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31 results (View BibTeX file of all listed publications)

2013

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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,651 (misc)

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[BibTex]

2013

pi Sitti, M., Kim, S. Dry adhesives and methods for making dry adhesives sep 2013, US Patent App. 14/016,651 (misc)

[BibTex]

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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,683 (misc)

pi

[BibTex]

pi Sitti, M., Kim, S. Dry adhesives and methods for making dry adhesives sep 2013, US Patent App. 14/016,683 (misc)

[BibTex]

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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent 8,524,092 (misc)

pi

[BibTex]

pi Sitti, M., Kim, S. Dry adhesives and methods for making dry adhesives sep 2013, US Patent 8,524,092 (misc)

[BibTex]

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Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

ei

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ei Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A. Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

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Dry adhesives and methods of making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2013, US Patent App. 13/845,702 (misc)

pi

[BibTex]

pi Sitti, M., Murphy, M., Aksak, B. Dry adhesives and methods of making dry adhesives March 2013, US Patent App. 13/845,702 (misc)

[BibTex]

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Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

ei

Web [BibTex]

ei Deisenroth, M., Szepesvári, C., Peters, J. Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24 pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

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Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

ei

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ei Muandet, K. Domain Generalization via Invariant Feature Representation 30th International Conference on Machine Learning (ICML2013), 2013 (talk)

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2008

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BCPy2000

Hill, N., Schreiner, T., Puzicha, C., Farquhar, J.

Workshop "Machine Learning Open-Source Software" at NIPS, December 2008 (talk)

ei

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2008

ei Hill, N., Schreiner, T., Puzicha, C., Farquhar, J. BCPy2000 Workshop "Machine Learning Open-Source Software" at NIPS, December 2008 (talk)

Web [BibTex]

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Logistic Regression for Graph Classification

Shervashidze, N., Tsuda, K.

NIPS Workshop on "Structured Input - Structured Output" (NIPS SISO), December 2008 (talk)

Abstract
In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression for graphs, which is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics.

ei

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ei Shervashidze, N., Tsuda, K. Logistic Regression for Graph Classification NIPS Workshop on "Structured Input - Structured Output" (NIPS SISO), December 2008 (talk)

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New Projected Quasi-Newton Methods with Applications

Sra, S.

Microsoft Research Tech-talk, December 2008 (talk)

Abstract
Box-constrained convex optimization problems are central to several applications in a variety of fields such as statistics, psychometrics, signal processing, medical imaging, and machine learning. Two fundamental examples are the non-negative least squares (NNLS) problem and the non-negative Kullback-Leibler (NNKL) divergence minimization problem. The non-negativity constraints are usually based on an underlying physical restriction, for e.g., when dealing with applications in astronomy, tomography, statistical estimation, or image restoration, the underlying parameters represent physical quantities such as concentration, weight, intensity, or frequency counts and are therefore only interpretable with non-negative values. Several modern optimization methods can be inefficient for simple problems such as NNLS and NNKL as they are really designed to handle far more general and complex problems. In this work we develop two simple quasi-Newton methods for solving box-constrained (differentiable) convex optimization problems that utilize the well-known BFGS and limited memory BFGS updates. We position our method between projected gradient (Rosen, 1960) and projected Newton (Bertsekas, 1982) methods, and prove its convergence under a simple Armijo step-size rule. We illustrate our method by showing applications to: Image deblurring, Positron Emission Tomography (PET) image reconstruction, and Non-negative Matrix Approximation (NMA). On medium sized data we observe performance competitive to established procedures, while for larger data the results are even better.

ei

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ei Sra, S. New Projected Quasi-Newton Methods with Applications Microsoft Research Tech-talk, December 2008 (talk)

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MR-Based PET Attenuation Correction: Initial Results for Whole Body

Hofmann, M., Steinke, F., Aschoff, P., Lichy, M., Brady, M., Schölkopf, B., Pichler, B.

Medical Imaging Conference, October 2008 (talk)

ei

[BibTex]

ei Hofmann, M., Steinke, F., Aschoff, P., Lichy, M., Brady, M., Schölkopf, B., Pichler, B. MR-Based PET Attenuation Correction: Initial Results for Whole Body Medical Imaging Conference, October 2008 (talk)

[BibTex]

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Nonparametric Indepedence Tests: Space Partitioning and Kernel Approaches

Gretton, A., Györfi, L.

19th International Conference on Algorithmic Learning Theory (ALT08), October 2008 (talk)

ei

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ei Gretton, A., Györfi, L. Nonparametric Indepedence Tests: Space Partitioning and Kernel Approaches 19th International Conference on Algorithmic Learning Theory (ALT08), October 2008 (talk)

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mGene: A Novel Discriminative Gene Finder

Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G.

Worm Genomics and Systems Biology meeting, July 2008 (talk)

ei

[BibTex]

ei Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G. mGene: A Novel Discriminative Gene Finder Worm Genomics and Systems Biology meeting, July 2008 (talk)

[BibTex]

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Discovering Common Sequence Variation in Arabidopsis thaliana

Rätsch, G., Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthman, N., Hu, T., Fu, G., Hinds, D., Cheng, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D., Schneeberger, K., Bohlen, A.

16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB), July 2008 (talk)

ei

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ei Rätsch, G., Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthman, N., Hu, T., Fu, G., Hinds, D., Cheng, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D., Schneeberger, K., Bohlen, A. Discovering Common Sequence Variation in Arabidopsis thaliana 16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB), July 2008 (talk)

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CogRob 2008: The 6th International Cognitive Robotics Workshop

Lespérance, Y., Lakemeyer, G., Peters, J., Pirri, F.

Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), pages: 35, Patras University Press, Patras, Greece, 6th International Cognitive Robotics Workshop (CogRob), July 2008 (proceedings)

ei

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ei Lespérance, Y., Lakemeyer, G., Peters, J., Pirri, F. CogRob 2008: The 6th International Cognitive Robotics Workshop Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), pages: 35, Patras University Press, Patras, Greece, 6th International Cognitive Robotics Workshop (CogRob), July 2008 (proceedings)

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Coding Theory in Brain-Computer Interfaces

Martens, SMM.

Soria Summerschool on Computational Mathematics "Algebraic Coding Theory" (S3CM), July 2008 (talk)

ei

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ei Martens, SMM. Coding Theory in Brain-Computer Interfaces Soria Summerschool on Computational Mathematics "Algebraic Coding Theory" (S3CM), July 2008 (talk)

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Motor Skill Learning for Cognitive Robotics

Peters, J.

6th International Cognitive Robotics Workshop (CogRob), July 2008 (talk)

Abstract
Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this tutorial, we give a general overview on motor skill learning for cognitive robotics using research at ATR, USC, CMU and Max-Planck in order to illustrate the problems in motor skill learning. For doing so, we discuss task-appropriate representations and algorithms for learning robot motor skills. Among the topics are the learning basic movements or motor primitives by imitation and reinforcement learning, learning rhytmic and discrete movements, fast regression methods for learning inverse dynamics and setups for learning task-space policies. Examples on various robots, e.g., SARCOS DB, the SARCOS Master Arm, BDI Little Dog and a Barrett WAM, are shown and include Ball-in-a-Cup, T-Ball, Juggling, Devil-Sticking, Operational Space Control and many others.

ei

Web [BibTex]

ei Peters, J. Motor Skill Learning for Cognitive Robotics 6th International Cognitive Robotics Workshop (CogRob), July 2008 (talk)

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Painless Embeddings of Distributions: the Function Space View (Part 1)

Fukumizu, K., Gretton, A., Smola, A.

25th International Conference on Machine Learning (ICML), July 2008 (talk)

Abstract
This tutorial will give an introduction to the recent understanding and methodology of the kernel method: dealing with higher order statistics by embedding painlessly random variables/probability distributions. In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear algorithms from linear ones. More recently, however, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics by embedding distributions in a suitable reproducing kernel Hilbert space (RKHS). Notably, unlike the straightforward expansion of higher order moments or conventional characteristic function approach, the use of kernels or RKHS provides a painless, tractable way of embedding distributions. This line of reasoning leads naturally to the questions: what does it mean to embed a distribution in an RKHS? when is this embedding injective (and thus, when do different distributions have unique mappings)? what implications are there for learning algorithms that make use of these embeddings? This tutorial aims at answering these questions. There are a great variety of applications in machine learning and computer science, which require distribution estimation and/or comparison.

ei

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ei Fukumizu, K., Gretton, A., Smola, A. Painless Embeddings of Distributions: the Function Space View (Part 1) 25th International Conference on Machine Learning (ICML), July 2008 (talk)

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Reinforcement Learning for Robotics

Peters, J.

8th European Workshop on Reinforcement Learning for Robotics (EWRL), July 2008 (talk)

ei

Web [BibTex]

ei Peters, J. Reinforcement Learning for Robotics 8th European Workshop on Reinforcement Learning for Robotics (EWRL), July 2008 (talk)

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Thin-Plate Splines Between Riemannian Manifolds

Steinke, F., Hein, M., Schölkopf, B.

Workshop on Geometry and Statistics of Shapes, June 2008 (talk)

Abstract
With the help of differential geometry we describe a framework to define a thin-plate spline like energy for maps between arbitrary Riemannian manifolds. The so-called Eells energy only depends on the intrinsic geometry of the input and output manifold, but not on their respective representation. The energy can then be used for regression between manifolds, we present results for cases where the outputs are rotations, sets of angles, or points on 3D surfaces. In the future we plan to also target regression where the output is an element of "shape space", understood as a Riemannian manifold. One could also further explore the meaning of the Eells energy when applied to diffeomorphisms between shapes, especially with regard to its potential use as a distance measure between shapes that does not depend on the embedding or the parametrisation of the shapes.

ei

Web [BibTex]

ei Steinke, F., Hein, M., Schölkopf, B. Thin-Plate Splines Between Riemannian Manifolds Workshop on Geometry and Statistics of Shapes, June 2008 (talk)

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Learning resolved velocity control

Peters, J.

2008 IEEE International Conference on Robotics and Automation (ICRA), May 2008 (talk)

ei

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ei Peters, J. Learning resolved velocity control 2008 IEEE International Conference on Robotics and Automation (ICRA), May 2008 (talk)

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Bayesian methods for protein structure determination

Habeck, M.

Machine Learning in Structural Bioinformatics, April 2008 (talk)

ei

Web [BibTex]

ei Habeck, M. Bayesian methods for protein structure determination Machine Learning in Structural Bioinformatics, April 2008 (talk)

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2005

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Some thoughts about Gaussian Processes

Chapelle, O.

NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

ei

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2005

ei Chapelle, O. Some thoughts about Gaussian Processes NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

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Building Sparse Large Margin Classifiers

Wu, M., Schölkopf, B., BakIr, G.

The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

ei

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ei Wu, M., Schölkopf, B., BakIr, G. Building Sparse Large Margin Classifiers The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

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Learning from Labeled and Unlabeled Data on a Directed Graph

Zhou, D.

The 22nd International Conference on Machine Learning, August 2005 (talk)

Abstract
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.

ei

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ei Zhou, D. Learning from Labeled and Unlabeled Data on a Directed Graph The 22nd International Conference on Machine Learning, August 2005 (talk)

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Machine-Learning Approaches to BCI in Tübingen

Bensch, M., Bogdan, M., Hill, N., Lal, T., Rosenstiel, W., Schölkopf, B., Schröder, M.

Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

ei

[BibTex]

ei Bensch, M., Bogdan, M., Hill, N., Lal, T., Rosenstiel, W., Schölkopf, B., Schröder, M. Machine-Learning Approaches to BCI in Tübingen Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

[BibTex]

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Adhesive microstructure and method of forming same

Fearing, R. S., Sitti, M.

March 2005, US Patent 6,872,439 (misc)

pi

[BibTex]

pi Fearing, R. S., Sitti, M. Adhesive microstructure and method of forming same March 2005, US Patent 6,872,439 (misc)

[BibTex]

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Kernel Constrained Covariance for Dependence Measurement

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Schölkopf, B., Logothetis, N.

AISTATS, January 2005 (talk)

Abstract
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables. We show that COCO is a test for independence if and only if the associated RKHSs are universal. That said, no independence test exists that can distinguish dependent and independent random variables in all circumstances. Dependent random variables can result in a COCO which is arbitrarily close to zero when the source densities are highly non-smooth. All current kernel-based independence tests share this behaviour. We demonstrate exponential convergence between the population and empirical COCO. Finally, we use COCO as a measure of joint neural activity between voxels in MRI recordings of the macaque monkey, and compare the results to the mutual information and the correlation. We also show the effect of removing breathing artefacts from the MRI recording.

ei

PostScript [BibTex]

ei Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Schölkopf, B., Logothetis, N. Kernel Constrained Covariance for Dependence Measurement AISTATS, January 2005 (talk)

PostScript [BibTex]