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2013


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Autonomous Reinforcement Learning with Hierarchical REPS

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

In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), pages: 1-8, 2013 (inproceedings)

DOI [BibTex]

2013

DOI [BibTex]


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Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry

Feragen, A., Petersen, J., Grimm, D., Dirksen, A., Pedersen, JH., Borgwardt, KM., de Bruijne, M.

In Information Processing in Medical Imaging, pages: 171-183, (Editors: JC Gee and S Joshi and KM Pohl and WM Wells and L Zöllei), Springer, Berlin Heidelberg, 23rd International Conference on Information Processing in Medical Imaging (IPMI), 2013, Lecture Notes in Computer Science, Vol. 7017 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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On estimation of functional causal models: Post-nonlinear causal model as an example

Zhang, K., Wang, Z., Schölkopf, B.

In First IEEE ICDM workshop on causal discovery , 2013, Held in conjunction with the 12th IEEE International Conference on Data Mining (ICDM 2013) (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Object Modeling and Segmentation by Robot Interaction with Cluttered Environments

van Hoof, H., Krömer, O., Peters, J.

In Proceedings of the IEEE International Conference on Humanoid Robots (HUMANOIDS), pages: 169-176, IEEE, 13th IEEE-RAS International Conference on Humanoid Robots, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Reflection methods for user-friendly submodular optimization

Jegelka, S., Bach, F., Sra, S.

In Advances in Neural Information Processing Systems 26, pages: 1313-1321, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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On Flat versus Hierarchical Classification in Large-Scale Taxonomies

Babbar, R., Partalas, I., Gaussier, E., Amini, M.

In Advances in Neural Information Processing Systems 26, pages: 1824-1832, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Data-Efficient Generalization of Robot Skills with Contextual Policy Search

Kupcsik, A., Deisenroth, M., Peters, J., Neumann, G.

In Proceedings of the 27th National Conference on Artificial Intelligence (AAAI 2013), (Editors: desJardins, M. and Littman, M. L.), AAAI Press, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

In Proceedings 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 449-458, (Editors: Ann Nicholson and Padhraic Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Modeling Information Propagation with Survival Theory

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28 (3), pages: 666-674, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

Web [BibTex]

Web [BibTex]


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How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data

Grosse-Wentrup, M., Harmeling, S., Zander, T., Hill, J., Schölkopf, B.

In Proceedings of the 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI), pages: 102-105, IEEE Xplore Digital Library, PRNI, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

Sgouritsa, E., Janzing, D., Peters, J., Schölkopf, B.

In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 556-565, (Editors: A Nicholson and P Smyth), AUAI Press Corvallis, Oregon, USA, UAI, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Improving alpha matting and motion blurred foreground estimation

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

In IEEE Conference on Image Processing (ICIP), pages: 3446-3450, IEEE, ICIP, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Towards Robot Skill Learning: From Simple Skills to Table Tennis

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

In Machine Learning and Knowledge Discovery in Databases, Proceedings of the European Conference on Machine Learning, Part III (ECML 2013), LNCS 8190, pages: 627-631, (Editors: Blockeel, H.,Kersting, K., Nijssen, S., and Zelezný, F.), Springer, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Nonparametric dynamics estimation for time periodic systems

Klenske, E., Zeilinger, M., Schölkopf, B., Hennig, P.

In Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing, pages: 486-493 , 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Scalable kernels for graphs with continuous attributes

Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., Borgwardt, KM.

In Advances in Neural Information Processing Systems 26, pages: 216-224, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Auto-Calibrating Spherical Deconvolution Based on ODF Sparsity

Schultz, T., Gröschel, S.

In Proceedings of Medical Image Computing and Computer-Assisted Intervention, Part I, pages: 663-670, (Editors: K Mori and I Sakuma and Y Sato and C Barillot and N Navab), Springer, MICCAI, 2013, Lecture Notes in Computer Science, vol. 8149 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Maximum-Margin Framework for Training Data Synchronization in Large-Scale Hierarchical Classification

Babbar, R., Partalas, I., Gaussier, E., Amini, M.

In Neural Information Processing - 20th International Conference, Proceedings, Part I, Lecture Notes in Computer Science, Vol. 8226, pages: 336-343, (Editors: M Lee and A Hirose and Z-G Hou and R M Kil), Springer, ICONIP, 2013 (inproceedings)

Web [BibTex]

Web [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Volume 28, number 1 (inproceedings)

Web [BibTex]

Web [BibTex]


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Learning Sequential Motor Tasks

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

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Information-Theoretic Motor Skill Learning

Neumann, G., Kupcsik, A., Deisenroth, M., Peters, J.

In Proceedings of the 27th AAAI 2013, Workshop on Intelligent Robotic Systems (AAAI 2013), 2013 (inproceedings)

[BibTex]

[BibTex]


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Measuring Statistical Dependence via the Mutual Information Dimension

Sugiyama, M., Borgwardt, KM.

In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), pages: 1692-1698, (Editors: Francesca Rossi), AAAI Press, Menlo Park, California, IJCAI, 2013 (inproceedings)

[BibTex]

[BibTex]


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Analytical probabilistic proton dose calculation and range uncertainties

Bangert, M., Hennig, P., Oelfke, U.

In 17th International Conference on the Use of Computers in Radiation Therapy, pages: 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 (inproceedings)

[BibTex]

[BibTex]


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Adaptivity to Local Smoothness and Dimension in Kernel Regression

Kpotufe, S., Garg, V.

In Advances in Neural Information Processing Systems 26, pages: 3075-3083, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators

Besserve, M., Logothetis, N., Schölkopf, B.

In Advances in Neural Information Processing Systems 26, pages: 2535-2543, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals

Rakitsch, B., Lippert, C., Borgwardt, KM., Stegle, O.

In Advances in Neural Information Processing Systems 26, pages: 1466-1474, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Comparative Classifier Evaluation for Web-Scale Taxonomies Using Power Law

Babbar, R., Partalas, I., Metzig, C., Gaussier, E., Amini, M.

In The Semantic Web: ESWC 2013 Satellite Events, Lecture Notes in Computer Science, Vol. 7955 , pages: 310-311, (Editors: P Cimiano and M Fernández and V Lopez and S Schlobach and J Völker), Springer, ESWC, 2013 (inproceedings)

Web [BibTex]

Web [BibTex]


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Model-based Imitation Learning by Probabilistic Trajectory Matching

Englert, P., Paraschos, A., Peters, J., Deisenroth, M.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), pages: 1922-1927, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Towards neurofeedback for improving visual attention

Zander, T., Battes, B., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, pages: Article ID: 086, (Editors: J.d.R. Millán, S. Gao, R. Müller-Putz, J.R. Wolpaw, and J.E. Huggins), Verlag der Technischen Universität Graz, 5th International Brain-Computer Interface Meeting, 2013, Article ID: 086 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions

Jung, M., Zscheischler, J.

In Proceedings of the International Conference on Computational Science, 18, pages: 2337 - 2346, Procedia Computer Science, (Editors: Alexandrov, V and Lees, M and Krzhizhanovskaya, V and Dongarra, J and Sloot, PMA), Elsevier, Amsterdam, Netherlands, ICCS, 2013 (inproceedings)

Web DOI [BibTex]

Web DOI [BibTex]


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Learning responsive robot behavior by imitation

Ben Amor, H., Vogt, D., Ewerton, M., Berger, E., Jung, B., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pages: 3257-3264, IEEE, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Learning Skills with Motor Primitives

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

In Proceedings of the 16th Yale Workshop on Adaptive and Learning Systems, 2013 (inproceedings)

[BibTex]

[BibTex]


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Scalable Influence Estimation in Continuous-Time Diffusion Networks

Du, N., Song, L., Gomez Rodriguez, M., Zha, H.

In Advances in Neural Information Processing Systems 26, pages: 3147-3155, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF PDF [BibTex]

PDF PDF [BibTex]


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Rapid Distance-Based Outlier Detection via Sampling

Sugiyama, M., Borgwardt, KM.

In Advances in Neural Information Processing Systems 26, pages: 467-475, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Probabilistic Movement Primitives

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

In Advances in Neural Information Processing Systems 26, pages: 2616-2624, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF PDF [BibTex]

PDF PDF [BibTex]


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Causal Inference on Time Series using Restricted Structural Equation Models

Peters, J., Janzing, D., Schölkopf, B.

In Advances in Neural Information Processing Systems 26, pages: 154-162, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Regression-tree Tuning in a Streaming Setting

Kpotufe, S., Orabona, F.

In Advances in Neural Information Processing Systems 26, pages: 1788-1796, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Density estimation from unweighted k-nearest neighbor graphs: a roadmap

von Luxburg, U., Alamgir, M.

In Advances in Neural Information Processing Systems 26, pages: 225-233, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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PAC-Bayes-Empirical-Bernstein Inequality

Tolstikhin, I. O., Seldin, Y.

In Advances in Neural Information Processing Systems 26, pages: 109-117, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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PLAL: Cluster-based active learning

Urner, R., Wulff, S., Ben-David, S.

In Proceedings of the 26th Annual Conference on Learning Theory, 30, pages: 376-397, (Editors: Shalev-Shwartz, S. and Steinwart, I.), JMLR, COLT, 2013 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Monochromatic Bi-Clustering

Wulff, S., Urner, R., Ben-David, S.

In Proceedings of the 30th International Conference on Machine Learning, 28, pages: 145-153, (Editors: Dasgupta, S. and McAllester, D.), JMLR, ICML, 2013 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Significance of variable height-bandwidth group delay filters in the spectral reconstruction of speech

Devanshu, A., Raj, A., Hegde, R. M.

INTERSPEECH 2013, 14th Annual Conference of the International Speech Communication Association, pages: 1682-1686, 2013 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Generative Multiple-Instance Learning Models For Quantitative Electromyography

Adel, T., Smith, B., Urner, R., Stashuk, D., Lizotte, D. J.

In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, AUAI Press, UAI, 2013 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Automatic Malaria Diagnosis system

Mehrjou, A., Abbasian, T., Izadi, M.

In First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pages: 205-211, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Abstraction in Decision-Makers with Limited Information Processing Capabilities

Genewein, T, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artificial systems abstractions are often implemented through computationally costly formations of groups or clusters. In this work we establish the relation between the free-energy framework for decision-making and rate-distortion theory and demonstrate how the application of rate-distortion for decision-making leads to the emergence of abstractions. We argue that abstractions are induced due to a limit in information processing capacity.

link (url) [BibTex]

link (url) [BibTex]


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Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.

link (url) [BibTex]

link (url) [BibTex]

2006


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Global Biclustering of Microarray Data

Wolf, T., Brors, B., Hofmann, T., Georgii, E.

In ICDMW 2006, pages: 125-129, (Editors: Tsumoto, S. , C. W. Clifton, N. Zhong, X. Wu, J. Liu, B. W. Wah, Y.-M. Cheung), IEEE Computer Society, Los Alamitos, CA, USA, Sixth IEEE International Conference on Data Mining, December 2006 (inproceedings)

Abstract
We consider the problem of simultaneously clustering genes and conditions of a gene expression data matrix. A bicluster is defined as a subset of genes that show similar behavior within a subset of conditions. Finding biclusters can be useful for revealing groups of genes involved in the same molecular process as well as groups of conditions where this process takes place. Previous work either deals with local, bicluster-based criteria or assumes a very specific structure of the data matrix (e.g. checkerboard or block-diagonal) [11]. In contrast, our goal is to find a set of flexibly arranged biclusters which is optimal in regard to a global objective function. As this is a NP-hard combinatorial problem, we describe several techniques to obtain approximate solutions. We benchmarked our approach successfully on the Alizadeh B-cell lymphoma data set [1].

Web DOI [BibTex]

2006

Web DOI [BibTex]


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Conformal Multi-Instance Kernels

Blaschko, M., Hofmann, T.

In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-6, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

Abstract
In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in the bag indicate class membership while ignoring vectors that do not. We describe here a kernel-based technique that defines a parametric family of kernels via conformal transformations and jointly learns a discriminant function over bags together with the optimal parameter settings of the kernel. Learning a conformal transformation effectively amounts to weighting regions in the feature space according to their contribution to classification accuracy; regions that are discriminative will be weighted higher than regions that are not. This allows the classifier to focus on regions contributing to classification accuracy while ignoring regions that correspond to vectors found both in positive and in negative bags. We show how parameters of this transformation can be learned for support vector machines by posing the problem as a multiple kernel learning problem. The resulting multiple instance classifier gives competitive accuracy for several multi-instance benchmark datasets from different domains.

PDF Web [BibTex]

PDF Web [BibTex]


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Information-theoretic Metric Learning

Davis, J., Kulis, B., Sra, S., Dhillon, I.

In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-5, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

Abstract
We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a low-rank kernel learning problem. Specifically, we minimize the Burg divergence of a low-rank kernel to an input kernel, subject to pairwise distance constraints. Our approach has several advantages over existing methods. First, we present a natural information-theoretic formulation for the problem. Second, the algorithm utilizes the methods developed by Kulis et al. [6], which do not involve any eigenvector computation; in particular, the running time of our method is faster than most existing techniques. Third, the formulation offers insights into connections between metric learning and kernel learning.

PDF Web [BibTex]

PDF Web [BibTex]


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Pattern Mining in Frequent Dynamic Subgraphs

Borgwardt, KM., Kriegel, H-P., Wackersreuther, P.

In pages: 818-822, (Editors: Clifton, C.W.), IEEE Computer Society, Los Alamitos, CA, USA, Sixth International Conference on Data Mining (ICDM), December 2006 (inproceedings)

Abstract
Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowledge. While current data mining deals with static graphs that do not change over time, coming years will see the advent of an increasing number of time series of graphs. In this article, we investigate how pattern mining on static graphs can be extended to time series of graphs. In particular, we are considering dynamic graphs with edge insertions and edge deletions over time. We define frequency in this setting and provide algorithmic solutions for finding frequent dynamic subgraph patterns. Existing subgraph mining algorithms can be easily integrated into our framework to make them handle dynamic graphs. Experimental results on real-world data confirm the practical feasibility of our approach.

Web DOI [BibTex]

Web DOI [BibTex]


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Acquiring web page information without commitment to downloading the web page

Heilbron, L., Platt, J. C., Schölkopf, B., Simard, P. Y.

United States Patent, No 7155489, December 2006 (patent)

[BibTex]

[BibTex]