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2015


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Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

2015

[BibTex]


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Neural Adaptive Sequential Monte Carlo

Gu, S., Ghahramani, Z., Turner, R. E.

Advances in Neural Information Processing Systems 28, pages: 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 (conference)

PDF Supplementary [BibTex]

PDF Supplementary [BibTex]


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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, pages: 1898–1906, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Active Nearest Neighbors in Changing Environments

Berlind, C., Urner, R.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1870-1879, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D. ), JMLR, ICML, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Learning Inverse Dynamics Models with Contacts

Calandra, R., Ivaldi, S., Deisenroth, M., Rückert, E., Peters, J.

In IEEE International Conference on Robotics and Automation, pages: 3186-3191, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation

Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J.

In Proceedings of the International Symposium of Robotics Research, ISRR, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

[BibTex]


Peer grading in a course on algorithms and data structures
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 (conference)

Arxiv [BibTex]

Arxiv [BibTex]


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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, pages: 2218–2226, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D.), JMLR, ICML, 2015 (inproceedings)

Extended version on arXiv link (url) [BibTex]

Extended version on arXiv link (url) [BibTex]


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Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Geiger, P., Zhang, K., Schölkopf, B., Gong, M., Janzing, D.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1917–1925, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Brain-Computer Interfacing in Amyotrophic Lateral Sclerosis: Implications of a Resting-State EEG Analysis

Jayaram, V., Widmann, N., Förster, C., Fomina, T., Hohmann, M. R., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Proceedings of the 37th IEEE Conference for Engineering in Medicine and Biology, pages: 6979-6982, EMBC, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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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, pages: 7566-7569, EMBC, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Towards Cognitive Brain-Computer Interfaces for Patients with Amyotrophic Lateral Sclerosis

Fomina, T., Schölkopf, B., Grosse-Wentrup, M.

In 7th Computer Science and Electronic Engineering Conference, pages: 77-80, Curran Associates, Inc., CEEC, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks

Kroemer, O., Daniel, C., Neumann, G., van Hoof, H., Peters, J.

In IEEE International Conference on Robotics and Automation, pages: 1503 - 1510, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Probabilistic Line Searches for Stochastic Optimization
Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 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 (inproceedings)

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 link (url) [BibTex]

Matlab research code link (url) [BibTex]


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BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

Rothenhäusler, D., Heinze, C., Peters, J., Meinshausen, N.

Advances in Neural Information Processing Systems 28, pages: 1513-1521, (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 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Particle Gibbs for Infinite Hidden Markov Models

Tripuraneni*, N., Gu*, S., Ge, H., Ghahramani, Z.

Advances in Neural Information Processing Systems 28, pages: 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, *equal contribution (conference)

PDF [BibTex]

PDF [BibTex]


Peer grading in a course on algorithms and data structures
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 (conference)

Arxiv [BibTex]

Arxiv [BibTex]


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A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.

In 18th International Conference on Medical Image Computing and Computer Assisted Intervention, 9349, pages: 597-604, Lecture Notes in Computer Science, MICCAI, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Recent Methodological Advances in Causal Discovery and Inference

Spirtes, P., Zhang, K.

In 15th Conference on Theoretical Aspects of Rationality and Knowledge, pages: 23-35, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

[BibTex]

[BibTex]


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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, pages: 4587-4592, IROS, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Model-Based Relative Entropy Stochastic Search

Abdolmaleki, A., Peters, J., Neumann, G.

In Advances in Neural Information Processing Systems 28, pages: 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 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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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 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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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, pages: 1511-1518, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Self-calibration of optical lenses

Hirsch, M., Schölkopf, B.

In IEEE International Conference on Computer Vision (ICCV 2015), pages: 612-620, IEEE, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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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, pages: 285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M. R., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, pages: 3187-3191, SMC, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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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, pages: 167-190, (Editors: Grünwald, P. and Hazan, E. and Kale, S.), JMLR, COLT, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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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, pages: 1535-1542, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Subspace Alignement based Domain Adaptation for RCNN detector

Raj, A., V., N., Tuytelaars, T.

Proceedings of the 26th British Machine Vision Conference (BMVC 2015), pages: 166.1-166.11, (Editors: Xianghua Xie and Mark W. Jones and Gary K. L. Tam), 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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Practical Probabilistic Programming with Monads

Ścibior, A., Ghahramani, Z., Gordon, A. D.

Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell, pages: 165-176, Haskell ’15, ACM, 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

link (url) [BibTex]


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Developing neural networks with neurons competing for survival

Peng, Z, Braun, DA

pages: 152-153, IEEE, Piscataway, NJ, USA, 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), August 2015 (conference)

Abstract
We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. We show in simulations that this naturally leads to abstractions in higher layers that emerge in a unsupervised fashion. When evaluating the network in a supervised learning paradigm, it is clear that our network is not competitive. What is interesting though is that this performance was achieved by neurons that simply struggle for survival and do not know about performance error. In contrast to most studies on neural evolution that rely on a network-wide fitness function, our goal was to show that learning behaviour can appear in a system without being driven by any specific utility function or reward signal.

DOI [BibTex]

DOI [BibTex]

2014


Human Pose Estimation with Fields of Parts
Human Pose Estimation with Fields of Parts

Kiefel, M., Gehler, P.

In Computer Vision – ECCV 2014, LNCS 8693, pages: 331-346, Lecture Notes in Computer Science, (Editors: Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne), Springer, 13th European Conference on Computer Vision, September 2014 (inproceedings)

Abstract
This paper proposes a new formulation of the human pose estimation problem. We present the Fields of Parts model, a binary Conditional Random Field model designed to detect human body parts of articulated people in single images. The Fields of Parts model is inspired by the idea of Pictorial Structures, it models local appearance and joint spatial configuration of the human body. However the underlying graph structure is entirely different. The idea is simple: we model the presence and absence of a body part at every possible position, orientation, and scale in an image with a binary random variable. This results into a vast number of random variables, however, we show that approximate inference in this model is efficient. Moreover we can encode the very same appearance and spatial structure as in Pictorial Structures models. This approach allows us to combine ideas from segmentation and pose estimation into a single model. The Fields of Parts model can use evidence from the background, include local color information, and it is connected more densely than a kinematic chain structure. On the challenging Leeds Sports Poses dataset we improve over the Pictorial Structures counterpart by 5.5% in terms of Average Precision of Keypoints (APK).

website pdf DOI Project Page [BibTex]

2014

website pdf DOI Project Page [BibTex]


Probabilistic Progress Bars
Probabilistic Progress Bars

Kiefel, M., Schuler, C., Hennig, P.

In Conference on Pattern Recognition (GCPR), 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

Abstract
Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user.

website+code pdf DOI [BibTex]

website+code pdf DOI [BibTex]


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Seeing the Arrow of Time

Pickup, L., Zheng, P., Donglai, W., YiChang, S., Changshui, Z., Zisserman, A., Schölkopf, B., Freeman, W.

Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages: 2043-2050, IEEE, CVPR, June 2014 (conference)

DOI [BibTex]

DOI [BibTex]


Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

Hennig, P., Hauberg, S.

In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, 33, pages: 347-355, JMLR: Workshop and Conference Proceedings, (Editors: S Kaski and J Corander), Microtome Publishing, Brookline, MA, AISTATS, April 2014 (inproceedings)

Abstract
We study a probabilistic numerical method for the solution of both boundary and initial value problems that returns a joint Gaussian process posterior over the solution. Such methods have concrete value in the statistics on Riemannian manifolds, where non-analytic ordinary differential equations are involved in virtually all computations. The probabilistic formulation permits marginalising the uncertainty of the numerical solution such that statistics are less sensitive to inaccuracies. This leads to new Riemannian algorithms for mean value computations and principal geodesic analysis. Marginalisation also means results can be less precise than point estimates, enabling a noticeable speed-up over the state of the art. Our approach is an argument for a wider point that uncertainty caused by numerical calculations should be tracked throughout the pipeline of machine learning algorithms.

pdf Youtube Supplements Project page link (url) [BibTex]

pdf Youtube Supplements Project page link (url) [BibTex]


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A Visual Analytics Approach to Study Anatomic Covariation

Hermann, M., Schunke, A., Schultz, T., Klein, R.

In Proceedings of IEEE Pacific Visualization 2014, pages: 161-168, March 2014 (inproceedings)

Abstract
Gaining insight into anatomic covariation helps the understanding of organismic shape variability in general and is of particular interest for delimiting morphological modules. Generation of hypotheses on structural covariation is undoubtedly a highly creative process, and as such, requires an exploratory approach. In this work we propose a new local anatomic covariance tensor which enables interactive visualizations to explore covariation at different levels of detail, stimulating rapid formation and (qualitative) evaluation of hypotheses. The effectiveness of the presented approach is demonstrated on a muCT dataset of mouse mandibles for which results from the literature are successfully reproduced, while providing a more detailed representation of covariation compared to state-of-the-art methods.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

DOI [BibTex]


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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 , pages: 199-207, SIAM, 2014 (inproceedings)

Web PDF DOI [BibTex]

Web PDF DOI [BibTex]


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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, pages: 170-179, (Editors: E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh), AAAI Press, ICWSM, 2014 (inproceedings)

Web [BibTex]

Web [BibTex]


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Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Daneshmand, H., Gomez Rodriguez, M., Song, L., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 793-801, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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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, pages: 2831-2837, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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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, pages: 4009-4014, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Visualizing Uncertainty in HARDI Tractography Using Superquadric Streamtubes

Wiens, V., Schlaffke, L., Schmidt-Wilcke, T., Schultz, T.

In Eurographics Conference on Visualization, Short Papers, (Editors: Elmqvist, N. and Hlawitschka, M. and Kennedy, J.), EuroVis, 2014 (inproceedings)

Abstract
Standard streamtubes for the visualization of diffusion MRI data are rendered either with a circular or with an elliptic cross section whose aspect ratio indicates the relative magnitudes of the medium and minor eigenvalues. Inspired by superquadric tensor glyphs, we propose to render streamtubes with a superquadric cross section, which develops sharp edges to more clearly convey the orientation of the second and third eigenvectors where they are uniquely defined, while maintaining a circular shape when the smaller two eigenvalues are equal. As a second contribution, we apply our novel superquadric streamtubes to visualize uncertainty in the tracking direction of HARDI tractography, which we represent using a novel propagation uncertainty tensor.

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Permutation-Based Kernel Conditional Independence Test

Doran, G., Muandet, K., Zhang, K., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), pages: 132-141, (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon, UAI2014, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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A unifying view of representer theorems

Argyriou, A., Dinuzzo, F.

In Proceedings of the 31th International Conference on Machine Learning, 32, pages: 748-756, (Editors: Xing, E. P. and Jebera, T.), ICML, 2014 (inproceedings)

PDF PDF [BibTex]

PDF PDF [BibTex]


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Riemannian Sparse Coding for Positive Definite Matrices

Cherian, A., Sra, S.

In 13th European Conference on Computer Vision, LNCS 8691, pages: 299-314, (Editors: Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T.), Springer, ECCV, 2014 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Probabilistic ODE Solvers with Runge-Kutta Means

Schober, M., Duvenaud, D., Hennig, P.

In Advances in Neural Information Processing Systems 27, pages: 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 (inproceedings)

Web link (url) [BibTex]

Web link (url) [BibTex]