Publications

Empirical Inference Miscellaneous Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws? Veiga, F., Peters, J. 2016 (Published) arXiv BibTeX

Empirical Inference Conference Paper Causal and statistical learning Schölkopf, B., Janzing, D., Lopez-Paz, D. Oberwolfach Reports, 13(3):1896-1899, (Editors: A. Christmann and K. Jetter and S. Smale and D.-X. Zhou), 2016 (Published) PDF DOI URL BibTeX

Empirical Inference Article Causal discovery and inference: concepts and recent methodological advances Spirtes, P., Zhang, K. Applied Informatics, 3(3):1-28, 2016 (Published) DOI BibTeX

Empirical Inference Article Causal inference using invariant prediction: identification and confidence intervals Peters, J., Bühlmann, P., Meinshausen, N. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 78(5):947-1012, 2016, (with discussion) (Published) DOI URL BibTeX

Empirical Inference Article Comparison of Positron Emission Tomography Quantification Using Magnetic Resonance–and Computed Tomography–Based Attenuation Correction in Physiological Tissues and Lesions: A Whole-Body Positron Emission Tomography/Magnetic Resonance Study in 66 Patients Seith, F., Gatidis, S., Schmidt, H., Bezrukov, I., la Fougère, C., Nikolaou, K., Pfannenberg, C., Schwenzer, N. Investigative Radiology, 51(1):66-71, 2016 (Published) DOI URL BibTeX

Empirical Inference Article Distinguishing cause from effect using observational data: methods and benchmarks Mooij, J., Peters, J., Janzing, D., Zscheischler, J., Schölkopf, B. Journal of Machine Learning Research, 17(32):1-102, 2016 (Published) URL BibTeX

Empirical Inference Probabilistic Numerics Article Dual Control for Approximate Bayesian Reinforcement Learning Klenske, E. D., Hennig, P. Journal of Machine Learning Research, 17(127):1-30, 2016 (Published) PDF URL BibTeX

Empirical Inference Miscellaneous Empirical Inference (2010-2015) Scientific Advisory Board Report, 2016 (Published) pdf BibTeX

Empirical Inference Article Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm Gomez Rodriguez, M., Song, L., Daneshmand, H., Schölkopf, B. Journal of Machine Learning Research, 17(90):1-29, 2016 (Published) URL BibTeX

Empirical Inference Probabilistic Numerics Article Gaussian Process-Based Predictive Control for Periodic Error Correction Klenske, E. D., Zeilinger, M., Schölkopf, B., Hennig, P. IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (Published) PDF DOI BibTeX

Empirical Inference Article Hierarchical Relative Entropy Policy Search Daniel, C., Neumann, G., Kroemer, O., Peters, J. Journal of Machine Learning Research, 17(93):1-50, 2016 (Published) URL BibTeX

Empirical Inference Poster Hippocampal neural events predict ongoing brain-wide BOLD activity Besserve, M., Logothetis, N. K. 47th Annual Meeting of the Society for Neuroscience (Neuroscience 2016), 2016 BibTeX

Empirical Inference Article Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach Grosse-Wentrup, M., Janzing, D., Siegel, M., Schölkopf, B. NeuroImage, 125:825-833, 2016 (Published) PDF DOI BibTeX

Empirical Inference Article Influence Estimation and Maximization in Continuous-Time Diffusion Networks Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B. ACM Transactions on Information Systems, 34(2):9:1-9:33, 2016 (Published) DOI BibTeX

Empirical Inference Article Influence of initial fixation position in scene viewing Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A., Engbert, R. Vision Research, 129:33-49, 2016 (Published) DOI URL BibTeX

Empirical Inference Article Kernel Mean Shrinkage Estimators Muandet, K., Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B. Journal of Machine Learning Research, 17(48):1-41, 2016 (Published) URL BibTeX

Empirical Inference Article Learning Taxonomy Adaptation in Large-scale Classification Babbar, R., Partalas, I., Gaussier, E., Amini, M., Amblard, C. Journal of Machine Learning Research, 17(98):1-37, 2016 (Published) URL BibTeX

Empirical Inference Article Learning to Deblur Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (Published) DOI BibTeX

Empirical Inference Article MERLiN: Mixture Effect Recovery in Linear Networks Weichwald, S., Grosse-Wentrup, M., Gretton, A. IEEE Journal of Selected Topics in Signal Processing, 10(7):1254-1266, 2016 (Published) Arxiv Code PDF DOI BibTeX

Empirical Inference Article Methods for causal inference from gene perturbation experiments and validation Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., Bühlmann, P. Proceedings of the National Academy of Sciences, 113(27):7361-7368, 2016 (Published) DOI BibTeX

Empirical Inference Article Modeling Confounding by Half-Sibling Regression Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J. Proceedings of the National Academy of Science, 113(27):7391-7398, 2016 (Published) Code DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Modeling Variability of Musculoskeletal Systems with Heteroscedastic Gaussian Processes Büchler, D., Calandra, R., Peters, J. Workshop on Neurorobotics, Neural Information Processing Systems (NIPS 2016), 2016 (Published) NIPS16Neurorobotics BibTeX

Empirical Inference Book Chapter Nonlinear functional causal models for distinguishing cause from effect Zhang, K., Hyvärinen, A. In Statistics and Causality: Methods for Applied Empirical Research, 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (Published) BibTeX

Empirical Inference Article Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data Schütt, H. H., Harmeling, S., Macke, J. H., Wichmann, F. A. Vision Research, 122:105-123, 2016 (Published) DOI URL BibTeX

Empirical Inference Article Preface to the ACM TIST Special Issue on Causal Discovery and Inference Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J. ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 17, 2016 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Article Probabilistic Inference for Determining Options in Reinforcement Learning Daniel, C., van Hoof, H., Peters, J., Neumann, G. Machine Learning, Special Issue, 104(2):337-357, (Editors: Gärtner, T., Nanni, M., Passerini, A. and Robardet, C.), European Conference on Machine Learning im Machine Learning, Journal Track, 2016, Best Student Paper Award of ECML-PKDD 2016 DOI BibTeX

Empirical Inference Article Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Rueckert, E., Camernik, J., Peters, J., Babic, J. Scientific Reports, 6(1):article no. 28455, 2016 (Published) DOI BibTeX

Empirical Inference Article Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation Townsend, J., Koep, N., Weichwald, S. Journal of Machine Learning Research, 17(137):1-5, 2016 (Published) PDF Arxiv Code Project page URL BibTeX

Empirical Inference Article Recurrent Spiking Networks Solve Planning Tasks Rueckert, E., Kappel, D., Tanneberg, D., Pecevski, D., Peters, J. Nature PG: Scientific Reports, 6(1):article no. 21142, 2016 (Published) DOI BibTeX

Empirical Inference Conference Paper Screening Rules for Convex Problems Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M. 13th International (Virtual) OPT Workshop on Optimization for Machine Learning, 2016 (Submitted) URL BibTeX

Empirical Inference Article Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS Fomina, T., Lohmann, G., Erb, M., Ethofer, T., Schölkopf, B., Grosse-Wentrup, M. Journal of Neural Engineering, 13(6):article no. 066021, 2016 URL BibTeX

Empirical Inference Poster Statistical source separation of rhythmic LFP patterns during sharp wave ripples in the macaque hippocampus Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M. 47th Annual Meeting of the Society for Neuroscience (Neuroscience 2016), 2016 BibTeX

Empirical Inference Conference Paper Surface Roughness Discrimination Using Bioinspired Tactile Sensors Yi, Z., Zhang, Y., Peters, J. Proceedings of the 16th International Conference on Biomedical Engineering, 2016 (Published) BibTeX

Empirical Inference Article Testing models of peripheral encoding using metamerism in an oddity paradigm Wallis, T. S. A., Bethge, M., Wichmann, F. A. Journal of Vision, 16(2):4, 2016 (Published) DOI BibTeX

Empirical Inference Article The population of long-period transiting exoplanets Foreman-Mackey, D., Morton, T. D., Hogg, D. W., Agol, E., Schölkopf, B. The Astronomical Journal, 152(6):article no. 206, 2016 (Published) URL BibTeX

Empirical Inference Ph.D. Thesis Tractable Structured Prediction using the Permutohedral Lattice Kiefel, M. ETH Zurich, 2016 (Published) BibTeX

Empirical Inference Article Transfer Learning in Brain-Computer Interfaces Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M. IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (Published) PDF DOI BibTeX

Empirical Inference Miscellaneous Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T. 2016 (Published) Arxiv BibTeX

Empirical Inference Conference Paper Adaptive information-theoretic bounded rational decision-making with parametric priors Grau-Moya, J., Braun, D. 1-4, NIPS 2015 Workshop on Bounded Optimality and Rational Metareasoning, December 2015

Abstract ›

Deviations from rational decision-making due to limited computational resources have been studied in the field of bounded rationality, originally proposed by Herbert Simon. There have been a number of different approaches to model bounded rationality ranging from optimality principles to heuristics. Here we take an information-theoretic approach to bounded rationality, where information-processing costs are measured by the relative entropy between a posterior decision strategy and a given fixed prior strategy. In the case of multiple environments, it can be shown that there is an optimal prior rendering the bounded rationality problem equivalent to the rate distortion problem for lossy compression in information theory. Accordingly, the optimal prior and posterior strategies can be computed by the well-known Blahut-Arimoto algorithm which requires the computation of partition sums over all possible outcomes and cannot be applied straightforwardly to continuous problems. Here we derive a sampling-based alternative update rule for the adaptation of prior behaviors of decision-makers and we show convergence to the optimal prior predicted by rate distortion theory. Importantly, the update rule avoids typical infeasible operations such as the computation of partition sums. We show in simulations a proof of concept for discrete action and environment domains. This approach is not only interesting as a generic computational method, but might also provide a more realistic model of human decision-making processes occurring on a fast and a slow time scale.

BibTeX

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

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

Empirical Inference Article What is epistemic value in free energy models of learning and acting? A bounded rationality perspective Ortega, P., Braun, D. Cognitive Neuroscience, 6(4):215-216, December 2015

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

Empirical Inference Autonomous Motion Conference Paper A Comparison of Contact Distribution Representations for Learning to Predict Object Interactions Leischnig, S., Luettgen, S., Kroemer, O., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 616-622, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Evaluation of Interactive Object Recognition with Tactile Sensing Hoelscher, J., Peters, J., Hermans, T. In 15th IEEE-RAS International Conference on Humanoid Robots, 310-317, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper First-Person Tele-Operation of a Humanoid Robot Fritsche, L., Unverzagt, F., Peters, J., Calandra, R. In 15th IEEE-RAS International Conference on Humanoid Robots, 997-1002, Humanoids, November 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin Calandra, R., Ivaldi, S., Deisenroth, M., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 690-695, Humanoids, November 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Optimizing Robot Striking Movement Primitives with Iterative Learning Control Koc, O., Maeda, G., Neumann, G., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 80-87, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Probabilistic Segmentation Applied to an Assembly Task Lioutikov, R., Neumann, G., Maeda, G., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 533-540, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Article 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 DOI BibTeX

Empirical Inference Ph.D. Thesis easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies Grimm, D. Eberhard Karls Universität Tübingen, November 2015 BibTeX

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