Header logo is


2012


no image
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]

2012


PDF [BibTex]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
Structured Apprenticeship Learning

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

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

ei

[BibTex]

[BibTex]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
Towards identifying and validating cognitive correlates in a passive Brain-Computer Interface for detecting Loss of Control

Zander, TO., Pape, AA.

In Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, EMBC, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Neural correlates of workload and puzzlement during loss of control

Pape, AA., Gerjets, P., Zander, TO.

In Meeting of the EARLI SIG 22 Neuroscience and Education, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Hypothesis testing using pairwise distances and associated kernels

Sejdinovic, D., Gretton, A., Sriperumbudur, B., Fukumizu, K.

In Proceedings of the 29th International Conference on Machine Learning, pages: 1111-1118, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Efficient Training of Graph-Regularized Multitask SVMs

Widmer, C., Kloft, M., Görnitz, N., Rätsch, G.

In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML/PKDD 2012, LNCS Vol. 7523, pages: 633-647, (Editors: PA Flach and T De Bie and N Cristianini), Springer, Berlin, Germany, ECML, 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Hilbert Space Embeddings of POMDPs

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

In Conference on Uncertainty in Artificial Intelligence (UAI), 2012 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Learning Throwing and Catching Skills

Kober, J., Mülling, K., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 5167-5168, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Maximally Informative Interaction Learning for Scene Exploration

van Hoof, H., Kroemer, O., Ben Amor, H., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 5152-5158, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Beta oscillations propagate as traveling waves in the macaque prefrontal cortex

Panagiotaropoulos, T., Besserve, M., Logothetis, N.

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

ei

[BibTex]

[BibTex]


no image
Investigating the Neural Basis of Brain-Computer Interface (BCI)-based Stroke Rehabilitation

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

In International Conference on NeuroRehabilitation (ICNR) , pages: 617-621, (Editors: JL Pons, D Torricelli, and M Pajaro), Springer, Berlin, Germany, ICNR, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function

Ortega, P., Grau-Moya, J., Genewein, T., Balduzzi, D., Braun, D.

In Advances in Neural Information Processing Systems 25, pages: 3014-3022, (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]


no image
Algorithms for Learning Markov Field Policies

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

In Advances in Neural Information Processing Systems 25, pages: 2186-2194, (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]


no image
Semi-Supervised Domain Adaptation with Copulas

Lopez-Paz, D., Hernandez-Lobato, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 674-682, (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]


no image
Gradient Weights help Nonparametric Regressors

Kpotufe, S., Boularias, A.

In Advances in Neural Information Processing Systems 25, pages: 2870-2878, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
A Blind Deconvolution Approach for Pseudo CT Prediction from MR Image Pairs

Hirsch, M., Hofmann, M., Mantlik, F., Pichler, B., Schölkopf, B., Habeck, M.

In 19th IEEE International Conference on Image Processing (ICIP) , pages: 2953 -2956, IEEE, ICIP, 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
A mixed model approach for joint genetic analysis of alternatively spliced transcript isoforms using RNA-Seq data

Rakitsch, B., Lippert, C., Topa, H., Borgwardt, KM., Honkela, A., Stegle, O.

In 2012 (inproceedings) Submitted

ei

Web [BibTex]

Web [BibTex]


no image
Evaluation of marginal likelihoods via the density of states

Habeck, M.

In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012) , 22, pages: 486-494, (Editors: N Lawrence and M Girolami), JMLR: W&CP 22, AISTATS, 2012 (inproceedings)

Abstract
Bayesian model comparison involves the evaluation of the marginal likelihood, the expectation of the likelihood under the prior distribution. Typically, this high-dimensional integral over all model parameters is approximated using Markov chain Monte Carlo methods. Thermodynamic integration is a popular method to estimate the marginal likelihood by using samples from annealed posteriors. Here we show that there exists a robust and flexible alternative. The new method estimates the density of states, which counts the number of states associated with a particular value of the likelihood. If the density of states is known, computation of the marginal likelihood reduces to a one- dimensional integral. We outline a maximum likelihood procedure to estimate the density of states from annealed posterior samples. We apply our method to various likelihoods and show that it is superior to thermodynamic integration in that it is more flexible with regard to the annealing schedule and the family of bridging distributions. Finally, we discuss the relation of our method with Skilling's nested sampling.

ei

PDF [BibTex]

PDF [BibTex]


no image
Distributed multisensory signals acquisition and analysis in dyadic interactions

Tawari, A., Tran, C., Doshi, A., Zander, TO.

In Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems Extended Abstracts, pages: 2261-2266, (Editors: JA Konstan and EH Chi and K Höök), ACM, New York, NY, USA, CHI, 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Measuring Cognitive Load by means of EEG-data - how detailed is the picture we can get?

Scharinger, C., Cierniak, G., Walter, C., Zander, TO., Gerjets, P.

In Meeting of the EARLI SIG 22 Neuroscience and Education, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Optimal kernel choice for large-scale two-sample tests

Gretton, A., Sriperumbudur, B., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K.

In Advances in Neural Information Processing Systems 25, pages: 1214-1222, (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]


no image
Inverse dynamics with optimal distribution of contact forces for the control of legged robots

Righetti, L., Schaal, S.

In Dynamic Walking 2012, Pensacola, 2012 (inproceedings)

am

[BibTex]

[BibTex]


no image
Topological optimization for continuum compliant mechanisms via morphological evolution of traditional mechanisms

Lum, GZ, Yeo, SH, Yang, GL, Teo, TJ, Sitti, M

In 4th International Conference on Computational Methods, pages: 8, 2012 (inproceedings)

pi

[BibTex]

[BibTex]


Thumb xl facialfeature
Real-time Facial Feature Detection using Conditional Regression Forests

Dantone, M., Gall, J., Fanelli, G., van Gool, L.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2578-2585, IEEE, Providence, RI, USA, 2012 (inproceedings)

ps

code pdf Project Page [BibTex]

code pdf Project Page [BibTex]


Thumb xl lht
Latent Hough Transform for Object Detection

Razavi, N., Gall, J., Kohli, P., van Gool, L.

In European Conference on Computer Vision (ECCV), 7574, pages: 312-325, LNCS, Springer, 2012 (inproceedings)

ps

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Thumb xl destflow
Destination Flow for Crowd Simulation

Pellegrini, S., Gall, J., Sigal, L., van Gool, L.

In Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, 7585, pages: 162-171, LNCS, Springer, 2012 (inproceedings)

ps

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Thumb xl soumyanips
From Deformations to Parts: Motion-based Segmentation of 3D Objects

Ghosh, S., Sudderth, E., Loper, M., Black, M.

In Advances in Neural Information Processing Systems 25 (NIPS), pages: 2006-2014, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

Abstract
We develop a method for discovering the parts of an articulated object from aligned meshes of the object in various three-dimensional poses. We adapt the distance dependent Chinese restaurant process (ddCRP) to allow nonparametric discovery of a potentially unbounded number of parts, while simultaneously guaranteeing a spatially connected segmentation. To allow analysis of datasets in which object instances have varying 3D shapes, we model part variability across poses via affine transformations. By placing a matrix normal-inverse-Wishart prior on these affine transformations, we develop a ddCRP Gibbs sampler which tractably marginalizes over transformation uncertainty. Analyzing a dataset of humans captured in dozens of poses, we infer parts which provide quantitatively better deformation predictions than conventional clustering methods.

ps

pdf supplemental code poster link (url) Project Page [BibTex]

pdf supplemental code poster link (url) Project Page [BibTex]


no image
Encoding of Periodic and their Transient Motions by a Single Dynamic Movement Primitive

Ernesti, J., Righetti, L., Do, M., Asfour, T., Schaal, S.

In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pages: 57-64, IEEE, Osaka, Japan, November 2012 (inproceedings)

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Adaptive Coding of Actions and Observations

Ortega, PA, Braun, DA

pages: 1-4, NIPS Workshop on Information in Perception and Action, December 2012 (conference)

Abstract
The application of expected utility theory to construct adaptive agents is both computationally intractable and statistically questionable. To overcome these difficulties, agents need the ability to delay the choice of the optimal policy to a later stage when they have learned more about the environment. How should agents do this optimally? An information-theoretic answer to this question is given by the Bayesian control rule—the solution to the adaptive coding problem when there are not only observations but also actions. This paper reviews the central ideas behind the Bayesian control rule.

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Learning Force Control Policies for Compliant Robotic Manipulation

Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S.

In ICML’12 Proceedings of the 29th International Coference on International Conference on Machine Learning, pages: 49-50, Edinburgh, Scotland, 2012 (inproceedings)

am mg

[BibTex]

[BibTex]


no image
Free Energy and the Generalized Optimality Equations for Sequential Decision Making

Ortega, PA, Braun, DA

pages: 1-10, 10th European Workshop on Reinforcement Learning (EWRL), July 2012 (conference)

Abstract
The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be axiomatically derived. Here we apply the free energy principle to general decision trees that include both adversarial and stochastic environments. We derive generalized sequential optimality equations that not only include the Bellman optimality equations as a limit case, but also lead to well-known decision-rules such as Expectimax, Minimax and Expectiminimax. We show how these decision-rules can be derived from a single free energy principle that assigns a resource parameter to each node in the decision tree. These resource parameters express a concrete computational cost that can be measured as the amount of samples that are needed from the distribution that belongs to each node. The free energy principle therefore provides the normative basis for generalized optimality equations that account for both adversarial and stochastic environments.

ei

link (url) [BibTex]

link (url) [BibTex]