Publications

Empirical Inference Article Context affects lightness at the level of surfaces Maertens, M., Wichmann, F., Shapley, R. Journal of Vision, 15(1):1-15, 2015 (Published) Web PDF DOI URL BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Technical Report 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 (Published) URL BibTeX

Empirical Inference Article 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., et al. Nature Biotechnology, 33:51-57, 2015 DOI BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Conference Paper Discovering Temporal Causal Relations from Subsampled Data Gong, M., Zhang, K., Schölkopf, B., Tao, D., Geiger, P. In Proceedings of the 32nd International Conference on Machine Learning, 37:1898–1906, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (Published) PDF URL BibTeX

Empirical Inference Poster Disparity estimation from a generative light field model Köhler, R., Schölkopf, B., Hirsch, M. IEEE International Conference on Computer Vision (ICCV 2015), Workshop on Inverse Rendering, 2015, Note: This work has been presented as a poster and is not included in the workshop proceedings. BibTeX

Empirical Inference Conference Paper Distinguishing Cause from Effect Based on Exogeneity Zhang, K., Zhang, J., Schölkopf, B. In Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, 261-271, (Editors: Ramanujam, R.), TARK, 2015 (Published) BibTeX

Empirical Inference Conference Paper Efficient Learning of Linear Separators under Bounded Noise Awasthi, P., Balcan, M., Haghtalab, N., Urner, R. In Proceedings of the 28th Conference on Learning Theory, 40:167-190, (Editors: Grünwald, P. and Hazan, E. and Kale, S.), JMLR, COLT, 2015 (Published) URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Extracting Low-Dimensional Control Variables for Movement Primitives Rueckert, E., Mundo, J., Paraschos, A., Peters, J., Neumann, G. In IEEE International Conference on Robotics and Automation, 1511-1518, ICRA, 2015 (Published) DOI URL BibTeX

Empirical Inference Article 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 PDF DOI BibTeX

Empirical Inference Conference Paper Hierarchical Label Queries with Data-Dependent Partitions Kpotufe, S., Urner, R., Ben-David, S. In Proceedings of the 28th Conference on Learning Theory, 40:1176-1189, (Editors: Grünwald, P. and Hazan, E. and Kale, S. ), JMLR, COLT, 2015 (Published) URL BibTeX

Empirical Inference Conference Paper Identification of Time-Dependent Causal Model: A Gaussian Process Treatment Huang, B., Zhang, K., Schölkopf, B. In 24th International Joint Conference on Artificial Intelligence, Machine Learning Track, 3561-3568, (Editors: Yang, Q. and Wooldridge, M.), AAAI Press, Palo Alto, California USA, IJCAI15, 2015 (Published) URL BibTeX

Empirical Inference Conference Paper Identification of the Default Mode Network with Electroencephalography Fomina, T., Hohmann, M. R., Schölkopf, B., Grosse-Wentrup, M. In Proceedings of the 37th IEEE Conference for Engineering in Medicine and Biology, 7566-7569, EMBC, 2015 (Published) DOI BibTeX

Empirical Inference Article Improved Bayesian Information Criterion for Mixture Model Selection Mehrjou, A., Hosseini, R., Araabi, B. Pattern Recognition Letters, 69:22-27, 2015 (Published) DOI BibTeX

Empirical Inference Poster Increasing the sensitivity of Kepler to Earth-like exoplanets Foreman-Mackey, D., Hogg, D., Schölkopf, B., Wang, D. Workshop: 225th American Astronomical Society Meeting 2015 , 105.01D, 2015 (Published) Web URL BibTeX

Empirical Inference Probabilistic Numerics Conference Paper Inference of Cause and Effect with Unsupervised Inverse Regression Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B. In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38:847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (Published) Web PDF BibTeX

Empirical Inference Talk Information-Theoretic Implications of Classical and Quantum Causal Structures Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D. 18th Conference on Quantum Information Processing (QIP 2015), 2015 (Published) Web URL BibTeX

Empirical Inference Book Chapter Justifying Information-Geometric Causal Inference Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B. In Measures of Complexity: Festschrift for Alexey Chervonenkis, 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Learning Inverse Dynamics Models with Contacts Calandra, R., Ivaldi, S., Deisenroth, M., Rückert, E., Peters, J. In IEEE International Conference on Robotics and Automation, 3186-3191, ICRA, 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Article 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:97-107, 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Learning Optimal Striking Points for A Ping-Pong Playing Robot Huang, Y., Schölkopf, B., Peters, J. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 4587-4592, IROS, 2015 (Published) PDF DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Learning multiple collaborative tasks with a mixture of Interaction Primitives Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G. In IEEE International Conference on Robotics and Automation, 1535-1542, ICRA, 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Learning of Non-Parametric Control Policies with High-Dimensional State Features van Hoof, H., Peters, J., Neumann, G. In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38:995–1003, (Editors: Lebanon, G. and Vishwanathan, S.V.N. ), JMLR, AISTATS, 2015 (Published) URL BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Model-Based Relative Entropy Stochastic Search Abdolmaleki, A., Peters, J., Neumann, G. In Advances in Neural Information Processing Systems 28, 3523-3531, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), 2015 (Published) URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G. In Workshop on Machine Learning for Social Robotics, ICRA, 2015 (Published) URL BibTeX

Empirical Inference Conference Paper Multi-Source Domain Adaptation: A Causal View Zhang, K., Gong, M., Schölkopf, B. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 3150-3157, AAAI Press, AAAI, 2015 (Published) Web PDF URL BibTeX

Empirical Inference Conference Paper Neural Adaptive Sequential Monte Carlo Gu, S., Ghahramani, Z., Turner, R. E. Advances in Neural Information Processing Systems 28, 2629-2637, (Editors: Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett), 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), 2015 (Published) PDF Supplementary BibTeX

Empirical Inference Article On the Intersection Property of Conditional Independence and its Application to Causal Discovery Peters, J. Journal of Causal Inference, 3(1):97-108, 2015 (Published) DOI BibTeX

Empirical Inference Conference Paper Particle Gibbs for Infinite Hidden Markov Models Tripuraneni*, N., Gu*, S., Ge, H., Ghahramani, Z. Advances in Neural Information Processing Systems 28, 2395-2403, (Editors: Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett), 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), 2015, *equal contribution (Published) PDF BibTeX

Empirical Inference Conference Paper Peer grading in a course on algorithms and data structures Sajjadi, M. S. M., Alamgir, M., von Luxburg, U. Workshop on Machine Learning for Education (ML4Ed) at the 32th International Conference on Machine Learning (ICML), 2015 (Published) Arxiv BibTeX

Empirical Inference Conference Paper Peer grading in a course on algorithms and data structures Sajjadi, M. S. M., Alamgir, M., von Luxburg, U. Workshop on Crowdsourcing and Machine Learning (CrowdML) Workshop on Machine Learning for Education (ML4Ed) at at the 32th International Conference on Machine Learning (ICML), 2015 (Published) Arxiv BibTeX

Empirical Inference Probabilistic Numerics Article Probabilistic Interpretation of Linear Solvers Hennig, P. SIAM Journal on Optimization, 25(1):234-260, 2015 (Published) Web PDF DOI URL BibTeX

Empirical Inference Probabilistic Numerics Conference Paper Probabilistic Line Searches for Stochastic Optimization Mahsereci, M., Hennig, P. In Advances in Neural Information Processing Systems 28, 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), 2015 (Published)

Abstract ›

In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

Matlab research code URL BibTeX

Empirical Inference Probabilistic Numerics Article 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 (Published)

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

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Conference Paper Recent Methodological Advances in Causal Discovery and Inference Spirtes, P., Zhang, K. In 15th Conference on Theoretical Aspects of Rationality and Knowledge, 23-35, (Editors: Ramanujam, R.), TARK, 2015 BibTeX

Empirical Inference Conference Paper Removing systematic errors for exoplanet search via latent causes Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J. In Proceedings of The 32nd International Conference on Machine Learning, 37:2218–2226, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D.), JMLR, ICML, 2015 (Published) Extended version on arXiv URL BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Conference Paper Self-calibration of optical lenses Hirsch, M., Schölkopf, B. In IEEE International Conference on Computer Vision (ICCV 2015), 612-620, IEEE, 2015 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Semi-Autonomous 3rd-Hand Robot Lopes, M., Peters, J., Piater, J., Toussaint, M., Baisero, A., Busch, B., Erkent, O., Kroemer, O., Lioutikov, R., Maeda, G., Mollard, Y., Munzer, T., Shukla, D. In Workshop on Cognitive Robotics in Future Manufacturing Scenarios, European Robotics Forum, 2015 (Published) URL BibTeX

Empirical Inference Master Thesis Sequential Image Deconvolution Using Probabilistic Linear Algebra Gao, M. Technical University of Munich, Germany, 2015 BibTeX

Empirical Inference Article Spatial statistics and attentional dynamics in scene viewing Engbert, R., Trukenbrod, H., Barthelmé, S., Wichmann, F. Journal of Vision, 15(1):1-17, 2015 (Published) Web PDF DOI URL BibTeX

Empirical Inference Article Structural Intervention Distance (SID) for Evaluating Causal Graphs Peters, J., Bühlmann, P. Neural Computation , 27(3):771-799, 2015 (Published) DOI BibTeX

Empirical Inference Conference Paper Telling cause from effect in deterministic linear dynamical systems Shajarisales, N., Janzing, D., Schölkopf, B., Besserve, M. In Proceedings of the 32nd International Conference on Machine Learning, 37:285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 PDF BibTeX

Empirical Inference Technical Report 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 (Published) URL BibTeX

Empirical Inference Article The Randomized Causation Coefficient Lopez-Paz, D., Muandet, K., Recht, B. Journal of Machine Learning, 16:2901-2907, 2015 (Published) URL BibTeX

Empirical Inference Master Thesis The effect of frowning on attention Ibarra Chaoul, A. Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 BibTeX

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