Publications

Empirical Inference Article Extreme events in gross primary production: a characterization across continents Zscheischler, J., Reichstein, M., Harmeling, S., Rammig, A., Tomelleri, E., Mahecha, M. Biogeosciences, 11:2909-2924, 2014 PDF Web DOI BibTeX

Empirical Inference Poster FID-guided retrospective motion correction based on autofocusing Babayeva, M., Loktyushin, A., Kober, T., Granziera, C., Nickisch, H., Gruetter, R., Krueger, G. Joint Annual Meeting ISMRM-ESMRMB 2014, Milano, Italy, 2014 BibTeX

Empirical Inference Article Factors controlling decomposition rates of fine root litter in temperate forests and grasslands Solly, E., Schöning, I., Boch, S., Kandeler, E., Marhan, S., Michalzik, B., Müller, J., Zscheischler, J., Trumbore, S., Schrumpf, M. Plant and Soil, 2014 PDF DOI BibTeX

Empirical Inference Conference Paper Fast Newton methods for the group fused lasso Wytock, M., Sra, S., Kolter, J. Z. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, 888-897, (Editors: Zhang, N. L. and Tian, J.), AUAI Press, UAI, 2014 (Published) URL BibTeX

Empirical Inference Book Chapter Fuzzy Fibers: Uncertainty in dMRI Tractography Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G. In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (Published) BibTeX

Empirical Inference Book Chapter Higher-Order Tensors in Diffusion Imaging Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L. In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (Published) BibTeX

Empirical Inference Article IM3SHAPE: Maximum likelihood galaxy shear measurement code for cosmic gravitational lensing Zuntz, J., Kacprzak, T., Voigt, L., Hirsch, M., Rowe, B., Bridle, S. Astrophysics Source Code Library, 1:09013, 2014 URL BibTeX

Empirical Inference Article Identifiability of Gaussian Structural Equation Models with Equal Error Variances Peters, J., Bühlman, P. Biometrika, 101(1):219-228, 2014 (Published) DOI BibTeX

Empirical Inference Article Impact of Large-Scale Climate Extremes on Biospheric Carbon Fluxes: An Intercomparison Based on MsTMIP Data Zscheischler, J., Michalak, A., Schwalm, M., Mahecha, M., Huntzinger, D., Reichstein, M., Berthier, G., Ciais, P., Cook, R., El-Masri, B., Huang, M., Ito, A., Jain, A., King, A., Lei, H., Lu, C., Mao, J., Peng, S., Poulter, B., Ricciuto, D., et al. Global Biogeochemical Cycles, 2014 Web DOI BibTeX

Autonomous Motion Empirical Inference Probabilistic Numerics Conference Paper Incremental Local Gaussian Regression Meier, F., Hennig, P., Schaal, S. In Advances in Neural Information Processing Systems 27, 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS 2014), 2014, clmc PDF URL BibTeX

Empirical Inference Article Indirect Robot Model Learning for Tracking Control Bocsi, B., Csató, L., Peters, J. Advanced Robotics, 28(9):589-599, 2014 PDF DOI BibTeX

Empirical Inference Conference Paper Inferring latent structures via information inequalities Chaves, R., Luft, L., Maciel, T., Gross, D., Janzing, D., Schölkopf, B. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, 112-121, (Editors: NL Zhang and J Tian), AUAI Press, Corvallis, Oregon, UAI, 2014 PDF BibTeX

Empirical Inference Conference Paper Interaction Primitives for Human-Robot Cooperation Tasks Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J. In Proceedings of 2014 IEEE International Conference on Robotics and Automation, 2831-2837, IEEE, ICRA, 2014 (Published) PDF DOI BibTeX

Empirical Inference Article Juggling revisited — A voxel based morphometry study with expert jugglers Gerber, P., Schlaffke, L., Heba, S., Greenlee, M., Schultz, T., Schmidt-Wilcke, T. NeuroImage, 95:320-325, 2014 (Published) Web DOI BibTeX

Empirical Inference Conference Paper Kernel Mean Estimation and Stein Effect Muandet, K., Fukumizu, K., Sriperumbudur, B., Gretton, A., Schölkopf, B. In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), 10-18, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 PDF BibTeX

Empirical Inference Conference Paper Kernel Mean Estimation via Spectral Filtering Muandet, K., Sriperumbudur, B., Schölkopf, B. In Advances in Neural Information Processing Systems 27, 1-9, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS 2014), 2014 (Published) Web URL BibTeX

Empirical Inference Article Kernel methods in system identification, machine learning and function estimation: A survey Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L. Automatica, 50(3):657-682, 2014 Web DOI BibTeX

Empirical Inference Conference Paper Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot Lioutikov, R., Kroemer, O., Peters, J., Maeda, G. In Proceedings of the 13th International Conference on Intelligent Autonomous Systems, 302:1601-1611, Advances in Intelligent Systems and Computing, (Editors: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H.), Springer, IAS, 2014 (Published) DOI BibTeX

Empirical Inference Book Learning Motor Skills: From Algorithms to Robot Experiments Kober, J., Peters, J. 97:191, Springer Tracts in Advanced Robotics, Springer, 2014 DOI BibTeX

Empirical Inference Article Learning Strategies in Table Tennis using Inverse Reinforcement Learning Mülling, K., Boularias, A., Mohler, B., Schölkopf, B., Peters, J. Biological Cybernetics, 108(5):603-619, 2014 (Published) PDF DOI BibTeX

Empirical Inference Conference Paper Learning to Deblur Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B. In NIPS 2014 Deep Learning and Representation Learning Workshop, 28th Annual Conference on Neural Information Processing Systems (NIPS 2014), 2014 (Published) URL BibTeX

Empirical Inference Conference Paper Learning to Predict Phases of Manipulation Tasks as Hidden States Kroemer, O., van Hoof, H., Neumann, G., Peters, J. In Proceedings of 2014 IEEE International Conference on Robotics and Automation, 4009-4014, IEEE, ICRA, 2014 (Published) PDF DOI BibTeX

Empirical Inference Conference Paper Learning to Unscrew a Light Bulb from Demonstrations Manschitz, S., Kober, J., Gienger, M., Peters, J. In Proceedings for the joint conference of ISR 2014, 45th International Symposium on Robotics and Robotik 2014, 2014 (Published) BibTeX

Empirical Inference Conference Paper Mask-Specific Inpainting with Deep Neural Networks Köhler, R., Schuler, C., Schölkopf, B., Harmeling, S. In Pattern Recognition (GCPR 2014), 523-534, (Editors: X Jiang, J Hornegger, and R Koch), Springer, 2014, Lecture Notes in Computer Science PDF DOI BibTeX

Empirical Inference Article Modular proximal optimization with application to total variation regularization Barbero, A. J., Sra, S. 2014 (In revision) URL BibTeX

Empirical Inference Conference Paper Multi-Task Feature Selection on Multiple Networks via Maximum Flows Sugiyama, M., Azencott, C., Grimm, D., Kawahara, Y., Borgwardt, K. In Proceedings of the 2014 SIAM International Conference on Data Mining , 199-207, SIAM, 2014 Web PDF DOI BibTeX

Empirical Inference Conference Paper Multi-Task Policy Search for Robotics Deisenroth, M., Englert, P., Peters, J., Fox, D. In Proceedings of 2014 IEEE International Conference on Robotics and Automation, 3876-3881, IEEE, ICRA, 2014 (Published) PDF DOI BibTeX

Empirical Inference Conference Paper Multi-modal filtering for non-linear estimation Kamthe, S., Peters, J., Deisenroth, M. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 7979-7983, IEEE, ICASSP, 2014 (Published) PDF DOI BibTeX

Empirical Inference Article Natural Evolution Strategies Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., Schmidhuber, J. Journal of Machine Learning Research, 15:949-980, 2014 PDF BibTeX

Empirical Inference Book Chapter Nonconvex Proximal Splitting with Computational Errors Sra, S. In Regularization, Optimization, Kernels, and Support Vector Machines, 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (Published) BibTeX

Empirical Inference Article Occam’s Razor in sensorimotor learning Genewein, T., Braun, D. Proceedings of the Royal Society of London B, 281(1783):1-7, May 2014

Abstract ›

A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor—an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models—a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants’ choice behaviour was quantitatively consistent with Bayesian Occam's Razor. We also show that participants’ drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam's Razor is a general behavioural principle already present during sensorimotor processing.

DOI BibTeX

Empirical Inference Article On power law distributions in large-scale taxonomies Babbar, R., Metzig, C., Partalas, I., Gaussier, E., Amini, M. SIGKDD Explorations, Special Issue on Big Data, 16(1):47-56, 2014 BibTeX

Empirical Inference Article On the Quantification Accuracy, Homogeneity, and Stability of Simultaneous Positron Emission Tomography/Magnetic Resonance Imaging Systems Schmidt, H., Schwenzer, N., Bezrukov, I., Mantlik, F., Kolb, A., Kupferschläger, J., Pichler, B. Investigative Radiology, 49(6):373-381, 2014 Web DOI BibTeX

Empirical Inference Conference Paper Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem Gomez Rodriguez, M., Song, L., Schölkopf, B. Proceedings of the 27th Conference on Learning Theory, 35:1276-1279, (Editors: Balcan, M.-F. and Szepesvári, C.), JMLR.org, COLT, 2014 PDF BibTeX

Empirical Inference Article Policy Evaluation with Temporal Differences: A Survey and Comparison Dann, C., Neumann, G., Peters, J. Journal of Machine Learning Research, 15:809-883, 2014 PDF BibTeX

Empirical Inference Conference Paper Policy Search For Learning Robot Control Using Sparse Data Bischoff, B., Nguyen-Tuong, D., van Hoof, H., McHutchon, A., Rasmussen, C., Knoll, A., Peters, J., Deisenroth, M. In Proceedings of 2014 IEEE International Conference on Robotics and Automation, 3882-3887, IEEE, ICRA, 2014 (Published) PDF DOI BibTeX

Empirical Inference Article Predicting Motor Learning Performance from Electroencephalographic Data Meyer, T., Peters, J., Zander, T., Schölkopf, B., Grosse-Wentrup, M. Journal of NeuroEngineering and Rehabilitation, 11:24, 2014 PDF DOI BibTeX

Empirical Inference Article Principles of PET/MR Imaging Disselhorst, J. A., Bezrukov, I., Kolb, A., Parl, C., Pichler, B. J. Journal of Nuclear Medicine, 55(6, Supplement 2):2S-10S, 2014 (Published) DOI BibTeX

Empirical Inference Probabilistic Numerics Conference Paper Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers Schober, M., Kasenburg, N., Feragen, A., Hennig, P., Hauberg, S. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Lecture Notes in Computer Science Vol. 8675, 265-272, (Editors: P. Golland, N. Hata, C. Barillot, J. Hornegger and R. Howe), Springer, Heidelberg, MICCAI, 2014 DOI BibTeX

Empirical Inference Probabilistic Numerics Conference Paper Probabilistic ODE Solvers with Runge-Kutta Means Schober, M., Duvenaud, D., Hennig, P. In Advances in Neural Information Processing Systems 27, 739-747, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS 2014), 2014 (Published) Web URL BibTeX

Empirical Inference Conference Paper Quantifying Information Overload in Social Media and its Impact on Social Contagions Gomez Rodriguez, M., Gummadi, K., Schölkopf, B. In Proceedings of the Eighth International Conference on Weblogs and Social Media, 170-179, (Editors: E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh), AAAI Press, ICWSM, 2014 (Published) Web BibTeX

Empirical Inference Talk Quantifying statistical dependency Besserve, M. Research Network on Learning Systems Summer School, 2014 (Published) BibTeX

Empirical Inference Article Quantifying the effect of intertrial dependence on perceptual decisions Fründ, I., Wichmann, F., Macke, J. Journal of Vision, 14(7):1-16, 2014 (Published) Web PDF DOI URL BibTeX

Empirical Inference Conference Paper Randomized Nonlinear Component Analysis Lopez-Paz, D., Sra, S., Smola, A., Ghahramani, Z., Schölkopf, B. In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), 1359-1367, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 PDF BibTeX

Empirical Inference Conference Paper Re-ranking Approach to Classification in Large-scale Power-law Distributed Category Systems Babbar, R., Partalas, I., Gaussier, E., Amini, M. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 1059-1062, (Editors: S Geva and A Trotman and P Bruza and CLA Clarke and K Järvelin), ACM, New York, NY, USA, SIGIR, 2014 DOI BibTeX

Empirical Inference Conference Paper Riemannian Sparse Coding for Positive Definite Matrices Cherian, A., Sra, S. In 13th European Conference on Computer Vision, LNCS 8691:299-314, (Editors: Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T.), Springer, ECCV, 2014 (Published) DOI BibTeX

Empirical Inference Conference Paper Sample-Based Information-Theoretic Stochastic Optimal Control Lioutikov, R., Paraschos, A., Peters, J., Neumann, G. In Proceedings of 2014 IEEE International Conference on Robotics and Automation, 3896-3902, IEEE, ICRA, 2014 (Published) PDF DOI BibTeX

Empirical Inference Probabilistic Numerics Conference Paper Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Gunter, T., Osborne, M., Garnett, R., Hennig, P., Roberts, S. In Advances in Neural Information Processing Systems 27, 2789-2797, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS 2014), 2014 (Published) Web URL BibTeX

Empirical Inference Article Simultaneous Whole-Body PET/MR Imaging in Comparison to PET/CT in Pediatric Oncology: Initial Results Schäfer, J. F., Gatidis, S., Schmidt, H., Gückel, B., Bezrukov, I., Pfannenberg, C. A., Reimold, M., M., E., Fuchs, J., Claussen, C. D., Schwenzer, N. F. Radiology, 273(1):220-231, 2014 (Published) DOI BibTeX

Empirical Inference Book Chapter Single-Source Domain Adaptation with Target and Conditional Shift Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C. In Regularization, Optimization, Kernels, and Support Vector Machines, 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 BibTeX

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