Publications | Max Planck Institute for Intelligent Systems

53 results (View BibTeX file of all listed publications)

2012

Support Vector Machines, Support Measure Machines, and Quasar Target Selection

Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

2012

ei Muandet, K. Support Vector Machines, Support Measure Machines, and Quasar Target Selection Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

[BibTex]

Hilbert Space Embedding for Dirichlet Process Mixtures

Muandet, K.

NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)

[BibTex]

ei Muandet, K. Hilbert Space Embedding for Dirichlet Process Mixtures NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)

[BibTex]

Coregistration: Supplemental Material

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J.

(No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

ps Hirshberg, D., Loper, M., Rachlin, E., Black, M. J. Coregistration: Supplemental Material (No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material

Freifeld, O., Black, M. J.

(No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

ps Freifeld, O., Black, M. J. Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material (No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

MPI-Sintel Optical Flow Benchmark: Supplemental Material

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

(No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

ps Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J. MPI-Sintel Optical Flow Benchmark: Supplemental Material (No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

Simultaneous small animal PET/MR in activated and resting state reveals multiple brain networks

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

20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

Web [BibTex]

ei Wehrl, H., Lankes, K., Hossain, M., Bezrukov, I., Liu, C., Martirosian, P., Schick, F., Pichler, B. Simultaneous small animal PET/MR in activated and resting state reveals multiple brain networks 20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

Web [BibTex]

A new PET insert for simultaneous PET/MR small animal imaging

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

20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

Web [BibTex]

ei Wehrl, H., Lankes, K., Hossain, M., Bezrukov, I., Liu, C., Martirosian, P., Reischl, G., Schick, F., Pichler, B. A new PET insert for simultaneous PET/MR small animal imaging 20th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2012 (talk)

Web [BibTex]

Evaluation of a new, large field of view, small animal PET/MR system

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

50. Jahrestagung der Deutschen Gesellschaft fuer Nuklearmedizin (NuklearMedizin), April 2012 (talk)

Web [BibTex]

ei Hossain, M., Wehrl, H., Lankes, K., Liu, C., Bezrukov, I., Reischl, G., Pichler, B. Evaluation of a new, large field of view, small animal PET/MR system 50. Jahrestagung der Deutschen Gesellschaft fuer Nuklearmedizin (NuklearMedizin), April 2012 (talk)

Web [BibTex]

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]

ei Grosse-Wentrup, M., Schölkopf, B. High Gamma-Power Predicts Performance in Brain-Computer Interfacing (3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

PDF [BibTex]

HUMIM Software for Articulated Tracking

Soren Hauberg, Kim S. Pedersen

(01/2012), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

Code PDF [BibTex]

ps Soren Hauberg, Kim S. Pedersen HUMIM Software for Articulated Tracking (01/2012), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

Code PDF [BibTex]

A geometric framework for statistics on trees

Aasa Feragen, Mads Nielsen, Soren Hauberg, Pechin Lo, Marleen de Bruijne, Francois Lauze

(11/02), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

PDF [BibTex]

ps Aasa Feragen, Mads Nielsen, Soren Hauberg, Pechin Lo, Marleen de Bruijne, Francois Lauze A geometric framework for statistics on trees (11/02), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

PDF [BibTex]

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]

ei Wehrl, H., Hossain, M., Lankes, K., Liu, C., Bezrukov, I., Martirosian, P., Reischl, G., Schick, F., Pichler, B. Simultaneous small animal PET/MR reveals different brain networks during stimulation and rest World Molecular Imaging Congress (WMIC), 2012 (talk)

[BibTex]

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]

ei Muandet, K. Support Measure Machines for Quasar Target Selection Astro Imaging Workshop, 2012 (talk)

Web [BibTex]

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]

ei Seldin, Y. PAC-Bayesian Analysis: A Link Between Inference and Statistical Physics Workshop on Statistical Physics of Inference and Control Theory, 2012 (talk)

Web [BibTex]

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]

ei Liu, C., Hossain, M., Lankes, K., Bezrukov, I., Wehrl, H., Kolb, A., Judenhofer, M., Pichler, B. PET Performance Measurements of a Next Generation Dedicated Small Animal PET/MR Scanner Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), 2012 (talk)

[BibTex]

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]

ei Seldin, Y., Laviolette, F., Shawe-Taylor, J. PAC-Bayesian Analysis of Supervised, Unsupervised, and Reinforcement Learning Tutorial at the 29th International Conference on Machine Learning (ICML), 2012 (talk)

Web Web [BibTex]

Influence of MR-based attenuation correction on lesions within bone and susceptibility artifact regions

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Pichler, B.

Molekulare Bildgebung (MoBi), 2012 (talk)

[BibTex]

ei Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Pichler, B. Influence of MR-based attenuation correction on lesions within bone and susceptibility artifact regions Molekulare Bildgebung (MoBi), 2012 (talk)

[BibTex]

Structured Apprenticeship Learning

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

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

[BibTex]

ei Boularias, A., Kroemer, O., Peters, J. Structured Apprenticeship Learning European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

[BibTex]

PAC-Bayesian Analysis and Its Applications

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

Tutorial at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (talk)

Web [BibTex]

ei Seldin, Y., Laviolette, F., Shawe-Taylor, J. PAC-Bayesian Analysis and Its Applications Tutorial at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (talk)

Web [BibTex]

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

Deisenroth, M., Peters, J.

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

[BibTex]

ei Deisenroth, M., Peters, J. Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

[BibTex]

Kernel Bellman Equations in POMDPs

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

Technical Committee on Infomation-Based Induction Sciences and Machine Learning (IBISML'12), 2012 (talk)

[BibTex]

ei Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K. Kernel Bellman Equations in POMDPs Technical Committee on Infomation-Based Induction Sciences and Machine Learning (IBISML'12), 2012 (talk)

[BibTex]

Beta oscillations propagate as traveling waves in the macaque prefrontal cortex

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

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

[BibTex]

ei Panagiotaropoulos, T., Besserve, M., Logothetis, N. Beta oscillations propagate as traveling waves in the macaque prefrontal cortex 42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (talk)

[BibTex]

The Playful Machine - Theoretical Foundation and Practical Realization of Self-Organizing Robots

Der, R., Martius, G.

Springer, Berlin Heidelberg, 2012 (book)

Abstract

Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complex machines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? The book provides answers by developing a general principle---homeokinesis, the dynamical symbiosis between brain, body, and environment---that is shown to drive robots to self-determined, individual development in a playful and obviously embodiment-related way: a dog-like robot starts playing with a barrier, eventually jumping or climbing over it; a snakebot develops coiling and jumping modes; humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarios when encountering an opponent. The book also develops guided self-organization, a new method that helps to make the playful machines fit for fulfilling tasks in the real world.

link (url) [BibTex]

al Der, R., Martius, G. The Playful Machine - Theoretical Foundation and Practical Realization of Self-Organizing Robots Springer, Berlin Heidelberg, 2012 (book)

link (url) [BibTex]

Consumer Depth Cameras for Computer Vision - Research Topics and Applications

Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K.

Advances in Computer Vision and Pattern Recognition, Springer, 2012 (book)

workshop publisher's site [BibTex]

ps Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. Consumer Depth Cameras for Computer Vision - Research Topics and Applications Advances in Computer Vision and Pattern Recognition, Springer, 2012 (book)

workshop publisher's site [BibTex]

2003

Learning Control and Planning from the View of Control Theory and Imitation

Peters, J., Schaal, S.

NIPS Workshop "Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (talk)

Abstract

Learning control and planning in high dimensional continuous state-action systems, e.g., as needed in a humanoid robot, has so far been a domain beyond the applicability of generic planning techniques like reinforcement learning and dynamic programming. This talk describes an approach we have taken in order to enable complex robotics systems to learn to accomplish control tasks. Adaptive learning controllers equipped with statistical learning techniques can be used to learn tracking controllers -- missing state information and uncertainty in the state estimates are usually addressed by observers or direct adaptive control methods. Imitation learning is used as an ingredient to seed initial control policies whose output is a desired trajectory suitable to accomplish the task at hand. Reinforcement learning with stochastic policy gradients using a natural gradient forms the third component that allows refining the initial control policy until the task is accomplished. In comparison to general learning control, this approach is highly prestructured and thus more domain specific. However, it seems to be a theoretically clean and feasible strategy for control systems of the complexity that we need to address.

Web [BibTex]

2003

ei Peters, J., Schaal, S. Learning Control and Planning from the View of Control Theory and Imitation NIPS Workshop "Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (talk)

Web [BibTex]

Recurrent neural networks from learning attractor dynamics

Schaal, S., Peters, J.

NIPS Workshop on RNNaissance: Recurrent Neural Networks, December 2003 (talk)

Abstract

Many forms of recurrent neural networks can be understood in terms of dynamic systems theory of difference equations or differential equations. Learning in such systems corresponds to adjusting some internal parameters to obtain a desired time evolution of the network, which can usually be characterized in term of point attractor dynamics, limit cycle dynamics, or, in some more rare cases, as strange attractor or chaotic dynamics. Finding a stable learning process to adjust the open parameters of the network towards shaping the desired attractor type and basin of attraction has remain a complex task, as the parameter trajectories during learning can lead the system through a variety of undesirable unstable behaviors, such that learning may never succeed. In this presentation, we review a recently developed learning framework for a class of recurrent neural networks that employs a more structured network approach. We assume that the canonical system behavior is known a priori, e.g., it is a point attractor or a limit cycle. With either supervised learning or reinforcement learning, it is possible to acquire the transformation from a simple representative of this canonical behavior (e.g., a 2nd order linear point attractor, or a simple limit cycle oscillator) to the desired highly complex attractor form. For supervised learning, one shot learning based on locally weighted regression techniques is possible. For reinforcement learning, stochastic policy gradient techniques can be employed. In any case, the recurrent network learned by these methods inherits the stability properties of the simple dynamic system that underlies the nonlinear transformation, such that stability of the learning approach is not a problem. We demonstrate the success of this approach for learning various skills on a humanoid robot, including tasks that require to incorporate additional sensory signals as coupling terms to modify the recurrent network evolution on-line.

Web [BibTex]

ei Schaal, S., Peters, J. Recurrent neural networks from learning attractor dynamics NIPS Workshop on RNNaissance: Recurrent Neural Networks, December 2003 (talk)

Web [BibTex]

Support Vector Channel Selection in BCI

Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.

(120), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, December 2003 (techreport)

Abstract

Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying EEG signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination [3] and Zero-Norm Optimization [13] which are based on the training of Support Vector Machines (SVM) [11]. These algorithms can provide more accurate solutions than standard filter methods for feature selection [14]. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.

PDF Web [BibTex]

ei Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B. Support Vector Channel Selection in BCI (120), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, December 2003 (techreport)

PDF Web [BibTex]

Technical report on Separation methods for nonlinear mixtures

Jutten, C., Karhunen, J., Almeida, L., Harmeling, S.

(D29), EU-Project BLISS, October 2003 (techreport)

PDF [BibTex]

ei Jutten, C., Karhunen, J., Almeida, L., Harmeling, S. Technical report on Separation methods for nonlinear mixtures (D29), EU-Project BLISS, October 2003 (techreport)

PDF [BibTex]

Image Reconstruction by Linear Programming

Tsuda, K., Rätsch, G.

(118), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, October 2003 (techreport)

PDF [BibTex]

ei Tsuda, K., Rätsch, G. Image Reconstruction by Linear Programming (118), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, October 2003 (techreport)

PDF [BibTex]

Technical report on implementation of linear methods and validation on acoustic sources

Harmeling, S., Bünau, P., Ziehe, A., Pham, D.

EU-Project BLISS, September 2003 (techreport)

PDF [BibTex]

ei Harmeling, S., Bünau, P., Ziehe, A., Pham, D. Technical report on implementation of linear methods and validation on acoustic sources EU-Project BLISS, September 2003 (techreport)

PDF [BibTex]

On optimization, parallelization and convergence of the Expectation-Maximization algorithm for finite mixtures of Bernoulli distributions.

Erhan, D.

Helsinki University of Technology, Helsinki, Finland, August 2003 (techreport)

PDF [BibTex]

ei Erhan, D. On optimization, parallelization and convergence of the Expectation-Maximization algorithm for finite mixtures of Bernoulli distributions. Helsinki University of Technology, Helsinki, Finland, August 2003 (techreport)

PDF [BibTex]

Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

PDF [BibTex]

ei Bousquet, O. Statistical Learning Theory Machine Learning Summer School, August 2003 (talk)

PDF [BibTex]

Remarks on Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

PDF [BibTex]

ei Bousquet, O. Remarks on Statistical Learning Theory Machine Learning Summer School, August 2003 (talk)

PDF [BibTex]

Ranking on Data Manifolds

Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.

(113), Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany, June 2003 (techreport)

Abstract

The Google search engine has had a huge success with its PageRank web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the World Wide Web using random walk. This algorithm can only be used for graph data, however. Here we propose a simple universal ranking algorithm for vectorial data, based on the exploration of the intrinsic global geometric structure revealed by a huge amount of data. Experimental results from image and text to bioinformatics illustrates the validity of our algorithm.

PDF [BibTex]

ei Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B. Ranking on Data Manifolds (113), Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany, June 2003 (techreport)

PDF [BibTex]

Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis

Kim, K., Franz, M., Schölkopf, B.

(109), MPI f. biologische Kybernetik, Tuebingen, June 2003 (techreport)

Abstract

A new method for performing a kernel principal component analysis is proposed. By kernelizing the generalized Hebbian algorithm, one can iteratively estimate the principal components in a reproducing kernel Hilbert space with only linear order memory complexity. The derivation of the method, a convergence proof, and preliminary applications in image hyperresolution are presented. In addition, we discuss the extension of the method to the online learning of kernel principal components.

PDF [BibTex]

ei Kim, K., Franz, M., Schölkopf, B. Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis (109), MPI f. biologische Kybernetik, Tuebingen, June 2003 (techreport)

PDF [BibTex]

Learning with Local and Global Consistency

Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.

(112), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, June 2003 (techreport)

Abstract

We consider the learning problem in the transductive setting. Given a set of points of which only some are labeled, the goal is to predict the label of the unlabeled points. A principled clue to solve such a learning problem is the consistency assumption that a classifying function should be sufficiently smooth with respect to the structure revealed by these known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

[BibTex]

ei Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B. Learning with Local and Global Consistency (112), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, June 2003 (techreport)

[BibTex]

The Metric Nearness Problem with Applications

Dhillon, I., Sra, S., Tropp, J.

Univ. of Texas at Austin, June 2003 (techreport)

GZIP [BibTex]

ei Dhillon, I., Sra, S., Tropp, J. The Metric Nearness Problem with Applications Univ. of Texas at Austin, June 2003 (techreport)

GZIP [BibTex]

Implicit Wiener Series

Franz, M., Schölkopf, B.

(114), Max Planck Institute for Biological Cybernetics, June 2003 (techreport)

Abstract

The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a neural system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose a new estimation method based on regression in a reproducing kernel Hilbert space that overcomes these problems. Numerical experiments show performance advantages in terms of convergence, interpretability and system size that can be handled.

PDF [BibTex]

ei Franz, M., Schölkopf, B. Implicit Wiener Series (114), Max Planck Institute for Biological Cybernetics, June 2003 (techreport)

PDF [BibTex]

Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms

Weston, J., Leslie, C., Elisseeff, A., Noble, W.

(111), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2003 (techreport)

Abstract

A key tool in protein function discovery is the ability to rank databases of proteins given a query amino acid sequence. The most successful method so far is a web-based tool called PSI-BLAST which uses heuristic alignment of a profile built using the large unlabeled database. It has been shown that such use of global information via an unlabeled data improves over a local measure derived from a basic pairwise alignment such as performed by PSI-BLAST's predecessor, BLAST. In this article we look at ways of leveraging techniques from the field of machine learning for the problem of ranking. We show how clustering and semi-supervised learning techniques, which aim to capture global structure in data, can significantly improve over PSI-BLAST.

PDF [BibTex]

ei Weston, J., Leslie, C., Elisseeff, A., Noble, W. Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms (111), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2003 (techreport)

PDF [BibTex]

The Geometry Of Kernel Canonical Correlation Analysis

Kuss, M., Graepel, T.

(108), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2003 (techreport)

Abstract

Canonical correlation analysis (CCA) is a classical multivariate method concerned with describing linear dependencies between sets of variables. After a short exposition of the linear sample CCA problem and its analytical solution, the article proceeds with a detailed characterization of its geometry. Projection operators are used to illustrate the relations between canonical vectors and variates. The article then addresses the problem of CCA between spaces spanned by objects mapped into kernel feature spaces. An exact solution for this kernel canonical correlation (KCCA) problem is derived from a geometric point of view. It shows that the expansion coefficients of the canonical vectors in their respective feature space can be found by linear CCA in the basis induced by kernel principal component analysis. The effect of mappings into higher dimensional feature spaces is considered critically since it simplifies the CCA problem in general. Then two regularized variants of KCCA are discussed. Relations to other methods are illustrated, e.g., multicategory kernel Fisher discriminant analysis, kernel principal component regression and possible applications thereof in blind source separation.

PDF [BibTex]

ei Kuss, M., Graepel, T. The Geometry Of Kernel Canonical Correlation Analysis (108), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2003 (techreport)

PDF [BibTex]

The Kernel Mutual Information

Gretton, A., Herbrich, R., Smola, A.

Max Planck Institute for Biological Cybernetics, April 2003 (techreport)

Abstract

We introduce two new functions, the kernel covariance (KC) and the kernel mutual information (KMI), to measure the degree of independence of several continuous random variables. The former is guaranteed to be zero if and only if the random variables are pairwise independent; the latter shares this property, and is in addition an approximate upper bound on the mutual information, as measured near independence, and is based on a kernel density estimate. We show that Bach and Jordan‘s kernel generalised variance (KGV) is also an upper bound on the same kernel density estimate, but is looser. Finally, we suggest that the addition of a regularising term in the KGV causes it to approach the KMI, which motivates the introduction of this regularisation. The performance of the KC and KMI is verified in the context of instantaneous independent component analysis (ICA), by recovering both artificial and real (musical) signals following linear mixing.

PostScript [BibTex]

ei Gretton, A., Herbrich, R., Smola, A. The Kernel Mutual Information Max Planck Institute for Biological Cybernetics, April 2003 (techreport)

PostScript [BibTex]

Rademacher and Gaussian averages in Learning Theory

Bousquet, O.

Universite de Marne-la-Vallee, March 2003 (talk)

PDF [BibTex]

ei Bousquet, O. Rademacher and Gaussian averages in Learning Theory Universite de Marne-la-Vallee, March 2003 (talk)

PDF [BibTex]

Statistical Learning Theory

Bousquet, O., Schölkopf, B.

March 2003 (talk)

PDF [BibTex]

ei Bousquet, O., Schölkopf, B. Statistical Learning Theory March 2003 (talk)

PDF [BibTex]

Concentration Inequalities and Data-Dependent Error Bounds

Bousquet, O.

Uni. Jena, February 2003 (talk)

PDF [BibTex]

ei Bousquet, O. Concentration Inequalities and Data-Dependent Error Bounds Uni. Jena, February 2003 (talk)

PDF [BibTex]

Introduction: Robots with Cognition?

Franz, MO.

6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

Abstract

Using robots as models of cognitive behaviour has a long tradition in robotics. Parallel to the historical development in cognitive science, one observes two major, subsequent waves in cognitive robotics. The first is based on ideas of classical, cognitivist Artificial Intelligence (AI). According to the AI view of cognition as rule-based symbol manipulation, these robots typically try to extract symbolic descriptions of the environment from their sensors that are used to update a common, global world representation from which, in turn, the next action of the robot is derived. The AI approach has been successful in strongly restricted and controlled environments requiring well-defined tasks, e.g. in industrial assembly lines. AI-based robots mostly failed, however, in the unpredictable and unstructured environments that have to be faced by mobile robots. This has provoked the second wave in cognitive robotics which tries to achieve cognitive behaviour as an emergent property from the interaction of simple, low-level modules. Robots of the second wave are called animats as their architecture is designed to closely model aspects of real animals. Using only simple reactive mechanisms and Hebbian-type or evolutionary learning, the resulting animats often outperformed the highly complex AI-based robots in tasks such as obstacle avoidance, corridor following etc. While successful in generating robust, insect-like behaviour, typical animats are limited to stereotyped, fixed stimulus-response associations. If one adopts the view that cognition requires a flexible, goal-dependent choice of behaviours and planning capabilities (H.A. Mallot, Kognitionswissenschaft, 1999, 40-48) then it appears that cognitive behaviour cannot emerge from a collection of purely reactive modules. It rather requires environmentally decoupled structures that work without directly engaging the actions that it is concerned with. This poses the current challenge to cognitive robotics: How can we build cognitive robots that show the robustness and the learning capabilities of animats without falling back into the representational paradigm of AI? The speakers of the symposium present their approaches to this question in the context of robot navigation and sensorimotor learning. In the first talk, Prof. Helge Ritter introduces a robot system for imitation learning capable of exploring various alternatives in simulation before actually performing a task. The second speaker, Angelo Arleo, develops a model of spatial memory in rat navigation based on his electrophysiological experiments. He validates the model on a mobile robot which, in some navigation tasks, shows a performance comparable to that of the real rat. A similar model of spatial memory is used to investigate the mechanisms of territory formation in a series of robot experiments presented by Prof. Hanspeter Mallot. In the last talk, we return to the domain of sensorimotor learning where Ralf M{\"o}ller introduces his approach to generate anticipatory behaviour by learning forward models of sensorimotor relationships.

Web [BibTex]

ei Franz, MO. Introduction: Robots with Cognition? 6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

Web [BibTex]

Expectation Maximization for Clustering on Hyperspheres

Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.

Univ. of Texas at Austin, February 2003 (techreport)

GZIP [BibTex]

ei Banerjee, A., Dhillon, I., Ghosh, J., Sra, S. Expectation Maximization for Clustering on Hyperspheres Univ. of Texas at Austin, February 2003 (techreport)

GZIP [BibTex]

Modeling Data using Directional Distributions

Dhillon, I., Sra, S.

Univ. of Texas at Austin, January 2003 (techreport)

GZIP [BibTex]

ei Dhillon, I., Sra, S. Modeling Data using Directional Distributions Univ. of Texas at Austin, January 2003 (techreport)

GZIP [BibTex]

A Note on Parameter Tuning for On-Line Shifting Algorithms

Bousquet, O.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2003 (techreport)

Abstract

In this short note, building on ideas of M. Herbster [2] we propose a method for automatically tuning the parameter of the FIXED-SHARE algorithm proposed by Herbster and Warmuth [3] in the context of on-line learning with shifting experts. We show that this can be done with a memory requirement of $O(nT)$ and that the additional loss incurred by the tuning is the same as the loss incurred for estimating the parameter of a Bernoulli random variable.

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