27(3):771-799, 2015 (article)

DOI [BibTex]

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Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence

Zhang, J., Zhang, K.

Philosophy of Science, Supplementary Volume 2015, 82(5):930-940, 2015 (article)

DOI [BibTex]

Zhang, J., Zhang, K. Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence Philosophy of Science, Supplementary Volume 2015, 82(5):930-940, 2015 (article)

DOI [BibTex]

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Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Küffner, R., Zach, N., Norel, R., Hawe, J., Schoenfeld, D., Wang, L., Li, G., Fang, L., Mackey, L., Hardiman, O., Cudkowicz, M., Sherman, A., Ertaylan, G., Grosse-Wentrup, M., Hothorn, T., van Ligtenberg, J., Macke, J., Meyer, T., Schölkopf, B., Tran, L., Vaughan, R., Stolovitzky, G., Leitner, M.

Nature Biotechnology, 33, pages: 51-57, 2015 (article)

DOI [BibTex]

Küffner, R., Zach, N., Norel, R., Hawe, J., Schoenfeld, D., Wang, L., Li, G., Fang, L., Mackey, L., Hardiman, O., Cudkowicz, M., Sherman, A., Ertaylan, G., Grosse-Wentrup, M., Hothorn, T., van Ligtenberg, J., Macke, J., Meyer, T., Schölkopf, B., Tran, L., Vaughan, R., Stolovitzky, G., Leitner, M. Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression Nature Biotechnology, 33, pages: 51-57, 2015 (article)

DOI [BibTex]

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Probabilistic Interpretation of Linear Solvers

Hennig, P.

SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

Web PDF link (url) DOI [BibTex]

Hennig, P. Probabilistic Interpretation of Linear Solvers SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

Web PDF link (url) DOI [BibTex]

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Developing biorobotics for veterinary research into cat movements

Mariti, C., Muscolo, G., Peters, J., Puig, D., Recchiuto, C., Sighieri, C., Solanas, A., von Stryk, O.

Journal of Veterinary Behavior: Clinical Applications and Research, 10(3):248-254, 2015 (article)

DOI [BibTex]

Mariti, C., Muscolo, G., Peters, J., Puig, D., Recchiuto, C., Sighieri, C., Solanas, A., von Stryk, O. Developing biorobotics for veterinary research into cat movements Journal of Veterinary Behavior: Clinical Applications and Research, 10(3):248-254, 2015 (article)

DOI [BibTex]

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Spatial statistics and attentional dynamics in scene viewing

Engbert, R., Trukenbrod, H., Barthelmé, S., Wichmann, F.

Journal of Vision, 15(1):1-17, 2015 (article)

Web PDF link (url) DOI [BibTex]

Engbert, R., Trukenbrod, H., Barthelmé, S., Wichmann, F. Spatial statistics and attentional dynamics in scene viewing Journal of Vision, 15(1):1-17, 2015 (article)

Web PDF link (url) DOI [BibTex]

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The Randomized Causation Coefficient

Lopez-Paz, D., Muandet, K., Recht, B.

Journal of Machine Learning, 16, pages: 2901-2907, 2015 (article)

link (url) [BibTex]

Lopez-Paz, D., Muandet, K., Recht, B. The Randomized Causation Coefficient Journal of Machine Learning, 16, pages: 2901-2907, 2015 (article)

link (url) [BibTex]

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Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter

Kopp, M., Harmeling, S., Schütz, G., Schölkopf, B., Fähnle, M.

Ultramicroscopy, 148, pages: 115-122, 2015 (article)

Abstract
The Kalman filter is a well-established approach to get information on the time-dependent state of a system from noisy observations. It was developed in the context of the Apollo project to see the deviation of the true trajectory of a rocket from the desired trajectory. Afterwards it was applied to many different systems with small numbers of components of the respective state vector (typically about 10). In all cases the equation of motion for the state vector was known exactly. The fast dissipative magnetization dynamics is often investigated by x-ray magnetic circular dichroism movies (XMCD movies), which are often very noisy. In this situation the number of components of the state vector is extremely large (about 105), and the equation of motion for the dissipative magnetization dynamics (especially the values of the material parameters of this equation) is not well known. In the present paper it is shown by theoretical considerations that – nevertheless – there is no principle problem for the use of the Kalman filter to denoise XMCD movies of fast dissipative magnetization dynamics.

Web DOI [BibTex]

Kopp, M., Harmeling, S., Schütz, G., Schölkopf, B., Fähnle, M. Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter Ultramicroscopy, 148, pages: 115-122, 2015 (article)

Web DOI [BibTex]

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Artificial intelligence: Learning to see and act

Schölkopf, B.

Nature, News & Views, 518(7540):486-487, 2015 (article)

DOI [BibTex]

Schölkopf, B. Artificial intelligence: Learning to see and act Nature, News & Views, 518(7540):486-487, 2015 (article)

DOI [BibTex]

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Context affects lightness at the level of surfaces

Maertens, M., Wichmann, F., Shapley, R.

Journal of Vision, 15(1):1-15, 2015 (article)

Web PDF link (url) DOI [BibTex]

Maertens, M., Wichmann, F., Shapley, R. Context affects lightness at the level of surfaces Journal of Vision, 15(1):1-15, 2015 (article)

Web PDF link (url) DOI [BibTex]

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Genome-wide analysis of local chromatin packing in Arabidopsis thaliana

Wang, C., Liu, C., Roqueiro, D., Grimm, D., Schwab, R., Becker, C., Lanz, C., Weigel, D.

Genome Research, 25(2):246-256, 2015 (article)

PDF DOI [BibTex]

Wang, C., Liu, C., Roqueiro, D., Grimm, D., Schwab, R., Becker, C., Lanz, C., Weigel, D. Genome-wide analysis of local chromatin packing in Arabidopsis thaliana Genome Research, 25(2):246-256, 2015 (article)

PDF DOI [BibTex]

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Segmentation-based attenuation correction in positron emission tomography/magnetic resonance: erroneous tissue identification and its impact on positron emission tomography interpretation

Brendle, C., Schmidt, H., Oergel, A., Bezrukov, I., Mueller, M., Schraml, C., Pfannenberg, C., la Fougère, C., Nikolaou, K., Schwenzer, N.

Investigative Radiology, 50(5):339-346, 2015 (article)

DOI [BibTex]

Brendle, C., Schmidt, H., Oergel, A., Bezrukov, I., Mueller, M., Schraml, C., Pfannenberg, C., la Fougère, C., Nikolaou, K., Schwenzer, N. Segmentation-based attenuation correction in positron emission tomography/magnetic resonance: erroneous tissue identification and its impact on positron emission tomography interpretation Investigative Radiology, 50(5):339-346, 2015 (article)

DOI [BibTex]

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Active Reward Learning with a Novel Acquisition Function

Daniel, C., Kroemer, O., Viering, M., Metz, J., Peters, J.

Autonomous Robots, 39(3):389-405, 2015 (article)

link (url) DOI [BibTex]

Daniel, C., Kroemer, O., Viering, M., Metz, J., Peters, J. Active Reward Learning with a Novel Acquisition Function Autonomous Robots, 39(3):389-405, 2015 (article)

link (url) DOI [BibTex]

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A systematic search for transiting planets in the K2 data

Foreman-Mackey, D., Montet, B., Hogg, D., Morton, T., Wang, D., Schölkopf, B.

The Astrophysical Journal, 806(2), 2015 (article)

Abstract
Photometry of stars from the K2 extension of NASA’s Kepler mission is afflicted by systematic effects caused by small (few-pixel) drifts in the telescope pointing and other spacecraft issues. We present a method for searching K2 light curves for evidence of exoplanets by simultaneously fitting for these systematics and the transit signals of interest. This method is more computationally expensive than standard search algorithms but we demonstrate that it can be efficiently implemented and used to discover transit signals. We apply this method to the full Campaign 1 data set and report a list of 36 planet candidates transiting 31 stars, along with an analysis of the pipeline performance and detection efficiency based on artificial signal injections and recoveries. For all planet candidates, we present posterior distributions on the properties of each system based strictly on the transit observables.

link (url) DOI [BibTex]

Foreman-Mackey, D., Montet, B., Hogg, D., Morton, T., Wang, D., Schölkopf, B. A systematic search for transiting planets in the K2 data The Astrophysical Journal, 806(2), 2015 (article)

link (url) DOI [BibTex]

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Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations

Manschitz, S., Kober, J., Gienger, M., Peters, J.

Robotics and Autonomous Systems, 74, Part A, pages: 97-107, 2015 (article)

link (url) DOI [BibTex]

Manschitz, S., Kober, J., Gienger, M., Peters, J. Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations Robotics and Autonomous Systems, 74, Part A, pages: 97-107, 2015 (article)

link (url) DOI [BibTex]

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Bayesian Optimization for Learning Gaits under Uncertainty

Calandra, R., Seyfarth, A., Peters, J., Deisenroth, M.

Annals of Mathematics and Artificial Intelligence, pages: 1-19, 2015 (article)

DOI [BibTex]

Calandra, R., Seyfarth, A., Peters, J., Deisenroth, M. Bayesian Optimization for Learning Gaits under Uncertainty Annals of Mathematics and Artificial Intelligence, pages: 1-19, 2015 (article)

DOI [BibTex]

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Assessment of murine brain tissue shrinkage caused by different histological fixatives using magnetic resonance and computed tomography imaging

Wehrl, H. F., Bezrukov, I., Wiehr, S., Lehnhoff, M., Fuchs, K., Mannheim, J. G., Quintanilla-Martinez, L., Kneilling, M., Pichler, B. J., Sauter, A. W.

Histology and Histopathology, 30(5):601-613, 2015 (article)

[BibTex]

Wehrl, H. F., Bezrukov, I., Wiehr, S., Lehnhoff, M., Fuchs, K., Mannheim, J. G., Quintanilla-Martinez, L., Kneilling, M., Pichler, B. J., Sauter, A. W. Assessment of murine brain tissue shrinkage caused by different histological fixatives using magnetic resonance and computed tomography imaging Histology and Histopathology, 30(5):601-613, 2015 (article)

[BibTex]

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Cosmology from Cosmic Shear with DES Science Verification Data

Abbott, T., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A. H., Baxter, E., others,

arXiv preprint arXiv:1507.05552, 2015 (techreport)

link (url) [BibTex]

Abbott, T., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A. H., Baxter, E., others, Cosmology from Cosmic Shear with DES Science Verification Data arXiv preprint arXiv:1507.05552, 2015 (techreport)

link (url) [BibTex]

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Improved Bayesian Information Criterion for Mixture Model Selection

Mehrjou, A., Hosseini, R., Araabi, B.

Pattern Recognition Letters, 69, pages: 22-27, 2015 (article)

DOI [BibTex]

Mehrjou, A., Hosseini, R., Araabi, B. Improved Bayesian Information Criterion for Mixture Model Selection Pattern Recognition Letters, 69, pages: 22-27, 2015 (article)

DOI [BibTex]

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Correlation matrix nearness and completion under observation uncertainty

Alaíz, C. M., Dinuzzo, F., Sra, S.

IMA Journal of Numerical Analysis, 35(1):325-340, 2015 (article)

DOI [BibTex]

Alaíz, C. M., Dinuzzo, F., Sra, S. Correlation matrix nearness and completion under observation uncertainty IMA Journal of Numerical Analysis, 35(1):325-340, 2015 (article)

DOI [BibTex]

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Quantitative evaluation of segmentation- and atlas- based attenuation correction for PET/MR on pediatric patients

Bezrukov, I., Schmidt, H., Gatidis, S., Mantlik, F., Schäfer, J. F., Schwenzer, N., Pichler, B. J.

Journal of Nuclear Medicine, 56(7):1067-1074, 2015 (article)

DOI [BibTex]

Bezrukov, I., Schmidt, H., Gatidis, S., Mantlik, F., Schäfer, J. F., Schwenzer, N., Pichler, B. J. Quantitative evaluation of segmentation- and atlas- based attenuation correction for PET/MR on pediatric patients Journal of Nuclear Medicine, 56(7):1067-1074, 2015 (article)

DOI [BibTex]

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The DES Science Verification Weak Lensing Shear Catalogs

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others,

arXiv preprint arXiv:1507.05603, 2015 (techreport)

link (url) [BibTex]

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others, The DES Science Verification Weak Lensing Shear Catalogs arXiv preprint arXiv:1507.05603, 2015 (techreport)

link (url) [BibTex]

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Probabilistic numerics and uncertainty in computations

Hennig, P., Osborne, M. A., Girolami, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

Abstract
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

PDF DOI [BibTex]

Hennig, P., Osborne, M. A., Girolami, M. Probabilistic numerics and uncertainty in computations Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

PDF DOI [BibTex]

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Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

Melchior, P., Suchyta, E., Huff, E., Hirsch, M., Kacprzak, T., Rykoff, E., Gruen, D., Armstrong, R., Bacon, D., Bechtol, K., others,

Monthly Notices of the Royal Astronomical Society, 449(3):2219-2238, Oxford University Press, 2015 (article)

DOI [BibTex]

Melchior, P., Suchyta, E., Huff, E., Hirsch, M., Kacprzak, T., Rykoff, E., Gruen, D., Armstrong, R., Bacon, D., Bechtol, K., others, Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data Monthly Notices of the Royal Astronomical Society, 449(3):2219-2238, Oxford University Press, 2015 (article)

DOI [BibTex]

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Entropic Movement Complexity Reflects Subjective Creativity Rankings of Visualized Hand Motion Trajectories

Peng, Z, Braun, DA

Frontiers in Psychology, 6(1879):1-13, December 2015 (article)

Abstract
In a previous study we have shown that human motion trajectories can be characterized by translating continuous trajectories into symbol sequences with well-defined complexity measures. Here we test the hypothesis that the motion complexity individuals generate in their movements might be correlated to the degree of creativity assigned by a human observer to the visualized motion trajectories. We asked participants to generate 55 novel hand movement patterns in virtual reality, where each pattern had to be repeated 10 times in a row to ensure reproducibility. This allowed us to estimate a probability distribution over trajectories for each pattern. We assessed motion complexity not only by the previously proposed complexity measures on symbolic sequences, but we also propose two novel complexity measures that can be directly applied to the distributions over trajectories based on the frameworks of Gaussian Processes and Probabilistic Movement Primitives. In contrast to previous studies, these new methods allow computing complexities of individual motion patterns from very few sample trajectories. We compared the different complexity measures to how a group of independent jurors rank ordered the recorded motion trajectories according to their personal creativity judgment. We found three entropic complexity measures that correlate significantly with human creativity judgment and discuss differences between the measures. We also test whether these complexity measures correlate with individual creativity in divergent thinking tasks, but do not find any consistent correlation. Our results suggest that entropic complexity measures of hand motion may reveal domain-specific individual differences in kinesthetic creativity.

DOI [BibTex]

Peng, Z, Braun, DA Entropic Movement Complexity Reflects Subjective Creativity Rankings of Visualized Hand Motion Trajectories Frontiers in Psychology, 6(1879):1-13, December 2015 (article)

DOI [BibTex]

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Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle

Genewein, T, Leibfried, F, Grau-Moya, J, Braun, DA

Frontiers in Robotics and AI, 2(27):1-24, October 2015 (article)

Abstract
Abstraction and hierarchical information-processing are hallmarks of human and animal intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving such a flexibility in artificial systems is challenging, even with more and more computational power. Here we investigate the hypothesis that abstraction and hierarchical information-processing might in fact be the consequence of limitations in information-processing power. In particular, we study an information-theoretic framework of bounded rational decision-making that trades off utility maximization against information-processing costs. We apply the basic principle of this framework to perception-action systems with multiple information-processing nodes and derive bounded optimal solutions. We show how the formation of abstractions and decision-making hierarchies depends on information-processing costs. We illustrate the theoretical ideas with example simulations and conclude by formalizing a mathematically unifying optimization principle that could potentially be extended to more complex systems.

DOI [BibTex]

Genewein, T, Leibfried, F, Grau-Moya, J, Braun, DA Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle Frontiers in Robotics and AI, 2(27):1-24, October 2015 (article)

DOI [BibTex]

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Signaling equilibria in sensorimotor interactions

Leibfried, F, Grau-Moya, J, Braun, DA

Cognition, 141, pages: 73-86, August 2015 (article)

Abstract
Although complex forms of communication like human language are often assumed to have evolved out of more simple forms of sensorimotor signaling, less attention has been devoted to investigate the latter. Here, we study communicative sensorimotor behavior of humans in a two-person joint motor task where each player controls one dimension of a planar motion. We designed this joint task as a game where one player (the sender) possesses private information about a hidden target the other player (the receiver) wants to know about, and where the sender's actions are costly signals that influence the receiver's control strategy. We developed a game-theoretic model within the framework of signaling games to investigate whether subjects' behavior could be adequately described by the corresponding equilibrium solutions. The model predicts both separating and pooling equilibria, in which signaling does and does not occur respectively. We observed both kinds of equilibria in subjects and found that, in line with model predictions, the propensity of signaling decreased with increasing signaling costs and decreasing uncertainty on the part of the receiver. Our study demonstrates that signaling games, which have previously been applied to economic decision-making and animal communication, provide a framework for human signaling behavior arising during sensorimotor interactions in continuous and dynamic environments.

DOI [BibTex]

Leibfried, F, Grau-Moya, J, Braun, DA Signaling equilibria in sensorimotor interactions Cognition, 141, pages: 73-86, August 2015 (article)

DOI [BibTex]

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Structure Learning in Bayesian Sensorimotor Integration

Genewein, T, Hez, E, Razzaghpanah, Z, Braun, DA

PLoS Computational Biology, 11(8):1-27, August 2015 (article)

Abstract
Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.

DOI [BibTex]

Genewein, T, Hez, E, Razzaghpanah, Z, Braun, DA Structure Learning in Bayesian Sensorimotor Integration PLoS Computational Biology, 11(8):1-27, August 2015 (article)

DOI [BibTex]

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A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker

Leibfried, F, Braun, DA

Neural Computation, 27(8):1686-1720, July 2015 (article)

Abstract
Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

DOI [BibTex]

Leibfried, F, Braun, DA A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker Neural Computation, 27(8):1686-1720, July 2015 (article)

DOI [BibTex]

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What is epistemic value in free energy models of learning and acting? A bounded rationality perspective

Ortega, PA, Braun, DA

Cognitive Neuroscience, 6(4):215-216, December 2015 (article)

Abstract
Free energy models of learning and acting do not only care about utility or extrinsic value, but also about intrinsic value, that is, the information value stemming from probability distributions that represent beliefs or strategies. While these intrinsic values can be interpreted as epistemic values or exploration bonuses under certain conditions, the framework of bounded rationality offers a complementary interpretation in terms of information-processing costs that we discuss here.

DOI [BibTex]

Ortega, PA, Braun, DA What is epistemic value in free energy models of learning and acting? A bounded rationality perspective Cognitive Neuroscience, 6(4):215-216, December 2015 (article)

DOI [BibTex]

2008

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Modelling contrast discrimination data suggest both the pedestal effect and stochastic resonance to be caused by the same mechanism

Goris, R., Wagemans, J., Wichmann, F.

Journal of Vision, 8(15):1-21, November 2008 (article)

Abstract
Computational models of spatial vision typically make use of a (rectified) linear filter, a nonlinearity and dominant late noise to account for human contrast discrimination data. Linear–nonlinear cascade models predict an improvement in observers' contrast detection performance when low, subthreshold levels of external noise are added (i.e., stochastic resonance). Here, we address the issue whether a single contrast gain-control model of early spatial vision can account for both the pedestal effect, i.e., the improved detectability of a grating in the presence of a low-contrast masking grating, and stochastic resonance. We measured contrast discrimination performance without noise and in both weak and moderate levels of noise. Making use of a full quantitative description of our data with few parameters combined with comprehensive model selection assessments, we show the pedestal effect to be more reduced in the presence of weak noise than in moderate noise. This reduction rules out independent, additive sources of performance improvement and, together with a simulation study, supports the parsimonious explanation that a single mechanism underlies the pedestal effect and stochastic resonance in contrast perception.

Web DOI [BibTex]

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Frequent Subgraph Retrieval in Geometric Graph Databases

Nowozin, S., Tsuda, K.

(180), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

Abstract
Discovery of knowledge from geometric graph databases is of particular importance in chemistry and biology, because chemical compounds and proteins are represented as graphs with 3D geometric coordinates. In such applications, scientists are not interested in the statistics of the whole database. Instead they need information about a novel drug candidate or protein at hand, represented as a query graph. We propose a polynomial-delay algorithm for geometric frequent subgraph retrieval. It enumerates all subgraphs of a single given query graph which are frequent geometric epsilon-subgraphs under the entire class of rigid geometric transformations in a database. By using geometric epsilon-subgraphs, we achieve tolerance against variations in geometry. We compare the proposed algorithm to gSpan on chemical compound data, and we show that for a given minimum support the total number of frequent patterns is substantially limited by requiring geometric matching. Although the computation time per pattern is larger than for non-geometric graph mining, the total time is within a reasonable level even for small minimum support.

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Nowozin, S., Tsuda, K. Frequent Subgraph Retrieval in Geometric Graph Databases (180), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

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The Effect of Mutual Information on Independent Component Analysis in EEG/MEG Analysis: A Simulation Study

Neumann, A., Grosse-Wentrup, M., Buss, M., Gramann, K.

International Journal of Neuroscience, 118(11):1534-1546, November 2008 (article)

Abstract
Objective: This study investigated the influence of mutual information (MI) on temporal and dipole reconstruction based on independent components (ICs) derived from independent component analysis (ICA). Method: Artificial electroencephalogram (EEG) datasets were created by means of a neural mass model simulating cortical activity of two neural sources within a four-shell spherical head model. Mutual information between neural sources was systematicallyvaried. Results: Increasing spatial error for reconstructed locations of ICs with increasing MI was observed. By contrast, the reconstruction error for the time course of source activity was largely independent of MI but varied systematically with Gaussianity of the sources. Conclusion: Independent component analysis is a viable tool for analyzing the temporal activity of EEG/MEG (magnetoencephalography) sources even if the underlying neural sources are mutually dependent. However, if ICA is used as a preprocessing algorithm for source localization, mutual information between sources introduces a bias in the reconstructed locations of the sources. Significance: Studies using ICA-algorithms based on MI have to be aware of possible errors in the spatial reconstruction of sources if these are coupled with other neural sources.

Web DOI [BibTex]

Neumann, A., Grosse-Wentrup, M., Buss, M., Gramann, K. The Effect of Mutual Information on Independent Component Analysis in EEG/MEG Analysis: A Simulation Study International Journal of Neuroscience, 118(11):1534-1546, November 2008 (article)

Web DOI [BibTex]

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gBoost: A Mathematical Programming Approach to Graph Classification and Regression

Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., Tsuda, K.

Machine Learning, 75(1):69-89, November 2008 (article)

Abstract
Graph mining methods enumerate frequently appearing subgraph patterns, which can be used as features for subsequent classification or regression. However, frequent patterns are not necessarily informative for the given learning problem. We propose a mathematical programming boosting method (gBoost) that progressively collects informative patterns. Compared to AdaBoost, gBoost can build the prediction rule with fewer iterations. To apply the boosting method to graph data, a branch-and-bound pattern search algorithm is developed based on the DFS code tree. The constructed search space is reused in later iterations to minimize the computation time. Our method can learn more efficiently than the simpler method based on frequent substructure mining, because the output labels are used as an extra information source for pruning the search space. Furthermore, by engineering the mathematical program, a wide range of machine learning problems can be solved without modifying the pattern search algorithm.

PDF DOI [BibTex]

Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., Tsuda, K. gBoost: A Mathematical Programming Approach to Graph Classification and Regression Machine Learning, 75(1):69-89, November 2008 (article)

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Machine Learning for Motor Skills in Robotics

Peters, J.

K{\"u}nstliche Intelligenz, 2008(4):41-43, November 2008 (article)

Abstract
Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator and humanoid robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.

PDF Web [BibTex]

Peters, J. Machine Learning for Motor Skills in Robotics K{\"u}nstliche Intelligenz, 2008(4):41-43, November 2008 (article)

PDF Web [BibTex]

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Kernels, Regularization and Differential Equations

Steinke, F., Schölkopf, B.

Pattern Recognition, 41(11):3271-3286, November 2008 (article)

Abstract
Many common machine learning methods such as Support Vector Machines or Gaussian process inference make use of positive definite kernels, reproducing kernel Hilbert spaces, Gaussian processes, and regularization operators. In this work these objects are presented in a general, unifying framework, and interrelations are highlighted. With this in mind we then show how linear stochastic differential equation models can be incorporated naturally into the kernel framework. And vice versa, many kernel machines can be interpreted in terms of differential equations. We focus especially on ordinary differential equations, also known as dynamical systems, and it is shown that standard kernel inference algorithms are equivalent to Kalman filter methods based on such models. In order not to cloud qualitative insights with heavy mathematical machinery, we restrict ourselves to finite domains, implying that differential equations are treated via their corresponding finite difference equations.

PDF DOI [BibTex]

Steinke, F., Schölkopf, B. Kernels, Regularization and Differential Equations Pattern Recognition, 41(11):3271-3286, November 2008 (article)

PDF DOI [BibTex]

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Simultaneous Implicit Surface Reconstruction and Meshing

Giesen, J., Maier, M., Schölkopf, B.

(179), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

Abstract
We investigate an implicit method to compute a piecewise linear representation of a surface from a set of sample points. As implicit surface functions we use the weighted sum of piecewise linear kernel functions. For such a function we can partition Rd in such a way that these functions are linear on the subsets of the partition. For each subset in the partition we can then compute the zero level set of the function exactly as the intersection of a hyperplane with the subset.

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Giesen, J., Maier, M., Schölkopf, B. Simultaneous Implicit Surface Reconstruction and Meshing (179), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

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Taxonomy Inference Using Kernel Dependence Measures

Blaschko, M., Gretton, A.

(181), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

Abstract
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, taking into account the relations between different clusters is shown to substantially improve the quality of the clustering, when compared with state-of-the-art algorithms in the literature (both spectral clustering and a previous dependence maximization approach). We demonstrate our algorithm on image and text data.

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Blaschko, M., Gretton, A. Taxonomy Inference Using Kernel Dependence Measures (181), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

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