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165 results (View BibTeX file of all listed publications)

2016

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Contextual Policy Search for Linear and Nonlinear Generalization of a Humanoid Walking Controller

Abdolmaleki, A., Lau, N., Reis, L., Peters, J., Neumann, G.

Journal of Intelligent & Robotic Systems, 83(3-4):393-408, (Editors: Luis Almeida, Lino Marques ), September 2016, Special Issue: Autonomous Robot Systems (article)

DOI [BibTex]

2016

Abdolmaleki, A., Lau, N., Reis, L., Peters, J., Neumann, G. Contextual Policy Search for Linear and Nonlinear Generalization of a Humanoid Walking Controller Journal of Intelligent & Robotic Systems, 83(3-4):393-408, (Editors: Luis Almeida, Lino Marques ), September 2016, Special Issue: Autonomous Robot Systems (article)

DOI [BibTex]

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Acquiring and Generalizing the Embodiment Mapping from Human Observations to Robot Skills

Maeda, G., Ewerton, M., Koert, D., Peters, J.

IEEE Robotics and Automation Letters, 1(2):784-791, July 2016 (article)

DOI [BibTex]

Maeda, G., Ewerton, M., Koert, D., Peters, J. Acquiring and Generalizing the Embodiment Mapping from Human Observations to Robot Skills IEEE Robotics and Automation Letters, 1(2):784-791, July 2016 (article)

DOI [BibTex]

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On estimation of functional causal models: General results and application to post-nonlinear causal model

Zhang, K., Wang, Z., Zhang, J., Schölkopf, B.

ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 13, January 2016 (article)

PDF DOI [BibTex]

Zhang, K., Wang, Z., Zhang, J., Schölkopf, B. On estimation of functional causal models: General results and application to post-nonlinear causal model ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 13, January 2016 (article)

PDF DOI [BibTex]

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Gaussian Process-Based Predictive Control for Periodic Error Correction

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

IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (article)

PDF DOI [BibTex]

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Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Townsend, J., Koep, N., Weichwald, S.

Journal of Machine Learning Research, 17(137):1-5, 2016 (article)

PDF Arxiv Code Project page link (url) [BibTex]

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A Causal, Data-driven Approach to Modeling the Kepler Data

Wang, D., Hogg, D. W., Foreman-Mackey, D., Schölkopf, B.

Publications of the Astronomical Society of the Pacific, 128(967):094503, 2016 (article)

link (url) DOI Project Page [BibTex]

Wang, D., Hogg, D. W., Foreman-Mackey, D., Schölkopf, B. A Causal, Data-driven Approach to Modeling the Kepler Data Publications of the Astronomical Society of the Pacific, 128(967):094503, 2016 (article)

link (url) DOI Project Page [BibTex]

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Probabilistic Inference for Determining Options in Reinforcement Learning

Daniel, C., van Hoof, H., Peters, J., Neumann, G.

Machine Learning, Special Issue, 104(2):337-357, (Editors: Gärtner, T., Nanni, M., Passerini, A. and Robardet, C.), European Conference on Machine Learning im Machine Learning, Journal Track, 2016, Best Student Paper Award of ECML-PKDD 2016 (article)

DOI Project Page [BibTex]

Daniel, C., van Hoof, H., Peters, J., Neumann, G. Probabilistic Inference for Determining Options in Reinforcement Learning Machine Learning, Special Issue, 104(2):337-357, (Editors: Gärtner, T., Nanni, M., Passerini, A. and Robardet, C.), European Conference on Machine Learning im Machine Learning, Journal Track, 2016, Best Student Paper Award of ECML-PKDD 2016 (article)

DOI Project Page [BibTex]

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Influence of initial fixation position in scene viewing

Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A., Engbert, R.

Vision Research, 129, pages: 33-49, 2016 (article)

link (url) DOI Project Page [BibTex]

Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A., Engbert, R. Influence of initial fixation position in scene viewing Vision Research, 129, pages: 33-49, 2016 (article)

link (url) DOI Project Page [BibTex]

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Testing models of peripheral encoding using metamerism in an oddity paradigm

Wallis, T. S. A., Bethge, M., Wichmann, F. A.

Journal of Vision, 16(2), 2016 (article)

DOI Project Page [BibTex]

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Modeling Confounding by Half-Sibling Regression

Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J.

Proceedings of the National Academy of Science, 113(27):7391-7398, 2016 (article)

Code link (url) DOI Project Page [BibTex]

Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J. Modeling Confounding by Half-Sibling Regression Proceedings of the National Academy of Science, 113(27):7391-7398, 2016 (article)

Code link (url) DOI Project Page [BibTex]

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Dual Control for Approximate Bayesian Reinforcement Learning

Klenske, E. D., Hennig, P.

Journal of Machine Learning Research, 17(127):1-30, 2016 (article)

PDF link (url) [BibTex]

Klenske, E. D., Hennig, P. Dual Control for Approximate Bayesian Reinforcement Learning Journal of Machine Learning Research, 17(127):1-30, 2016 (article)

PDF link (url) [BibTex]

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A Population Based Gaussian Mixture Model Incorporating 18F-FDG-PET and DW-MRI Quantifies Tumor Tissue Classes

Divine, M. R., Katiyar, P., Kohlhofer, U., Quintanilla-Martinez, L., Disselhorst, J. A., Pichler, B. J.

Journal of Nuclear Medicine, 57(3):473-479, 2016 (article)

DOI [BibTex]

Divine, M. R., Katiyar, P., Kohlhofer, U., Quintanilla-Martinez, L., Disselhorst, J. A., Pichler, B. J. A Population Based Gaussian Mixture Model Incorporating 18F-FDG-PET and DW-MRI Quantifies Tumor Tissue Classes Journal of Nuclear Medicine, 57(3):473-479, 2016 (article)

DOI [BibTex]

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Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

Schütt, H. H., Harmeling, S., Macke, J. H., Wichmann, F. A.

Vision Research, 122, pages: 105-123, 2016 (article)

link (url) DOI Project Page [BibTex]

Schütt, H. H., Harmeling, S., Macke, J. H., Wichmann, F. A. Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data Vision Research, 122, pages: 105-123, 2016 (article)

link (url) DOI Project Page [BibTex]

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Hierarchical Relative Entropy Policy Search

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

Journal of Machine Learning Research, 17(93):1-50, 2016 (article)

link (url) Project Page [BibTex]

Daniel, C., Neumann, G., Kroemer, O., Peters, J. Hierarchical Relative Entropy Policy Search Journal of Machine Learning Research, 17(93):1-50, 2016 (article)

link (url) Project Page [BibTex]

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Kernel Mean Shrinkage Estimators

Muandet, K., Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B.

Journal of Machine Learning Research, 17(48):1-41, 2016 (article)

link (url) [BibTex]

Muandet, K., Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B. Kernel Mean Shrinkage Estimators Journal of Machine Learning Research, 17(48):1-41, 2016 (article)

link (url) [BibTex]

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Learning to Deblur

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

IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)

DOI [BibTex]

Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B. Learning to Deblur IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)

DOI [BibTex]

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Transfer Learning in Brain-Computer Interfaces

Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M.

IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (article)

PDF DOI [BibTex]

Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M. Transfer Learning in Brain-Computer Interfaces IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (article)

PDF DOI [BibTex]

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MERLiN: Mixture Effect Recovery in Linear Networks

Weichwald, S., Grosse-Wentrup, M., Gretton, A.

IEEE Journal of Selected Topics in Signal Processing, 10(7):1254-1266, 2016 (article)

Arxiv Code PDF DOI Project Page [BibTex]

Weichwald, S., Grosse-Wentrup, M., Gretton, A. MERLiN: Mixture Effect Recovery in Linear Networks IEEE Journal of Selected Topics in Signal Processing, 10(7):1254-1266, 2016 (article)

Arxiv Code PDF DOI Project Page [BibTex]

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Causal inference using invariant prediction: identification and confidence intervals

Peters, J., Bühlmann, P., Meinshausen, N.

Journal of the Royal Statistical Society, Series B (Statistical Methodology), 78(5):947-1012, 2016, (with discussion) (article)

link (url) DOI [BibTex]

Peters, J., Bühlmann, P., Meinshausen, N. Causal inference using invariant prediction: identification and confidence intervals Journal of the Royal Statistical Society, Series B (Statistical Methodology), 78(5):947-1012, 2016, (with discussion) (article)

link (url) DOI [BibTex]

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Causal discovery and inference: concepts and recent methodological advances

Spirtes, P., Zhang, K.

Applied Informatics, 3(3):1-28, 2016 (article)

DOI [BibTex]

Spirtes, P., Zhang, K. Causal discovery and inference: concepts and recent methodological advances Applied Informatics, 3(3):1-28, 2016 (article)

DOI [BibTex]

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Influence Estimation and Maximization in Continuous-Time Diffusion Networks

Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B.

ACM Transactions on Information Systems, 34(2):9:1-9:33, 2016 (article)

DOI Project Page Project Page [BibTex]

Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B. Influence Estimation and Maximization in Continuous-Time Diffusion Networks ACM Transactions on Information Systems, 34(2):9:1-9:33, 2016 (article)

DOI Project Page Project Page [BibTex]

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The population of long-period transiting exoplanets

Foreman-Mackey, D., Morton, T. D., Hogg, D. W., Agol, E., Schölkopf, B.

The Astronomical Journal, 152(6):206, 2016 (article)

link (url) Project Page [BibTex]

Foreman-Mackey, D., Morton, T. D., Hogg, D. W., Agol, E., Schölkopf, B. The population of long-period transiting exoplanets The Astronomical Journal, 152(6):206, 2016 (article)

link (url) Project Page [BibTex]

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Screening Rules for Convex Problems

Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M.

2016 (unpublished) Submitted

[BibTex]

Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M. Screening Rules for Convex Problems 2016 (unpublished) Submitted

[BibTex]

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An overview of quantitative approaches in Gestalt perception

Jäkel, F., Singh, M., Wichmann, F. A., Herzog, M. H.

Vision Research, 126, pages: 3-8, 2016 (article)

link (url) DOI Project Page [BibTex]

Jäkel, F., Singh, M., Wichmann, F. A., Herzog, M. H. An overview of quantitative approaches in Gestalt perception Vision Research, 126, pages: 3-8, 2016 (article)

link (url) DOI Project Page [BibTex]

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Bootstrat: Population Informed Bootstrapping for Rare Variant Tests

Huang, H., Peloso, G. M., Howrigan, D., Rakitsch, B., Simon-Gabriel, C. J., Goldstein, J. I., Daly, M. J., Borgwardt, K., Neale, B. M.

bioRxiv, 2016, preprint (article)

link (url) DOI [BibTex]

Huang, H., Peloso, G. M., Howrigan, D., Rakitsch, B., Simon-Gabriel, C. J., Goldstein, J. I., Daly, M. J., Borgwardt, K., Neale, B. M. Bootstrat: Population Informed Bootstrapping for Rare Variant Tests bioRxiv, 2016, preprint (article)

link (url) DOI [BibTex]

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Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control

Rueckert, E., Camernik, J., Peters, J., Babic, J.

Nature PG: Scientific Reports, 6(Article number: 28455), 2016 (article)

DOI Project Page [BibTex]

Rueckert, E., Camernik, J., Peters, J., Babic, J. Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Nature PG: Scientific Reports, 6(Article number: 28455), 2016 (article)

DOI Project Page [BibTex]

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Learning Taxonomy Adaptation in Large-scale Classification

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

Journal of Machine Learning Research, 17(98):1-37, 2016 (article)

link (url) Project Page [BibTex]

Babbar, R., Partalas, I., Gaussier, E., Amini, M., Amblard, C. Learning Taxonomy Adaptation in Large-scale Classification Journal of Machine Learning Research, 17(98):1-37, 2016 (article)

link (url) Project Page [BibTex]

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BOiS—Berlin Object in Scene Database: Controlled Photographic Images for Visual Search Experiments with Quantified Contextual Priors

Mohr, J., Seyfarth, J., Lueschow, A., Weber, J. E., Wichmann, F. A., Obermayer, K.

Frontiers in Psychology, 2016 (article)

DOI [BibTex]

Mohr, J., Seyfarth, J., Lueschow, A., Weber, J. E., Wichmann, F. A., Obermayer, K. BOiS—Berlin Object in Scene Database: Controlled Photographic Images for Visual Search Experiments with Quantified Contextual Priors Frontiers in Psychology, 2016 (article)

DOI [BibTex]

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Preface to the ACM TIST Special Issue on Causal Discovery and Inference

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J.

ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 17, 2016 (article)

DOI [BibTex]

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J. Preface to the ACM TIST Special Issue on Causal Discovery and Inference ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 17, 2016 (article)

DOI [BibTex]

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Recurrent Spiking Networks Solve Planning Tasks

Rueckert, E., Kappel, D., Tanneberg, D., Pecevski, D., Peters, J.

Nature PG: Scientific Reports, 6(Article number: 21142), 2016 (article)

DOI Project Page [BibTex]

Rueckert, E., Kappel, D., Tanneberg, D., Pecevski, D., Peters, J. Recurrent Spiking Networks Solve Planning Tasks Nature PG: Scientific Reports, 6(Article number: 21142), 2016 (article)

DOI Project Page [BibTex]

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Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations

Genewein, T, Braun, DA

Biological Cybernetics, 110(2):135–150, June 2016 (article)

Abstract
Bayesian inference and bounded rational decision-making require the accumulation of evidence or utility, respectively, to transform a prior belief or strategy into a posterior probability distribution over hypotheses or actions. Crucially, this process cannot be simply realized by independent integrators, since the different hypotheses and actions also compete with each other. In continuous time, this competitive integration process can be described by a special case of the replicator equation. Here we investigate simple analog electric circuits that implement the underlying differential equation under the constraint that we only permit a limited set of building blocks that we regard as biologically interpretable, such as capacitors, resistors, voltage-dependent conductances and voltage- or current-controlled current and voltage sources. The appeal of these circuits is that they intrinsically perform normalization without requiring an explicit divisive normalization. However, even in idealized simulations, we find that these circuits are very sensitive to internal noise as they accumulate error over time. We discuss in how far neural circuits could implement these operations that might provide a generic competitive principle underlying both perception and action.

DOI [BibTex]

Genewein, T, Braun, DA Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations Biological Cybernetics, 110(2):135–150, June 2016 (article)

DOI [BibTex]

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Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context: Experiments and an Information-Theoretic Ambiguity Model

Grau-Moya, J, Ortega, PA, Braun, DA

PLoS ONE, 11(4):1-21, April 2016 (article)

Abstract
A number of recent studies have investigated differences in human choice behavior depending on task framing, especially comparing economic decision-making to choice behavior in equivalent sensorimotor tasks. Here we test whether decision-making under ambiguity exhibits effects of task framing in motor vs. non-motor context. In a first experiment, we designed an experience-based urn task with varying degrees of ambiguity and an equivalent motor task where subjects chose between hitting partially occluded targets. In a second experiment, we controlled for the different stimulus design in the two tasks by introducing an urn task with bar stimuli matching those in the motor task. We found ambiguity attitudes to be mainly influenced by stimulus design. In particular, we found that the same subjects tended to be ambiguity-preferring when choosing between ambiguous bar stimuli, but ambiguity-avoiding when choosing between ambiguous urn sample stimuli. In contrast, subjects’ choice pattern was not affected by changing from a target hitting task to a non-motor context when keeping the stimulus design unchanged. In both tasks subjects’ choice behavior was continuously modulated by the degree of ambiguity. We show that this modulation of behavior can be explained by an information-theoretic model of ambiguity that generalizes Bayes-optimal decision-making by combining Bayesian inference with robust decision-making under model uncertainty. Our results demonstrate the benefits of information-theoretic models of decision-making under varying degrees of ambiguity for a given context, but also demonstrate the sensitivity of ambiguity attitudes across contexts that theoretical models struggle to explain.

DOI [BibTex]

2013

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Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System

Schmidt, H., Brendle, C., Schraml, C., Martirosian, P., Bezrukov, I., Hetzel, J., Müller, M., Sauter, A., Claussen, C., Pfannenberg, C., Schwenzer, N.

Investigative Radiology, 48(5):247-255, May 2013 (article)

Web [BibTex]

2013

Schmidt, H., Brendle, C., Schraml, C., Martirosian, P., Bezrukov, I., Hetzel, J., Müller, M., Sauter, A., Claussen, C., Pfannenberg, C., Schwenzer, N. Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System Investigative Radiology, 48(5):247-255, May 2013 (article)

Web [BibTex]

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Replacing Causal Faithfulness with Algorithmic Independence of Conditionals

Lemeire, J., Janzing, D.

Minds and Machines, 23(2):227-249, May 2013 (article)

Abstract
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms.

PDF Web DOI [BibTex]

Lemeire, J., Janzing, D. Replacing Causal Faithfulness with Algorithmic Independence of Conditionals Minds and Machines, 23(2):227-249, May 2013 (article)

PDF Web DOI [BibTex]

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What can neurons do for their brain? Communicate selectivity with bursts

Balduzzi, D., Tononi, G.

Theory in Biosciences , 132(1):27-39, Springer, March 2013 (article)

Abstract
Neurons deep in cortex interact with the environment extremely indirectly; the spikes they receive and produce are pre- and post-processed by millions of other neurons. This paper proposes two information-theoretic constraints guiding the production of spikes, that help ensure bursting activity deep in cortex relates meaningfully to events in the environment. First, neurons should emphasize selective responses with bursts. Second, neurons should propagate selective inputs by burst-firing in response to them. We show the constraints are necessary for bursts to dominate information-transfer within cortex, thereby providing a substrate allowing neurons to distribute credit amongst themselves. Finally, since synaptic plasticity degrades the ability of neurons to burst selectively, we argue that homeostatic regulation of synaptic weights is necessary, and that it is best performed offline during sleep.

PDF Web DOI [BibTex]

Balduzzi, D., Tononi, G. What can neurons do for their brain? Communicate selectivity with bursts Theory in Biosciences , 132(1):27-39, Springer, March 2013 (article)

PDF Web DOI [BibTex]

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Apprenticeship Learning with Few Examples

Boularias, A., Chaib-draa, B.

Neurocomputing, 104, pages: 83-96, March 2013 (article)

Abstract
We consider the problem of imitation learning when the examples, provided by an expert human, are scarce. Apprenticeship learning via inverse reinforcement learning provides an efficient tool for generalizing the examples, based on the assumption that the expert's policy maximizes a value function, which is a linear combination of state and action features. Most apprenticeship learning algorithms use only simple empirical averages of the features in the demonstrations as a statistics of the expert's policy. However, this method is efficient only when the number of examples is sufficiently large to cover most of the states, or the dynamics of the system is nearly deterministic. In this paper, we show that the quality of the learned policies is sensitive to the error in estimating the averages of the features when the dynamics of the system is stochastic. To reduce this error, we introduce two new approaches for bootstrapping the demonstrations by assuming that the expert is near-optimal and the dynamics of the system is known. In the first approach, the expert's examples are used to learn a reward function and to generate furthermore examples from the corresponding optimal policy. The second approach uses a transfer technique, known as graph homomorphism, in order to generalize the expert's actions to unvisited regions of the state space. Empirical results on simulated robot navigation problems show that our approach is able to learn sufficiently good policies from a significantly small number of examples.

Web DOI [BibTex]

Boularias, A., Chaib-draa, B. Apprenticeship Learning with Few Examples Neurocomputing, 104, pages: 83-96, March 2013 (article)

Web DOI [BibTex]

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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

Abstract
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

website+code pdf link (url) [BibTex]

Hennig, P., Kiefel, M. Quasi-Newton Methods: A New Direction Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

website+code pdf link (url) [BibTex]

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Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling

Cavusoglu, M., Pohmann, R., Burger, H. C., Uludag, K.

Magnetic Resonance in Medicine, 69(2):524-530, Febuary 2013 (article)

Abstract
Most experiments assume a global transit delay time with blood flowing from the tagging region to the imaging slice in plug flow without any dispersion of the magnetization. However, because of cardiac pulsation, nonuniform cross-sectional flow profile, and complex vessel networks, the transit delay time is not a single value but follows a distribution. In this study, we explored the regional effects of magnetization dispersion on quantitative perfusion imaging for varying transit times within a very large interval from the direct comparison of pulsed, pseudo-continuous, and dual-coil continuous arterial spin labeling encoding schemes. Longer distances between tagging and imaging region typically used for continuous tagging schemes enhance the regional bias on the quantitative cerebral blood flow measurement causing an underestimation up to 37% when plug flow is assumed as in the standard model.

Web DOI [BibTex]

Cavusoglu, M., Pohmann, R., Burger, H. C., Uludag, K. Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling Magnetic Resonance in Medicine, 69(2):524-530, Febuary 2013 (article)

Web DOI [BibTex]

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The multivariate Watson distribution: Maximum-likelihood estimation and other aspects

Sra, S., Karp, D.

Journal of Multivariate Analysis, 114, pages: 256-269, February 2013 (article)

Abstract
This paper studies fundamental aspects of modelling data using multivariate Watson distributions. Although these distributions are natural for modelling axially symmetric data (i.e., unit vectors where View the MathML source are equivalent), for high-dimensions using them can be difficult—largely because for Watson distributions even basic tasks such as maximum-likelihood are numerically challenging. To tackle the numerical difficulties some approximations have been derived. But these are either grossly inaccurate in high-dimensions [K.V. Mardia, P. Jupp, Directional Statistics, second ed., John Wiley & Sons, 2000] or when reasonably accurate [A. Bijral, M. Breitenbach, G.Z. Grudic, Mixture of Watson distributions: a generative model for hyperspherical embeddings, in: Artificial Intelligence and Statistics, AISTATS 2007, 2007, pp. 35–42], they lack theoretical justification. We derive new approximations to the maximum-likelihood estimates; our approximations are theoretically well-defined, numerically accurate, and easy to compute. We build on our parameter estimation and discuss mixture-modelling with Watson distributions; here we uncover a hitherto unknown connection to the “diametrical clustering” algorithm of Dhillon et al. [I.S. Dhillon, E.M. Marcotte, U. Roshan, Diametrical clustering for identifying anticorrelated gene clusters, Bioinformatics 19 (13) (2003) 1612–1619].

Web DOI [BibTex]

Sra, S., Karp, D. The multivariate Watson distribution: Maximum-likelihood estimation and other aspects Journal of Multivariate Analysis, 114, pages: 256-269, February 2013 (article)

Web DOI [BibTex]

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How the result of graph clustering methods depends on the construction of the graph

Maier, M., von Luxburg, U., Hein, M.

ESAIM: Probability & Statistics, 17, pages: 370-418, January 2013 (article)

Abstract
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one rst has to construct a graph on the data points and then apply a graph clustering algorithm to nd a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) in uences the outcome of the nal clustering result. To this end we study the convergence of cluster quality measures such as the normalized cut or the Cheeger cut on various kinds of random geometric graphs as the sample size tends to in nity. It turns out that the limit values of the same objective function are systematically di erent on di erent types of graphs. This implies that clustering results systematically depend on the graph and can be very di erent for di erent types of graph. We provide examples to illustrate the implications on spectral clustering.

PDF DOI [BibTex]

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Explicit eigenvalues of certain scaled trigonometric matrices

Sra, S.

Linear Algebra and its Applications, 438(1):173-181, January 2013 (article)

DOI [BibTex]

Sra, S. Explicit eigenvalues of certain scaled trigonometric matrices Linear Algebra and its Applications, 438(1):173-181, January 2013 (article)

DOI [BibTex]

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How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?

Gerhard, H., Wichmann, F., Bethge, M.

PLoS Computational Biology, 9(1):e1002873, January 2013 (article)

Abstract
Several aspects of primate visual physiology have been identified as adaptations to local regularities of natural images. However, much less work has measured visual sensitivity to local natural image regularities. Most previous work focuses on global perception of large images and shows that observers are more sensitive to visual information when image properties resemble those of natural images. In this work we measure human sensitivity to local natural image regularities using stimuli generated by patch-based probabilistic natural image models that have been related to primate visual physiology. We find that human observers can learn to discriminate the statistical regularities of natural image patches from those represented by current natural image models after very few exposures and that discriminability depends on the degree of regularities captured by the model. The quick learning we observed suggests that the human visual system is biased for processing natural images, even at very fine spatial scales, and that it has a surprisingly large knowledge of the regularities in natural images, at least in comparison to the state-of-the-art statistical models of natural images.

DOI [BibTex]

Gerhard, H., Wichmann, F., Bethge, M. How Sensitive Is the Human Visual System to the Local Statistics of Natural Images? PLoS Computational Biology, 9(1):e1002873, January 2013 (article)

DOI [BibTex]

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A neural population model for visual pattern detection

Goris, R., Putzeys, T., Wagemans, J., Wichmann, F.

Psychological Review, 120(3):472–496, 2013 (article)

DOI [BibTex]

Goris, R., Putzeys, T., Wagemans, J., Wichmann, F. A neural population model for visual pattern detection Psychological Review, 120(3):472–496, 2013 (article)

DOI [BibTex]

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Accurate indel prediction using paired-end short reads

Grimm, D., Hagmann, J., Koenig, D., Weigel, D., Borgwardt, KM.

BMC Genomics, 14(132), 2013 (article)

Web DOI [BibTex]

Grimm, D., Hagmann, J., Koenig, D., Weigel, D., Borgwardt, KM. Accurate indel prediction using paired-end short reads BMC Genomics, 14(132), 2013 (article)

Web DOI [BibTex]