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2012


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Robot Learning

Sigaud, O., Peters, J.

In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

ei

Web [BibTex]

2012


Web [BibTex]


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Probabilistic Modeling of Human Movements for Intention Inference

Wang, Z., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J.

In Proceedings of Robotics: Science and Systems VIII, pages: 8, R:SS, 2012 (inproceedings)

Abstract
Inference of human intention may be an essential step towards understanding human actions [21] and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions. We train the model based on observed human behaviors/actions and we introduce an approximate inference algorithm to efficiently infer the human’s intention from an ongoing action. We verify the feasibility of the IDDM in two scenarios, i.e., target inference in robot table tennis and action recognition for interactive humanoid robots. In both tasks, the IDDM achieves substantial improvements over state-of-the-art regression and classification.

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise

Deisenroth, M., Peters, J.

In The 10th European Workshop on Reinforcement Learning (EWRL), 2012 (inproceedings)

ei

[BibTex]

[BibTex]


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On Causal and Anticausal Learning

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.

In Proceedings of the 29th International Conference on Machine Learning, pages: 1255-1262, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Juggling Increases Interhemispheric Brain Connectivity: A Visual and Quantitative dMRI Study.

Schultz, T., Gerber, P., Schmidt-Wilcke, T.

Vision, Modeling and Visualization (VMV), 2012 (poster)

ei

[BibTex]

[BibTex]


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Reinforcement Learning in Robotics: A Survey

Kober, J., Peters, J.

In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Abstract
As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Learning from distributions via support measure machines

Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 10-18, (Editors: P Bartlett, FCN Pereira, CJC. Burges, L Bottou, and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Blind Deconvolution in Scientific Imaging & Computational Photography

Hirsch, M.

Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Scalable nonconvex inexact proximal splitting

Sra, S.

In Advances of Neural Information Processing Systems 25, pages: 539-547, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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A min-cut solution to mapping phenotypes to networks of genetic markers

Azencott, C., Grimm, D., Kawahara, Y., Borgwardt, K.

In 17th Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2012 (inproceedings) Submitted

ei

[BibTex]

[BibTex]


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Efficiently mapping phenotypes to networks of genetic loci

Azencott, C., Grimm, D., Kawahara, Y., Borgwardt, K.

In NIPS Workshop on Machine Learning in Computational Biology (MLCB), 2012 (inproceedings) Submitted

ei

[BibTex]

[BibTex]


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PAC-Bayesian Analysis of Supervised, Unsupervised, and Reinforcement Learning

Seldin, Y., Laviolette, F., Shawe-Taylor, J.

Tutorial at the 29th International Conference on Machine Learning (ICML), 2012 (talk)

ei

Web Web [BibTex]

Web Web [BibTex]


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The geometry and statistics of geometric trees

Feragen, A., Lo, P., de Bruijne, M., Nielsen, M., Lauze, F.

T{\"u}bIt day of bioinformatics, June, 2012 (poster)

ei

[BibTex]

[BibTex]


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Influence of MR-based attenuation correction on lesions within bone and susceptibility artifact regions

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Pichler, B.

Molekulare Bildgebung (MoBi), 2012 (talk)

ei

[BibTex]

[BibTex]


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Modelling transition dynamics in MDPs with RKHS embeddings

Grünewälder, S., Lever, G., Baldassarre, L., Pontil, M., Gretton, A.

In Proceedings of the 29th International Conference on Machine Learning, pages: 535-542, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Clustering: Science or Art?

von Luxburg, U., Williamson, R., Guyon, I.

In JMLR Workshop and Conference Proceedings, Volume 27, pages: 65-79, Workshop on Unsupervised Learning and Transfer Learning, 2012 (inproceedings)

Abstract
We examine whether the quality of di erent clustering algorithms can be compared by a general, scienti cally sound procedure which is independent of particular clustering algorithms. We argue that the major obstacle is the diculty in evaluating a clustering algorithm without taking into account the context: why does the user cluster his data in the rst place, and what does he want to do with the clustering afterwards? We argue that clustering should not be treated as an application-independent mathematical problem, but should always be studied in the context of its end-use. Di erent techniques to evaluate clustering algorithms have to be developed for di erent uses of clustering. To simplify this procedure we argue that it will be useful to build a \taxonomy of clustering problems" to identify clustering applications which can be treated in a uni ed way and that such an e ort will be more fruitful than attempting the impossible | developing \optimal" domain-independent clustering algorithms or even classifying clustering algorithms in terms of how they work.

ei

PDF [BibTex]

PDF [BibTex]


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Reinforcement learning to adjust parametrized motor primitives to new situations

Kober, J., Wilhelm, A., Oztop, E., Peters, J.

Autonomous Robots, 33(4):361-379, 2012 (article)

Abstract
Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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A Brain-Robot Interface for Studying Motor Learning after Stroke

Meyer, T., Peters, J., Brötz, D., Zander, T., Schölkopf, B., Soekadar, S., Grosse-Wentrup, M.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 4078 - 4083 , IEEE, Piscataway, NJ, USA, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Generalization of Human Grasping for Multi-Fingered Robot Hands

Ben Amor, H., Kroemer, O., Hillenbrand, U., Neumann, G., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 2043-2050, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning Concurrent Motor Skills in Versatile Solution Spaces

Daniel, C., Neumann, G., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 3591-3597, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning to Select and Generalize Striking Movements in Robot Table Tennis

Mülling, K., Kober, J., Kroemer, O., Peters, J.

In AAAI Fall Symposium on Robots Learning Interactively from Human Teachers, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


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On the Empirical Estimation of Integral Probability Metrics

Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B., Lanckriet, G.

Electronic Journal of Statistics, 6, pages: 1550-1599, 2012 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Computational vascular morphometry for the assessment of pulmonary vascular disease based on scale-space particles

Estépar, R., Ross, J., Krissian, K., Schultz, T., Washko, G., Kindlmann, G.

In pages: 1479-1482, IEEE, 9th International Symposium on Biomedical Imaging (ISBI) , 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Structured Apprenticeship Learning

Boularias, A., Kroemer, O., Peters, J.

European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

ei

[BibTex]

[BibTex]


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Hilbert space embedding for Dirichlet Process mixtures

Muandet, K.

In NIPS Workshop on confluence between kernel methods and graphical models, 2012 (inproceedings)

ei

arXiv [BibTex]

arXiv [BibTex]


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Causal discovery with scale-mixture model for spatiotemporal variance dependencies

Chen, Z., Zhang, K., Chan, L.

In Advances in Neural Information Processing Systems 25, pages: 1736-1744, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Bridging Offline and Online Social Graph Dynamics

Gomez Rodriguez, M., Rogati, M.

In 21st ACM Conference on Information and Knowledge Management, pages: 2447-2450, (Editors: Chen, X., Lebanon, G., Wang, H. and Zaki, M. J.), ACM, CIKM, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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A new metric on the manifold of kernel matrices with application to matrix geometric means

Sra, S.

In Advances in Neural Information Processing Systems 25, pages: 144-152, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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PAC-Bayesian Analysis and Its Applications

Seldin, Y., Laviolette, F., Shawe-Taylor, J.

Tutorial at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (talk)

ei

Web [BibTex]

Web [BibTex]


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Effect of MR contrast agents on quantitative accuracy of PET in combined whole-body PET/MR imaging

Lois, C., Bezrukov, I., Schmidt, H., Schwenzer, N., Werner, M., Kupferschläger, J., Beyer, T.

European Journal of Nuclear Medicine and Molecular Imaging, 39(11):1756-1766, 2012 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Conditional mean embeddings as regressors

Grünewälder, S., Lever, G., Baldassarre, L., Patterson, S., Gretton, A., Pontil, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 1823-1830, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Multitask Learning in Computational Biology

Widmer, C., Rätsch, G.

JMLR W\&CP. ICML 2011 Unsupervised and Transfer Learning Workshop, 27, pages: 207-216, 2012 (article)

Abstract
Computational Biology provides a wide range of applications for Multitask Learning (MTL) methods. As the generation of labels often is very costly in the biomedical domain, combining data from different related problems or tasks is a promising strategy to reduce label cost. In this paper, we present two problems from sequence biology, where MTL was successfully applied. For this, we use regularization-based MTL methods, with a special focus on the case of a hierarchical relationship between tasks. Furthermore, we propose strategies to refine the measure of task relatedness, which is of central importance in MTL and finally give some practical guidelines, when MTL strategies are likely to pay off.

ei

PDF [BibTex]

PDF [BibTex]


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Machine Learning and Interpretation in Neuroimaging - Revised Selected and Invited Contributions

Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B.

pages: 266, Springer, Heidelberg, Germany, International Workshop, MLINI, Held at NIPS, 2012, Lecture Notes in Computer Science, Vol. 7263 (proceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Therapy monitoring of patients with chronic sclerodermic graft-versus-host-disease using PET/MRI

Sauter, A., Schmidt, H., Mantlik, F., Kolb, A., Federmann, B., Bethge, W., Reimold, M., Pfannenberg, C., Pichler, B., Horger, M.

2012 SNM Annual Meeting, 2012 (poster)

ei

Web [BibTex]

Web [BibTex]


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Shortest path distance in random k-nearest neighbor graphs

Alamgir, M., von Luxburg, U.

In Proceedings of the 29th International Conference on Machine Learning, International Machine Learning Society, International Conference on Machine Learning (ICML), 2012 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Toward Fast Policy Search for Learning Legged Locomotion

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

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 1787-1792, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Robot Skill Learning

Peters, J., Kober, J., Mülling, K., Nguyen-Tuong, D., Kroemer, O.

In 20th European Conference on Artificial Intelligence , pages: 40-45, ECAI, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Centrality of the Mammalian Functional Brain Network

Besserve, M., Bartels, A., Murayama, Y., Logothetis, N.

42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (poster)

ei

[BibTex]

[BibTex]


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Towards a learning-theoretic analysis of spike-timing dependent plasticity

Balduzzi, D., Besserve, M.

In Advances in Neural Information Processing Systems 25, pages: 2465-2473, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Kernel Mean Embeddings of POMDPs

Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K.

21st Machine Learning Summer School , 2012 (poster)

ei

[BibTex]

[BibTex]


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Kernel Bellman Equations in POMDPs

Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K.

Technical Committee on Infomation-Based Induction Sciences and Machine Learning (IBISML'12), 2012 (talk)

ei

[BibTex]

[BibTex]


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Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database

Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., Harmeling, S.

In Computer Vision - ECCV 2012, LNCS Vol. 7578, pages: 27-40, (Editors: A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid), Springer, Berlin, Germany, 12th European Conference on Computer Vision, ECCV , 2012 (inproceedings)

Abstract
Motion blur due to camera shake is one of the predominant sources of degradation in handheld photography. Single image blind deconvolution (BD) or motion deblurring aims at restoring a sharp latent image from the blurred recorded picture without knowing the camera motion that took place during the exposure. BD is a long-standing problem, but has attracted much attention recently, cumulating in several algorithms able to restore photos degraded by real camera motion in high quality. In this paper, we present a benchmark dataset for motion deblurring that allows quantitative performance evaluation and comparison of recent approaches featuring non-uniform blur models. To this end, we record and analyse real camera motion, which is played back on a robot platform such that we can record a sequence of sharp images sampling the six dimensional camera motion trajectory. The goal of deblurring is to recover one of these sharp images, and our dataset contains all information to assess how closely various algorithms approximate that goal. In a comprehensive comparison, we evaluate state-of-the-art single image BD algorithms incorporating uniform and non-uniform blur models.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Higher-Order Tensors in Diffusion MRI

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, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

ei

[BibTex]

[BibTex]


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MICCAI, Workshop on Computational Diffusion MRI, 2012 (electronic publication)

Panagiotaki, E., O’Donnell, L., Schultz, T., Zhang, G.

15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI , 2012 (proceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis

Windram, O., Madhou, P., McHattie, S., Hill, C., Hickman, R., Cooke, E., Jenkins, DJ., Penfold, CA., Baxter, Ll., Breeze, E., Kiddle, SJ., Rhodes, J., Atwell, S., Kliebenstein, D., Kim, Y-S., Stegle, O., Borgwardt, KM., others

The Plant Cell Online, 24(9):3530-3557, 2012, all authors: Oliver Windram,Priyadharshini Madhou,Stuart McHattie,Claire Hill,Richard Hickman,Emma Cooke,Dafyd J. Jenkins,Christopher A. Penfold,Laura Baxter,Emily Breeze,Steven J. Kiddle,Johanna Rhodes,Susanna Atwell,Daniel J. (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Improved Linear Mixed Models for Genome-Wide Association Studies

Listgarten, J., Lippert, C., Kadie, CM., Davidson, RI., Eskin, E., Heckerman, D.

Nature Methods, 9, pages: 525–526, 2012 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Calibration of Boltzmann distribution priors in Bayesian data analysis

Mechelke, M., Habeck, M.

Physical Review E, 86(6):066705, 2012 (article)

ei

DOI [BibTex]

DOI [BibTex]