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2012


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An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli

Hill, N., Schölkopf, B.

Journal of Neural Engineering, 9(2):026011, February 2012 (article)

Abstract
We report on the development and online testing of an electroencephalogram-based brain–computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare 'oddball' stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

PDF DOI [BibTex]


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A non-monotonic method for large-scale non-negative least squares

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

Optimization Methods and Software, 28(5):1012-1039, Febuary 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Inferring Networks of Diffusion and Influence

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

ACM Transactions on Knowledge Discovery from Data, 5(4:21), February 2012 (article)

Abstract
Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions (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 finds provably near-optimal networks. We demonstrate the effectiveness of our approach by tracing information diffusion 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 for the top 1,000 media sites and blogs 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.

Web DOI [BibTex]

Web DOI [BibTex]


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High Gamma-Power Predicts Performance in Brain-Computer Interfacing

Grosse-Wentrup, M., Schölkopf, B.

(3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

Abstract
Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency gamma-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this nding as empirical support for an in uence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.

PDF [BibTex]

PDF [BibTex]


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Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

Stegle, O., Parts, L., Piipari, M., Winn, J., Durbin, R.

Nature Protocols, 7(3):500–507, February 2012 (article)

Abstract
We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad variance components in the measurements. PEER takes as input transcript profiles and covariates from a set of individuals, and then outputs hidden factors that explain much of the expression variability. Optionally, these factors can be interpreted as pathway or transcription factor activations by providing prior information about which genes are involved in the pathway or targeted by the factor. The inferred factors are used in genetic association analyses. First, they are treated as additional covariates, and are included in the model to increase detection power for mapping expression traits. Second, they are analyzed as phenotypes themselves to understand the causes of global expression variability. PEER extends previous related surrogate variable models and can be implemented within hours on a desktop computer.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment

Zander, TO., Jatzev, S.

Journal of Neural Engineering, 9(1):016003, 10, February 2012 (article)

Abstract
Brain–computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information—otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human–machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human–machine system. The EEG data from this experiment are available for public download at www.phypa.org.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Expectation-Maximization methods for solving (PO)MDPs and optimal control problems

Toussaint, M., Storkey, A., Harmeling, S.

In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

PDF [BibTex]

PDF [BibTex]


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Active Learning Methods in Classification of Remote Sensing Images

Bruzzone, L., Persello, C., Demir, B.

In Signal and Image Processing for Remote Sensing, (Editors: CH Chen), CRC Press, Boca Raton, FL, USA, January 2012 (inbook) In press

[BibTex]

[BibTex]


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Personalized medicine: from genotypes and molecular phenotypes towards computed therapy

Stegle, O., Roth, FP., Morris, Q., Listgarten, J.

In pages: 323-326, (Editors: Altman, R.B. , A.K. Dunker, L. Hunter, T. Murray, T.E. Klein), World Scientific Publishing, Singapore, Pacific Symposium on Biocomputing (PSB), January 2012 (inproceedings)

Abstract
Joint genotyping and large-scale phenotyping of molecular traits are currently available for a number of important patient study cohorts and will soon become feasible in routine medical practice. These data are one component of several that are setting the stage for the development of personalized medicine, promising to yield better disease classification, enabling more specific treatment, and also allowing for improved preventive medical screening. This conference session explores statistical challenges and new opportunities that arise from application of genome-scale experimentation for personalized genomics and medicine.

Web DOI [BibTex]

Web DOI [BibTex]


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Approximate Gaussian Integration using Expectation Propagation

Cunningham, J., Hennig, P., Lacoste-Julien, S.

In pages: 1-11, -, January 2012 (inproceedings) Submitted

Abstract
While Gaussian probability densities are omnipresent in applied mathematics, Gaussian cumulative probabilities are hard to calculate in any but the univariate case. We offer here an empirical study of the utility of Expectation Propagation (EP) as an approximate integration method for this problem. For rectangular integration regions, the approximation is highly accurate. We also extend the derivations to the more general case of polyhedral integration regions. However, we find that in this polyhedral case, EP's answer, though often accurate, can be almost arbitrarily wrong. These unexpected results elucidate an interesting and non-obvious feature of EP not yet studied in detail, both for the problem of Gaussian probabilities and for EP more generally.

Web [BibTex]

Web [BibTex]


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How the initialization affects the stability of the k-means algorithm

Bubeck, S., Meila, M., von Luxburg, U.

ESAIM: Probability and Statistics, 16, pages: 436-452, January 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies

Fusi, N., Stegle, O., Lawrence, ND.

PLoS Computational Biology, 8(1):1-9, January 2012 (article)

Abstract
Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/.

Web DOI [BibTex]

Web DOI [BibTex]


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Simultaneous small animal PET/MR reveals different brain networks during stimulation and rest

Wehrl, H., Hossain, M., Lankes, K., Liu, C., Bezrukov, I., Martirosian, P., Reischl, G., Schick, F., Pichler, B.

World Molecular Imaging Congress (WMIC), 2012 (talk)

[BibTex]

[BibTex]


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On empirical tradeoffs in large scale hierarchical classification

Babbar, R., Partalas, I., Gaussier, E., Amblard, C.

In 21st ACM International Conference on Information and Knowledge Management, pages: 2299-2302, (Editors: G Lebanon and H Wang and MJ Zaki), ACM, CIKM, 2012 (inproceedings)

Web [BibTex]

Web [BibTex]


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Kernel Topic Models

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

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 511-519, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS , 2012 (inproceedings)

Abstract
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.

PDF Web [BibTex]

PDF Web [BibTex]


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Bayesian flexible fitting of biomolecular structures into EM maps

Habeck, M.

Biophysical journal, 2012 (article) Submitted

[BibTex]

[BibTex]


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Structured Apprenticeship Learning

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

In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Identifying endogenous rhythmic spatio-temporal patterns in micro-electrode array recordings

Besserve, M., Panagiotaropoulos, T., Crocker, B., Kapoor, V., Tolias, A., Panzeri, S., Logothetis, N.

9th annual Computational and Systems Neuroscience meeting (Cosyne), 2012 (poster)

[BibTex]

[BibTex]


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Blind Correction of Optical Aberrations

Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B.

In Computer Vision - ECCV 2012, LNCS Vol. 7574, pages: 187-200, (Editors: A Fitzgibbon, S Lazebnik, P Perona, Y Sato, and C Schmid), Springer, Berlin, Germany, 12th IEEE European Conference on Computer Vision, ECCV, 2012 (inproceedings)

Abstract
Camera lenses are a critical component of optical imaging systems, and lens imperfections compromise image quality. While traditionally, sophisticated lens design and quality control aim at limiting optical aberrations, recent works [1,2,3] promote the correction of optical flaws by computational means. These approaches rely on elaborate measurement procedures to characterize an optical system, and perform image correction by non-blind deconvolution. In this paper, we present a method that utilizes physically plausible assumptions to estimate non-stationary lens aberrations blindly, and thus can correct images without knowledge of specifics of camera and lens. The blur estimation features a novel preconditioning step that enables fast deconvolution. We obtain results that are competitive with state-of-the-art non-blind approaches.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Interactive Domain Adaptation Technique for the Classification of Remote Sensing Images

Persello, C., Dinuzzo, F.

In IEEE International Geoscience and Remote Sensing Symposium , pages: 6872-6875, IEEE, IGARSS, 2012 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Point Cloud Completion Using Symmetries and Extrusions

Kroemer, O., Ben Amor, H., Ewerton, M., Peters, J.

In IEEE-RAS International Conference on Humanoid Robots , pages: 680-685, IEEE, HUMANOIDS, 2012 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Reconstruction using Gaussian mixture models

Joubert, P., Habeck, M.

2012 Gordon Research Conference on Three-Dimensional Electron Microscopy (3DEM), 2012 (poster)

Web [BibTex]

Web [BibTex]


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Support Measure Machines for Quasar Target Selection

Muandet, K.

Astro Imaging Workshop, 2012 (talk)

Abstract
In this talk I will discuss the problem of quasar target selection. The objects attributes in astronomy such as fluxes are often subjected to substantial and heterogeneous measurement uncertainties, especially for the medium-redshift between 2.2 and 3.5 quasars which is relatively rare and must be targeted down to g ~ 22 mag. Most of the previous works for quasar target selection includes UV-excess, kernel density estimation, a likelihood approach, and artificial neural network cannot directly deal with the heterogeneous input uncertainties. Recently, extreme deconvolution (XD) has been used to tackle this problem in a well-posed manner. In this work, we present a discriminative approach for quasar target selection that can deal with input uncertainties directly. To do so, we represent each object as a Gaussian distribution whose mean is the object's attribute vector and covariance is the given flux measurement uncertainty. Given a training set of Gaussian distributions, the support measure machines (SMMs) algorithm are trained and used to build the quasar targeting catalog. Preliminary results will also be presented. Joint work with Jo Bovy and Bernhard Sch{\"o}lkopf

Web [BibTex]


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Measurement and Calibration of Noise Bias in Weak Lensing Galaxy Shape Estimation

Kacprzak, T., Zuntz, J., Rowe, B., Bridle, S., Refregier, A., Amara, A., Voigt, L., Hirsch, M.

Monthly Notices of the Royal Astronomical Society (MNRAS), 2012 (article)

[BibTex]

[BibTex]


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The representer theorem for Hilbert spaces: a necessary and sufficient condition

Dinuzzo, F., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 189-196, (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)

PDF [BibTex]

PDF [BibTex]


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PAC-Bayesian Analysis: A Link Between Inference and Statistical Physics

Seldin, Y.

Workshop on Statistical Physics of Inference and Control Theory, 2012 (talk)

Web [BibTex]

Web [BibTex]


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LMM-Lasso: A Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction

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

Bioinformatics, 29(2):206-214, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Complexity of computing distances between geometric trees

Feragen, A.

In Structural, Syntactic, and Statistical Pattern Recognition, LNCS vol 7626, pages: 89-97, (Editors: G Gimel´farb and E Hancock and A Imiya and A Kuijper and M Kudo and S Omachi and T Windeatt and K Yamada), Springer, 14th international workshop on Syntactical and Structural Pattern Recognition (S+SSPR), 2012 (inproceedings)

[BibTex]

[BibTex]


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PET Performance Measurements of a Next Generation Dedicated Small Animal PET/MR Scanner

Liu, C., Hossain, M., Lankes, K., Bezrukov, I., Wehrl, H., Kolb, A., Judenhofer, M., Pichler, B.

Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), 2012 (talk)

[BibTex]

[BibTex]


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Inferential structure determination from NMR data

Habeck, M.

In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

[BibTex]

[BibTex]


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Same, same, but different: EEG correlates of n-back and span working memory tasks

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

In Proceedings of the 48th Congress of the German Society for Psychology, 2012 (inproceedings)

[BibTex]

[BibTex]


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Structure and Dynamics of Diffusion Networks

Gomez Rodriguez, M.

Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

Web [BibTex]

Web [BibTex]


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Learning from Distributions via Support Measure Machines

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

26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (poster)

PDF [BibTex]

PDF [BibTex]


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Existential neuroscience: a functional magnetic resonance imaging investigation of neural responses to reminders of one’s mortality

Quirin, M., Loktyushin, A., Arndt, J., Küstermann, E., Lo, Y., Kuhl, J., Eggert, L.

Social Cognitive and Affective Neuroscience, 7(2):193-198, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Active learning for domain adaptation in the supervised classification of remote sensing images

Persello, C., Bruzzone, L.

IEEE Transactions on Geoscience and Remote Sensing, 50(11):4468-4483, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Adaptive Classifier Selection in Large-Scale Hierarchical Classification

Partalas, I., Babbar, R., Gaussier, E., Amblard, C.

In Neural Information Processing - 19th International Conference, Lecture Notes in Computer Science, Vol. 7665, pages: 612-619, (Editors: T Huang and Z Zeng and C Li and CS Leung), Springer, ICONIP, 2012 (inproceedings)

Web [BibTex]

Web [BibTex]


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Robot Learning

Sigaud, O., Peters, J.

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

Web [BibTex]

Web [BibTex]


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Probabilistic Modeling of Human Movements for Intention Inference

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

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

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

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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

Deisenroth, M., Peters, J.

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

[BibTex]

[BibTex]


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On Causal and Anticausal Learning

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

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

PDF PDF [BibTex]

PDF PDF [BibTex]


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

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

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

[BibTex]

[BibTex]


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Reinforcement Learning in Robotics: A Survey

Kober, J., Peters, J.

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

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

Web DOI [BibTex]

Web DOI [BibTex]


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Learning from distributions via support measure machines

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

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

PDF [BibTex]

PDF [BibTex]


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Blind Deconvolution in Scientific Imaging & Computational Photography

Hirsch, M.

Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

Web [BibTex]

Web [BibTex]


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Scalable nonconvex inexact proximal splitting

Sra, S.

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

PDF [BibTex]

PDF [BibTex]


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

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

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

[BibTex]

[BibTex]


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Efficiently mapping phenotypes to networks of genetic loci

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

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

[BibTex]

[BibTex]


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

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

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

Web Web [BibTex]

Web Web [BibTex]


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The geometry and statistics of geometric trees

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

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

[BibTex]

[BibTex]