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2010


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A Nearest Neighbor Data Structure for Graphics Hardware

Cayton, L.

In Proceedings of the First International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (ADMS 2010), pages: 1-6, First International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (ADMS), September 2010 (inproceedings)

Abstract
Nearest neighbor search is a core computational task in database systems and throughout data analysis. It is also a major computational bottleneck, and hence an enormous body of research has been devoted to data structures and algorithms for accelerating the task. Recent advances in graphics hardware provide tantalizing speedups on a variety of tasks and suggest an alternate approach to the problem: simply run brute force search on a massively parallel sys- tem. In this paper we marry the approaches with a novel data structure that can effectively make use of parallel systems such as graphics cards. The architectural complexities of graphics hardware - the high degree of parallelism, the small amount of memory relative to instruction throughput, and the single instruction, multiple data design- present significant challenges for data structure design. Furthermore, the brute force approach applies perfectly to graphics hardware, leading one to question whether an intelligent algorithm or data structure can even hope to outperform this basic approach. Despite these challenges and misgivings, we demonstrate that our data structure - termed a Random Ball Cover - provides significant speedups over the GPU- based brute force approach.

PDF Web [BibTex]

2010

PDF Web [BibTex]


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Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis

Gomez Rodriguez, M., Grosse-Wentrup, M., Peters, J., Naros, G., Hill, J., Schölkopf, B., Gharabaghi, A.

In Proceedings of the 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (ICPR WBD 2010), pages: 36-39, (Editors: J. Richiardi and D Van De Ville and C Davatzikos and J Mourao-Miranda), IEEE, Piscataway, NJ, USA, 1st Workshop on Brain Decoding (WBD), August 2010 (inproceedings)

Abstract
Brain-Computer Interfaces (BCI) that rely upon epidural electrocorticographic signals may become a promising tool for neurorehabilitation of patients with severe hemiparatic syndromes due to cerebrovascular, traumatic or tumor-related brain damage. Here, we show in a patient-based feasibility study that online classification of arm movement intention is possible. The intention to move or to rest can be identified with high accuracy (~90 %), which is sufficient for BCI-guided neurorehabilitation. The observed spatial distribution of relevant features on the motor cortex indicates that cortical reorganization has been induced by the brain lesion. Low- and high-frequency components of the electrocorticographic power spectrum provide complementary information towards classification of arm movement intention.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Simulating Human Table Tennis with a Biomimetic Robot Setup

Mülling, K., Kober, J., Peters, J.

In From Animals to Animats 11, pages: 273-282, (Editors: Doncieux, S. , B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, J.-B. Mouret), Springer, Berlin, Germany, 11th International Conference on Simulation of Adaptive Behavior (SAB), August 2010 (inproceedings)

Abstract
Playing table tennis is a difficult motor task which requires fast movements, accurate control and adaptation to task parameters. Although human beings see and move slower than most robot systems they outperform all table tennis robots significantly. In this paper we study human table tennis and present a robot system that mimics human striking behavior. Therefore we model the human movements involved in hitting a table tennis ball using discrete movement stages and the virtual hitting point hypothesis. The resulting model is implemented on an anthropomorphic robot arm with 7 degrees of freedom using robotics methods. We verify the functionality of the model both in a physical realistic simulation of an anthropomorphic robot arm and on a real Barrett WAM.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Adapting Preshaped Grasping Movements Using Vision Descriptors

Kroemer, O., Detry, R., Piater, J., Peters, J.

In From Animals to Animats 11, pages: 156-166, (Editors: Doncieux, S. , B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, J.-B. Mouret), Springer, Berlin, Germany, 11th International Conference on Simulation of Adaptive Behavior (SAB), August 2010 (inproceedings)

Abstract
Grasping is one of the most important abilities needed for future service robots. In the task of picking up an object from between clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which are often not available. Therefore, methods are needed that execute grasps robustly even with imprecise information gathered only from standard stereo vision. We propose techniques that reactively modify the robot‘s learned motor primitives based on non-parametric potential fields centered on the Early Cognitive Vision descriptors. These allow both obstacle avoidance, and the adapting of finger motions to the object‘s local geometry. The methods were tested on a real robot, where they led to improved adaptability and quality of grasping actions.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Inferring Networks of Diffusion and Influence

Gomez Rodriguez, M., Leskovec, J., Krause, A.

In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010), pages: 1019-1028, (Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang), ACM Press, New York, NY, USA, 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), July 2010 (inproceedings)

Abstract
Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Relative Entropy Policy Search

Peters, J., Mülling, K., Altun, Y.

In Proceedings of the Twenty-Fourth National Conference on Artificial Intelligence, pages: 1607-1612, (Editors: Fox, M. , D. Poole), AAAI Press, Menlo Park, CA, USA, Twenty-Fourth National Conference on Artificial Intelligence (AAAI-10), July 2010 (inproceedings)

Abstract
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients (Bagnell and Schneider 2003), many of these problems may be addressed by constraining the information loss. In this paper, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It works well on typical reinforcement learning benchmark problems.

PDF Web [BibTex]

PDF Web [BibTex]


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Predictive Representations For Sequential Decision Making Under Uncertainty

Boularias, A.

Université Laval, Quebec, Canada, July 2010 (phdthesis)

Abstract
The problem of making decisions is ubiquitous in life. This problem becomes even more complex when the decisions should be made sequentially. In fact, the execution of an action at a given time leads to a change in the environment of the problem, and this change cannot be predicted with certainty. The aim of a decision-making process is to optimally select actions in an uncertain environment. To this end, the environment is often modeled as a dynamical system with multiple states, and the actions are executed so that the system evolves toward a desirable state. In this thesis, we proposed a family of stochastic models and algorithms in order to improve the quality of of the decision-making process. The proposed models are alternative to Markov Decision Processes, a largely used framework for this type of problems. In particular, we showed that the state of a dynamical system can be represented more compactly if it is described in terms of predictions of certain future events. We also showed that even the cognitive process of selecting actions, known as policy, can be seen as a dynamical system. Starting from this observation, we proposed a panoply of algorithms, all based on predictive policy representations, in order to solve different problems of decision-making, such as decentralized planning, reinforcement learning, or imitation learning. We also analytically and empirically demonstrated that the proposed approaches lead to a decrease in the computational complexity and an increase in the quality of the decisions, compared to standard approaches for planning and learning under uncertainty.

PDF [BibTex]


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Inferring deterministic causal relations

Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., Schölkopf, B.

In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages: 143-150, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

Abstract
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

PDF Web [BibTex]

PDF Web [BibTex]


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Reinforcement Learning by Relative Entropy Policy Search

Peters, J., Mülling, K., Altun, Y.

30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), 30, pages: 69, July 2010 (poster)

Abstract
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients, many of these problems may be addressed by constraining the information loss. In this book chapter, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It works well on typical reinforcement learning benchmark problems. We will also present a real-world applications where a robot employs REPS to learn how to return balls in a game of table tennis.

PDF [BibTex]

PDF [BibTex]


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Recent trends in classification of remote sensing data: active and semisupervised machine learning paradigms

Bruzzone, L., Persello, C.

In pages: 3720-3723 , IEEE, Piscataway, NJ, USA, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2010 (inproceedings)

Abstract
This paper addresses the recent trends in machine learning methods for the automatic classification of remote sensing (RS) images. In particular, we focus on two new paradigms: semisupervised and active learning. These two paradigms allow one to address classification problems in the critical conditions where the available labeled training samples are limited. These operational conditions are very usual in RS problems, due to the high cost and time associated with the collection of labeled samples. Semisupervised and active learning techniques allow one to enrich the initial training set information and to improve classification accuracy by exploiting unlabeled samples or requiring additional labeling phases from the user, respectively. The two aforementioned strategies are theoretically and experimentally analyzed considering SVM-based techniques in order to highlight advantages and disadvantages of both strategies.

Web DOI [BibTex]

Web DOI [BibTex]


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Source Separation and Higher-Order Causal Analysis of MEG and EEG

Zhang, K., Hyvärinen, A.

In Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Sixth Conference (UAI 2010), pages: 709-716, (Editors: Grünwald, P. , P. Spirtes), AUAI Press, Corvallis, OR, USA, 26th Conference on Uncertainty in Artificial Intelligence (UAI), July 2010 (inproceedings)

Abstract
Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a twolayer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two steps. We first propose a new source separation technique which takes into account the autocorrelations (which may be time-varying) and time-varying variances of the sources. The causality in the envelopes is then discovered by exploiting a special kind of multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model. The resulting causal diagram gives the effective connectivity between the separated sources; in our experimental results on MEG data, sources with similar functions are grouped together, with negative influences between groups, and the groups are connected via some interesting sources.

PDF Web [BibTex]

PDF Web [BibTex]


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Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery

Zhang, K., Schölkopf, B., Janzing, D.

In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages: 717-724, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

Abstract
In nonlinear latent variable models or dynamic models, if we consider the latent variables as confounders (common causes), the noise dependencies imply further relations between the observed variables. Such models are then closely related to causal discovery in the presence of nonlinear confounders, which is a challenging problem. However, generally in such models the observation noise is assumed to be independent across data dimensions, and consequently the noise dependencies are ignored. In this paper we focus on the Gaussian process latent variable model (GPLVM), from which we develop an extended model called invariant GPLVM (IGPLVM), which can adapt to arbitrary noise covariances. With the Gaussian process prior put on a particular transformation of the latent nonlinear functions, instead of the original ones, the algorithm for IGPLVM involves almost the same computational loads as that for the original GPLVM. Besides its potential application in causal discovery, IGPLVM has the advantage that its estimated latent nonlinear manifold is invariant to any nonsingular linear transformation of the data. Experimental results on both synthetic and realworld data show its encouraging performance in nonlinear manifold learning and causal discovery.

PDF Web [BibTex]

PDF Web [BibTex]


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Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

PDF [BibTex]

PDF [BibTex]


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Multi-Label Learning by Exploiting Label Dependency

Zhang, M., Zhang, K.

In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), pages: 999-1008, (Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang), ACM Press, New York, NY, USA, 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), July 2010 (inproceedings)

Abstract
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefore, the key to successful multi-label learning is how to effectively exploit correlations between different labels to facilitate the learning process. In this paper, we propose to use a Bayesian network structure to efficiently encode the condi- tional dependencies of the labels as well as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient procedure to find such a network structure. With the help of this network, multi-label learning is decomposed into a series of single-label classification problems, where a classifier is constructed for each label by incorporating its parental labels as additional features. Label sets of unseen examples are predicted recursively according to the label ordering given by the network. Extensive experiments on a broad range of data sets validate the effectiveness of our approach against other well-established methods.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A Maximum Entropy Approach to Semi-supervised Learning

Erkan, A., Altun, Y.

30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), 30, pages: 80, July 2010 (poster)

Abstract
Maximum entropy (MaxEnt) framework has been studied extensively in supervised learning. Here, the goal is to find a distribution p that maximizes an entropy function while enforcing data constraints so that the expected values of some (pre-defined) features with respect to p match their empirical counterparts approximately. Using different entropy measures, different model spaces for p and different approximation criteria for the data constraints yields a family of discriminative supervised learning methods (e.g., logistic regression, conditional random fields, least squares and boosting). This framework is known as the generalized maximum entropy framework. Semi-supervised learning (SSL) has emerged in the last decade as a promising field that combines unlabeled data along with labeled data so as to increase the accuracy and robustness of inference algorithms. However, most SSL algorithms to date have had trade-offs, e.g., in terms of scalability or applicability to multi-categorical data. We extend the generalized MaxEnt framework to develop a family of novel SSL algorithms. Extensive empirical evaluation on benchmark data sets that are widely used in the literature demonstrates the validity and competitiveness of the proposed algorithms.

PDF PDF [BibTex]

PDF PDF [BibTex]


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Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution

Hirsch, M., Sra, S., Schölkopf, B., Harmeling, S.

In Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition, pages: 607-614, IEEE, Piscataway, NJ, USA, CVPR, June 2010 (inproceedings)

Abstract
Ultimately being motivated by facilitating space-variant blind deconvolution, we present a class of linear transformations, that are expressive enough for space-variant filters, but at the same time especially designed for efficient matrix-vector-multiplications. Successful results on astronomical imaging through atmospheric turbulences and on noisy magnetic resonance images of constantly moving objects demonstrate the practical significance of our approach.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Grasping with Vision Descriptors and Motor Primitives

Kroemer, O., Detry, R., Piater, J., Peters, J.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2010), pages: 47-54, (Editors: Filipe, J. , J. Andrade-Cetto, J.-L. Ferrier), SciTePress , Lisboa, Portugal, 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO), June 2010 (inproceedings)

Abstract
Grasping is one of the most important abilities needed for future service robots. Given the task of picking up an object from betweem clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which may not always be available. Therefore, methods for executing grasps are required, which perform well with information gathered from only standard stereo vision, and make only a few necessary assumptions about the task environment. We propose techniques that reactively modify the robot’s learned motor primitives based on information derived from Early Cognitive Vision descriptors. The proposed techniques employ non-parametric potential fields centered on the Early Cognitive Vision descriptors to allow for curving hand trajectories around objects, and finger motions that adapt to the object’s local geometry. The methods were tested on a real robot and found to allow for easier imitation learning of human movements and give a considerable improvement to the robot’s performance in grasping tasks.

PDF Web [BibTex]

PDF Web [BibTex]


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An efficient divide-and-conquer cascade for nonlinear object detection

Lampert, CH.

In Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pages: 1022-1029, IEEE, Piscataway, NJ, USA, Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010 (inproceedings)

Abstract
We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-and-conquer procedure in the space of candidate regions. Compared to the exhaustive procedure that thus far is the state-of-the-art for cascade evaluation, the proposed method requires fewer evaluations of the classifier functions, thereby speeding up the search. Furthermore, we show how the recently developed efficient subwindow search (ESS) procedure [11] can be integrated into the last stage of our method. This allows us to use our method to act not only as a faster procedure for cascade evaluation, but also as a tool to perform efficient branch-and-bound object detection with nonlinear quality functions, in particular kernelized support vector machines. Experiments on the PASCAL VOC 2006 dataset show an acceleration of more than 50% by our method compared to standard cascade evaluation.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Non-parametric estimation of integral probability metrics

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

In Proceedings of the IEEE International Symposium on Information Theory (ISIT 2010), pages: 1428-1432, IEEE, Piscataway, NJ, USA, IEEE International Symposium on Information Theory (ISIT), June 2010 (inproceedings)

Abstract
In this paper, we develop and analyze a nonparametric method for estimating the class of integral probability metrics (IPMs), examples of which include the Wasserstein distance, Dudley metric, and maximum mean discrepancy (MMD). We show that these distances can be estimated efficiently by solving a linear program in the case of Wasserstein distance and Dudley metric, while MMD is computable in a closed form. All these estimators are shown to be strongly consistent and their convergence rates are analyzed. Based on these results, we show that IPMs are simple to estimate and the estimators exhibit good convergence behavior compared to fi-divergence estimators.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Causal Markov condition for submodular information measures

Steudel, B., Janzing, D., Schölkopf, B.

In Proceedings of the 23rd Annual Conference on Learning Theory, pages: 464-476, (Editors: AT Kalai and M Mohri), OmniPress, Madison, WI, USA, COLT, June 2010 (inproceedings)

Abstract
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences among the observations that are entailed by a causal hypothesis in terms of a directed acyclic graph. In the conventional setting, the observations are random variables and the independence is a statistical one, i.e., the information content of observations is measured in terms of Shannon entropy. We formulate a generalized CMC for any kind of observations on which independence is defined via an arbitrary submodular information measure. Recently, this has been discussed for observations in terms of binary strings where information is understood in the sense of Kolmogorov complexity. Our approach enables us to find computable alternatives to Kolmogorov complexity, e.g., the length of a text after applying existing data compression schemes. We show that our CMC is justified if one restricts the attention to a class of causal mechanisms that is adapted to the respective information measure. Our justification is similar to deriving the statistical CMC from functional models of causality, where every variable is a deterministic function of its observed causes and an unobserved noise term. Our experiments on real data demonstrate the performance of compression based causal inference.

PDF Web [BibTex]

PDF Web [BibTex]


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The effect of positioning aids on PET quantification following MR-based attenuation correction (AC) in PET/MR imaging

Mantlik, F., Hofmann, M., Kupferschläger, J., Werner, M., Pichler, B., Beyer, T.

Journal of Nuclear Medicine, 51(Supplement 2):1418 , June 2010 (poster)

Abstract
Objectives: We study the quantitative effect of not accounting for the attenuation of patient positioning aids in combined PET/MR imaging. Methods: Positioning aids cannot be detected with conventional MR sequences. We mimic this effect using PET/CT data (Biograph HiRez16) with the foams removed from CT images prior to using them for CT-AC. PET/CT data were acquired using standard parameters (phantoms/patients): 120/140 kVp, 30/250 mAs, 5 mm slices, OSEM (4i, 8s, 5 mm filter) following CT-AC. First, a uniform 68Ge-cylinder was positioned centrally in the PET/CT and fixed with a vacuum mattress (10 cm thick). Second, the same cylinder was placed in 3 positioning aids from the PET/MR (BrainPET-3T). Third, 5 head/neck patients who were fixed in a vacuum mattress were selected. In all 3 studies PET recon post CT-AC based on measured CT images was used as the reference (mCT-AC). The PET/MR set-up was mimicked by segmenting the foam inserts from the measured CT images and setting their voxel values to -1000 HU (air). PET images were reconstructed using CT-AC with the segmented CT images (sCT-AC). PET images with mCT- and sCT-AC were compared. Results: sCT-AC underestimated PET voxel values in the phantom by 6.7% on average compared to mCT-AC with the vacuum mattress in place. 5% of the PET voxels were underestimated by >=10%. Not accounting for MR positioning aids during AC led to an underestimation of 2.8% following sCT-AC, with 5% of the PET voxels being underestimated by >=7% wrt mCT-AC. Preliminary evaluation of the patient data indicates a slightly higher bias from not accounting for patient positioning aids (mean: -9.1%, 5% percentile: -11.2%). Conclusions: A considerable and regionally variable underestimation of the PET activity following AC is observed when positioning aids are not accounted for. This bias may become relevant in neurological activation or dementia studies with PET/MR

Web [BibTex]

Web [BibTex]


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UDP Communication channel design of master-slave robot system

Hong, A., Cho, JH., Wang, H., Lee, DY.

In pages: 231-232, 2010 KSME Conference, June 2010 (inproceedings)

[BibTex]

[BibTex]


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Telling cause from effect based on high-dimensional observations

Janzing, D., Hoyer, P., Schölkopf, B.

In Proceedings of the 27th International Conference on Machine Learning, pages: 479-486, (Editors: J Fürnkranz and T Joachims), International Machine Learning Society, Madison, WI, USA, ICML, June 2010 (inproceedings)

Abstract
We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if the covariance matrix of the cause and the structure matrix mapping the cause to the effect are independently chosen. The method applies to both stochastic and deterministic causal relations, provided that the dimensionality is sufficiently high (in some experiments, 5 was enough). It is applicable to Gaussian as well as non-Gaussian data.

PDF Web [BibTex]

PDF Web [BibTex]


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Multi-task Learning for Zero Training Brain-Computer Interfaces

Alamgir, M., Grosse-Wentrup, M., Altun, Y.

4th International BCI Meeting, June 2010 (poster)

Abstract
Brain-computer interfaces (BCIs) are limited in their applicability in everyday settings by the current necessity to record subject-specific calibration data prior to actual use of the BCI for communication. In this work, we utilize the framework of multitask learning to construct a BCI that can be used without any subject-specific calibration process, i.e., with zero training data. In BCIs based on EEG or MEG, the predictive function of a subject's intention is commonly modeled as a linear combination of some features derived from spatial and spectral recordings. The coefficients of this combination correspond to the importance of the features for predicting the intention of the subject. These coefficients are usually learned separately for each subject due to inter-subject variability. Principle feature characteristics, however, are known to remain invariant across subject. For example, it is well known that in motor imagery paradigms spectral power in the mu- and beta frequency ranges (roughly 8-14 Hz and 20-30 Hz, respectively) over sensorimotor areas provides most information on a subject's intention. Based on this assumption, we define the intention prediction function as a combination of subject-invariant and subject-specific models, and propose a machine learning method that infers these models jointly using data from multiple subjects. This framework leads to an out-of-the-box intention predictor, where the subject-invariant model can be employed immediately for a subject with no prior data. We present a computationally efficient method to further improve this BCI to incorporate subject-specific variations as such data becomes available. To overcome the problem of high dimensional feature spaces in this context, we further present a new method for finding the relevance of different recording channels according to actions performed by subjects. Usually, the BCI feature representation is a concatenation of spectral features extracted from different channels. This representation, however, is redundant, as recording channels at different spatial locations typically measure overlapping sources within the brain due to volume conduction. We address this problem by assuming that the relevance of different spectral bands is invariant across channels, while learning different weights for each recording electrode. This framework allows us to significantly reduce the feature space dimensionality without discarding potentially useful information. Furthermore, the resulting out-of-the-box BCI can be adapted to different experimental setups, for example EEG caps with different numbers of channels, as long as there exists a mapping across channels in different setups. We demonstrate the feasibility of our approach on a set of experimental EEG data recorded during a standard two-class motor imagery paradigm from a total of ten healthy subjects. Specifically, we show that satisfactory classification results can be achieved with zero training data, and that combining prior recordings with subject-specific calibration data substantially outperforms using subject-specific data only.

Web [BibTex]


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Causal Influence of Gamma Oscillations on Performance in Brain-Computer Interfaces

Grosse-Wentrup, M., Hill, J., Schölkopf, B.

4th International BCI Meeting0, June 2010 (poster)

Abstract
Background and Objective: While machine learning approaches have led to tremendous advances in brain-computer interfaces (BCIs) in recent years (cf. [1]), there still exists a large variation in performance across subjects. Furthermore, a significant proportion of subjects appears incapable of achieving above chance-level classification accuracy [2], which to date includes all subjects in a completely locked-in state that have been trained in BCI control. Understanding the reasons for this variation in performance arguably constitutes one of the most fundamental open questions in research on BCIs. Methods & Results Using a machine learning approach, we derive a trial-wise measure of how well EEG recordings can be classified as either left- or right-hand motor imagery. Specifically, we train a support vector machine (SVM) on log-bandpower features (7-40 Hz) derived from EEG channels after spatial filtering with a surface Laplacian, and then compute the trial-wise distance of the output of the SVM from the separating hyperplane using a cross-validation procedure. We then correlate this trial-wise performance measure, computed on EEG recordings of ten healthy subjects, with log-bandpower in the gamma frequency range (55-85 Hz), and demonstrate that it is positively correlated with frontal- and occipital gamma-power and negatively correlated with centro-parietal gamma-power. This correlation is shown to be highly significant on the group level as well as in six out of ten subjects on the single-subject level. We then utilize the framework for causal inference developed by Pearl, Spirtes and others [3,4] to present evidence that gamma-power is not only correlated with BCI performance but does indeed exert a causal influence on it. Discussion and Conclusions Our results indicate that successful execution of motor imagery, and hence reliable communication by means of a BCI based on motor imagery, requires a volitional shift of gamma-power from centro-parietal to frontal and occipital regions. As such, our results provide the first non-trivial explanation for the variation in BCI performance across and within subjects. As this topographical alteration in gamma-power is likely to correspond to a specific attentional shift, we propose to provide subjects with feedback on their topographical distribution of gamma-power in order to establish the attentional state required for successful execution of motor imagery.

Web [BibTex]


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Solving large-scale nonnegative least-squares

Sra, S.

16th Conference of the International Linear Algebra Society (ILAS 2010), 16, pages: 19, June 2010, based on Joint work with Dongmin Kim and Inderjit Dhillon (poster)

Abstract
We study the fundamental problem of nonnegative least squares. This problem was apparently introduced by Lawson and Hanson [1] under the name NNLS. As is evident from its name, NNLS seeks least-squares solutions that are also nonnegative. Owing to its wide-applicability numerous algorithms have been derived for NNLS, beginning from the active-set approach of Lawson and Hanson [1] leading up to the sophisticated interior-point method of Bellavia et al. [2]. We present a new algorithm for NNLS that combines projected subgradients with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our resulting algorithm is called BBSG, and we guarantee its convergence by exploiting properties of NNLS in conjunction with projected subgradients. BBSG is surprisingly simple and scales well to large problems. We substantiate our claims by empirically evaluating BBSG and comparing it with established convex solvers and specialized NNLS algorithms. The numerical results suggest that BBSG is a practical method for solving large-scale NNLS problems.

PDF Web [BibTex]

PDF Web [BibTex]


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A scalable trust-region algorithm with application to mixed-norm regression

Kim, D., Sra, S., Dhillon, I.

In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pages: 519-526, (Editors: Fürnkranz, J. , T. Joachims), International Machine Learning Society, Madison, WI, USA, 27th International Conference on Machine Learning (ICML), June 2010 (inproceedings)

Abstract
We present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably, for sparsity-promoting regularizers such as ℓ1 or ℓ1, ∞ norms, it enables efficient computation of sparse solutions. Our approach is based on the trust-region framework with nonsmooth objectives, which allows us to build on known results to provide convergence analysis. We avoid the computational overheads associated with the conventional Hessian approximation used by trust-region methods by instead using a simple separable quadratic approximation. This approximation also enables use of proximity operators for tackling nonsmooth regularizers. We illustrate the versatility of our resulting algorithm by specializing it to three mixed-norm regression problems: group lasso [36], group logistic regression [21], and multi-task lasso [19]. We experiment with both synthetic and real-world large-scale data—our method is seen to be competitive, robust, and scalable.

PDF Web [BibTex]

PDF Web [BibTex]


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The Influence of the Image Basis on Modeling and Steganalysis Performance

Schwamberger, V., Le, P., Schölkopf, B., Franz, M.

In Information Hiding, pages: 133-144, (Editors: R Böhme and PWL Fong and R Safavi-Naini), Springer, Berlin, Germany, 12th international Workshop (IH), June 2010 (inproceedings)

Abstract
We compare two image bases with respect to their capabilities for image modeling and steganalysis. The first basis consists of wavelets, the second is a Laplacian pyramid. Both bases are used to decompose the image into subbands where the local dependency structure is modeled with a linear Bayesian estimator. Similar to existing approaches, the image model is used to predict coefficient values from their neighborhoods, and the final classification step uses statistical descriptors of the residual. Our findings are counter-intuitive on first sight: Although Laplacian pyramids have better image modeling capabilities than wavelets, steganalysis based on wavelets is much more successful. We present a number of experiments that suggest possible explanations for this result.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Simultaneous PET/MRI for the evaluation of hemato-oncological diseases with lower extremity manifestations

Sauter, A., Horger, M., Boss, A., Kolb, A., Mantlik, F., Kanz, L., Pfannenberg, C., Stegger, L., Claussen, C., Pichler, B.

Journal of Nuclear Medicine, 51(Supplement 2):1001 , June 2010 (poster)

Abstract
Objectives: The study purpose is the evaluation of patients, suffering from hemato-oncological disease with complications at the lower extremities, using simultaneous PET/MRI. Methods: Until now two patients (chronic active graft-versus-host-disease [GvHD], B-non Hodgkin lymphoma [B-NHL]) before and after therapy were examined in a 3-Tesla-BrainPET/MRI hybrid system following F-18-FDG-PET/CT. Simultaneous static PET (1200 sec.) and MRI scans (T1WI, T2WI, post-CA) were acquired. Results: Initial results show the feasibility of using hybrid PET/MRI-technology for musculoskeletal imaging of the lower extremities. Simultaneous PET and MRI could be acquired in diagnostic quality. Before treatment our patient with GvHD had a high fascia and muscle FDG uptake, possibly due to muscle encasement. T2WI and post gadolinium T1WI revealed a fascial thickening and signs of inflammation. After therapy with steroids followed by imatinib the patient’s symptoms improved while, the muscular FDG uptake droped whereas the MRI signal remained unchanged. We assume that fascial elasticity improved during therapy despite persistance of fascial thickening. The examination of the second patient with B-NHL manifestation in the tibia showed a significant signal and uptake decrease in the bone marrow and surrounding lesions in both, MRI and PET after therapy with rituximab. The lack of residual FDG-uptake proved superior to MRI information alone helping for exclusion of vital tumor. Conclusions: Combined PET/MRI is a powerful tool to monitor diseases requiring high soft tissue contrast along with molecular information from the FDG uptake.

Web [BibTex]

Web [BibTex]


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A PAC-Bayesian Analysis of Co-clustering, Graph Clustering, and Pairwise Clustering

Seldin, Y.

In ICML 2010 Workshop on Social Analytics: Learning from human interactions, pages: 1-5, ICML Workshop on Social Analytics: Learning from human interactions, June 2010 (inproceedings)

Abstract
We review briefly the PAC-Bayesian analysis of co-clustering (Seldin and Tishby, 2008, 2009, 2010), which provided generalization guarantees and regularization terms absent in the preceding formulations of this problem and achieved state-of-the-art prediction results in MovieLens collaborative filtering task. Inspired by this analysis we formulate weighted graph clustering1 as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. Following the lines of (Seldin and Tishby, 2010) we derive PAC-Bayesian generalization bounds for graph clustering. The bounds show that graph clustering should optimize a trade-off between empirical data fit and the mutual information that clusters preserve on the graph nodes. A similar trade-off derived from information-theoretic considerations was already shown to produce state-of-the-art results in practice (Slonim et al., 2005; Yom-Tov and Slonim, 2009). This paper supports the empirical evidence by providing a better theoretical foundation, suggesting formal generalization guarantees, and offering a more accurate way to deal with finite sample issues.

PDF Web [BibTex]

PDF Web [BibTex]


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Apprenticeship learning via soft local homomorphisms

Boularias, A., Chaib-Draa, B.

In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pages: 2971-2976, IEEE, Piscataway, NJ, USA, 2010 IEEE International Conference on Robotics and Automation (ICRA), May 2010 (inproceedings)

Abstract
We consider the problem of apprenticeship learning when the expert's demonstration covers only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient solution to this problem based on the assumption that the expert is optimally acting in a Markov Decision Process (MDP). However, past work on IRL requires an accurate estimate of the frequency of encountering each feature of the states when the robot follows the expert‘s policy. Given that the complete policy of the expert is unknown, the features frequencies can only be empirically estimated from the demonstrated trajectories. In this paper, we propose to use a transfer method, known as soft homomorphism, in order to generalize the expert‘s policy to unvisited regions of the state space. The generalized policy can be used either as the robot‘s final policy, or to calculate the features frequencies within an IRL algorithm. Empirical results show that our approach is able to learn good policies from a small number of demonstrations.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Using Model Knowledge for Learning Inverse Dynamics

Nguyen-Tuong, D., Peters, J.

In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pages: 2677-2682, IEEE, Piscataway, NJ, USA, 2010 IEEE International Conference on Robotics and Automation (ICRA), May 2010 (inproceedings)

Abstract
In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in most robot learning approaches, the model is learned from scratch disregarding all prior knowledge about the system. For many complex robot systems, available prior knowledge from advanced physics-based modeling techniques can entail valuable information for model learning that may result in faster learning speed, higher accuracy and better generalization. In this paper, we investigate how parametric physical models (e.g., obtained from rigid body dynamics) can be used to improve the learning performance, and, especially, how semiparametric regression methods can be applied in this context. We present two possible semiparametric regression approaches, where the knowledge of the physical model can either become part of the mean function or of the kernel in a nonparametric Gaussian process regression. We compare the learning performance o f these methods first on sampled data and, subsequently, apply the obtained inverse dynamics models in tracking control on a real Barrett WAM. The results show that the semiparametric models learned with rigid body dynamics as prior outperform the standard rigid body dynamics models on real data while generalizing better for unknown parts of the state space.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Coherent Inference on Optimal Play in Game Trees

Hennig, P., Stern, D., Graepel, T.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 326-333, (Editors: Teh, Y.W. , M. Titterington ), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

Abstract
Round-based games are an instance of discrete planning problems. Some of the best contemporary game tree search algorithms use random roll-outs as data. Relying on a good policy, they learn on-policy values by propagating information upwards in the tree, but not between sibling nodes. Here, we present a generative model and a corresponding approximate message passing scheme for inference on the optimal, off-policy value of nodes in smooth AND/OR trees, given random roll-outs. The crucial insight is that the distribution of values in game trees is not completely arbitrary. We define a generative model of the on-policy values using a latent score for each state, representing the value under the random roll-out policy. Inference on the values under the optimal policy separates into an inductive, pre-data step and a deductive, post-data part. Both can be solved approximately with Expectation Propagation, allowing off-policy value inference for any node in the (exponentially big) tree in linear time.

PDF Web [BibTex]

PDF Web [BibTex]


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Incremental Sparsification for Real-time Online Model Learning

Nguyen-Tuong, D., Peters, J.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 557-564, (Editors: Teh, Y.W. , M. Titterington), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

Abstract
Online model learning in real-time is required by many applications such as in robot tracking control. It poses a difficult problem, as fast and incremental online regression with large data sets is the essential component which cannot be achieved by straightforward usage of off-the-shelf machine learning methods (such as Gaussian process regression or support vector regression). In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for large scale real-time model learning. The proposed approach combines a sparsification method based on an independence measure with a large scale database. In combination with an incremental learning approach such as sequential support vector regression, we obtain a regression method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real robot emphasizes the applicability of the proposed approach in real-time online model learning for real world systems.

PDF Web [BibTex]

PDF Web [BibTex]


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Multitask Learning for Brain-Computer Interfaces

Alamgir, M., Grosse-Wentrup, M., Altun, Y.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 17-24, (Editors: Teh, Y.W. , M. Titterington), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics , May 2010 (inproceedings)

Abstract
Brain-computer interfaces (BCIs) are limited in their applicability in everyday settings by the current necessity to record subjectspecific calibration data prior to actual use of the BCI for communication. In this paper, we utilize the framework of multitask learning to construct a BCI that can be used without any subject-specific calibration process. We discuss how this out-of-the-box BCI can be further improved in a computationally efficient manner as subject-specific data becomes available. The feasibility of the approach is demonstrated on two sets of experimental EEG data recorded during a standard two-class motor imagery paradigm from a total of 19 healthy subjects. Specifically, we show that satisfactory classification results can be achieved with zero training data, and combining prior recordings with subjectspecific calibration data substantially outperforms using subject-specific data only. Our results further show that transfer between recordings under slightly different experimental setups is feasible.

PDF Web [BibTex]

PDF Web [BibTex]


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Identifying Cause and Effect on Discrete Data using Additive Noise Models

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

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 597-604, (Editors: YW Teh and M Titterington), JMLR, Cambridge, MA, USA, 13th International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

Abstract
Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. Recently, methods using additive noise models have been suggested to approach the case of continuous variables. In many situations, however, the variables of interest are discrete or even have only finitely many states. In this work we extend the notion of additive noise models to these cases. Whenever the joint distribution P(X;Y ) admits such a model in one direction, e.g. Y = f(X) + N; N ? X, it does not admit the reversed model X = g(Y ) + ~N ; ~N ? Y as long as the model is chosen in a generic way. Based on these deliberations we propose an efficient new algorithm that is able to distinguish between cause and effect for a finite sample of discrete variables. We show that this algorithm works both on synthetic and real data sets.

PDF Web [BibTex]

PDF Web [BibTex]


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Semi-supervised Learning via Generalized Maximum Entropy

Erkan, A., Altun, Y.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 209-216, (Editors: Teh, Y.W. , M. Titterington), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics , May 2010 (inproceedings)

Abstract
Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that maximizes an entropy function while respecting prior information represented as potential functions in miscellaneous forms of constraints and/or penalties. We extend this framework to semi-supervised learning by incorporating unlabeled data via modifications to these potential functions reflecting structural assumptions on the data geometry. The proposed approach leads to a family of discriminative semi-supervised algorithms, that are convex, scalable, inherently multi-class, easy to implement, and that can be kernelized naturally. Experimental evaluation of special cases shows the competitiveness of our methodology.

PDF Web [BibTex]

PDF Web [BibTex]


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A New Algorithm for Improving the Resolution of Cryo-EM Density Maps

Hirsch, M., Schölkopf, B., Habeck, M.

In Research in Computational Molecular Biology, Lecture Notes in Bioinformatics, Vol. 6044 , pages: 174-188, (Editors: B Berger), Springer, Berlin, Germany, 14th International Conference on Research in Computational Molecular Biology (RECOMB), May 2010 (inproceedings)

Abstract
Cryo-electron microscopy (cryo-EM) plays an increasingly prominent role in structure elucidation of macromolecular assemblies. Advances in experimental instrumentation and computational power have spawned numerous cryo-EM studies of large biomolecular complexes resulting in the reconstruction of three-dimensional density maps at intermediate and low resolution. In this resolution range, identification and interpretation of structural elements and modeling of biomolecular structure with atomic detail becomes problematic. In this paper, we present a novel algorithm that enhances the resolution of intermediate- and low-resolution density maps. Our underlying assumption is to model the low-resolution density map as a blurred and possibly noise-corrupted version of an unknown high-resolution map that we seek to recover by deconvolution. By exploiting the nonnegativity of both the high-resolution map and blur kernel we derive multiplicative updates reminiscent of those used in nonnegative matrix factorization. Our framework allows for easy incorporation of additional prior knowledge such as smoothness and sparseness, on both the sharpened density map and the blur kernel. A probabilistic formulation enables us to derive updates for the hyperparameters, therefore our approach has no parameter that needs adjustment. We apply the algorithm to simulated three-dimensional electron microscopic data. We show that our method provides better resolved density maps when compared with B-factor sharpening, especially in the presence of noise. Moreover, our method can use additional information provided by homologous structures, which helps to improve the resolution even further.

Web DOI [BibTex]

Web DOI [BibTex]


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Movement Templates for Learning of Hitting and Batting

Kober, J., Mülling, K., Krömer, O., Lampert, C., Schölkopf, B., Peters, J.

In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pages: 853-858, IEEE, Piscataway, NJ, USA, 2010 IEEE International Conference on Robotics and Automation (ICRA), May 2010 (inproceedings)

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Solving large-scale nonnegative least squares using an adaptive non-monotonic method

Sra, S., Kim, D., Dhillon, I.

24th European Conference on Operational Research (EURO 2010), 24, pages: 223, April 2010 (poster)

Abstract
We present an efficient algorithm for large-scale non-negative least-squares (NNLS). We solve NNLS by extending the unconstrained quadratic optimization method of Barzilai and Borwein (BB) to handle nonnegativity constraints. Our approach is simple yet efficient. It differs from other constrained BB variants as: (i) it uses a specific subset of variables for computing BB steps; and (ii) it scales these steps adaptively to ensure convergence. We compare our method with both established convex solvers and specialized NNLS methods, and observe highly competitive empirical performance.

PDF [BibTex]

PDF [BibTex]


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Sparse regression via a trust-region proximal method

Kim, D., Sra, S., Dhillon, I.

24th European Conference on Operational Research (EURO 2010), 24, pages: 278, April 2010 (poster)

Abstract
We present a method for sparse regression problems. Our method is based on the nonsmooth trust-region framework that minimizes a sum of smooth convex functions and a nonsmooth convex regularizer. By employing a separable quadratic approximation to the smooth part, the method enables the use of proximity operators, which in turn allow tackling the nonsmooth part efficiently. We illustrate our method by implementing it for three important sparse regression problems. In experiments with synthetic and real-world large-scale data, our method is seen to be competitive, robust, and scalable.

PDF [BibTex]

PDF [BibTex]


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Quantitative Evaluation of MR-based Attenuation Correction for Positron Emission Tomography (PET)

Mantlik, F.

Biologische Kybernetik, Universität Mannheim, Germany, March 2010 (diplomathesis)

[BibTex]

[BibTex]


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PAC-Bayesian Bounds for Discrete Density Estimation and Co-clustering Analysis

Seldin, Y., Tishby, N.

Workshop "Foundations and New Trends of PAC Bayesian Learning", 2010, March 2010 (poster)

Abstract
We applied PAC-Bayesian framework to derive gen- eralization bounds for co-clustering1. The analysis yielded regularization terms that were absent in the preceding formulations of this task. The bounds sug- gested that co-clustering should optimize a trade-off between its empirical performance and the mutual in- formation that the cluster variables preserve on row and column indices. Proper regularization enabled us to achieve state-of-the-art results in prediction of the missing ratings in the MovieLens collaborative filtering dataset. In addition a PAC-Bayesian bound for discrete den- sity estimation was derived. We have shown that the PAC-Bayesian bound for classification is a spe- cial case of the PAC-Bayesian bound for discrete den- sity estimation. We further introduced combinatorial priors to PAC-Bayesian analysis. The combinatorial priors are more appropriate for discrete domains, as opposed to Gaussian priors, the latter of which are suitable for continuous domains. It was shown that combinatorial priors lead to regularization terms in the form of mutual information.

PDF Web [BibTex]

PDF Web [BibTex]


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Experiments with Motor Primitives to learn Table Tennis

Peters, J., Mülling, K., Kober, J.

In Experimental Robotics, pages: 1-13, (Editors: Khatib, O. , V. Kumar, G. Sukhatme), Springer, Berlin, Germany, 12th International Symposium on Experimental Robotics (ISER), March 2010 (inproceedings)

Web [BibTex]

Web [BibTex]


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Causality: Objectives and Assessment

Guyon, I., Janzing, D., Schölkopf, B.

In JMLR Workshop and Conference Proceedings: Volume 6 , pages: 1-42, (Editors: I Guyon and D Janzing and B Schölkopf), MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , February 2010 (inproceedings)

Abstract
The NIPS 2008 workshop on causality provided a forum for researchers from different horizons to share their view on causal modeling and address the difficult question of assessing causal models. There has been a vivid debate on properly separating the notion of causality from particular models such as graphical models, which have been dominating the field in the past few years. Part of the workshop was dedicated to discussing the results of a challenge, which offered a wide variety of applications of causal modeling. We have regrouped in these proceedings the best papers presented. Most lectures were videotaped or recorded. All information regarding the challenge and the lectures are found at http://www.clopinet.com/isabelle/Projects/NIPS2008/. This introduction provides a synthesis of the findings and a gentle introduction to causality topics, which are the object of active research.

Web [BibTex]

Web [BibTex]


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Finding Gene-Gene Interactions using Support Vector Machines

Rakitsch, B.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

[BibTex]

[BibTex]


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Leveraging Sequence Classification by Taxonomy-based Multitask Learning

Widmer, C., Leiva, J., Altun, Y., Rätsch, G.

In Research in Computational Molecular Biology, LNCS, Vol. 6044, pages: 522-534, (Editors: B Berger), Springer, Berlin, Germany, 14th Annual International Conference, RECOMB, 2010 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Probabilistic latent variable models for distinguishing between cause and effect

Mooij, J., Stegle, O., Janzing, D., Zhang, K., Schölkopf, B.

In Advances in Neural Information Processing Systems 23, pages: 1687-1695, (Editors: J Lafferty and CKI Williams and J Shawe-Taylor and RS Zemel and A Culotta), Curran, Red Hook, NY, USA, 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

Abstract
We propose a novel method for inferring whether X causes Y or vice versa from joint observations of X and Y. The basic idea is to model the observed data using probabilistic latent variable models, which incorporate the effects of unobserved noise. To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive). An important novel aspect of our work is that we do not restrict the model class, but instead put general non-parametric priors on this function and on the distribution of the cause. The causal direction can then be inferred by using standard Bayesian model selection. We evaluate our approach on synthetic data and real-world data and report encouraging results.

PDF Web [BibTex]

PDF Web [BibTex]