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44 results (View BibTeX file of all listed publications)

2013

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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)

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2013

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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Domain Generalization via Invariant Feature Representation

Muandet, K.

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

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

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2012

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

Muandet, K.

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

[BibTex]

2012

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]

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Hilbert Space Embedding for Dirichlet Process Mixtures

Muandet, K.

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

[BibTex]

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

[BibTex]

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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)

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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)

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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)

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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)

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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)

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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)

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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]

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]

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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

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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)

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

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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]

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]

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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]

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)

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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]

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)

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Structured Apprenticeship Learning

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

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

[BibTex]

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

[BibTex]

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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]

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]

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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]

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]

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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]

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]

2006

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A Kernel Method for the Two-Sample-Problem

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.

20th Annual Conference on Neural Information Processing Systems (NIPS), December 2006 (talk)

Abstract
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. We show that the test statistic can be computed in $O(m^2)$ time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

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2006

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A. A Kernel Method for the Two-Sample-Problem 20th Annual Conference on Neural Information Processing Systems (NIPS), December 2006 (talk)

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Ab-initio gene finding using machine learning

Schweikert, G., Zeller, G., Zien, A., Ong, C., de Bona, F., Sonnenburg, S., Phillips, P., Rätsch, G.

NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

Web [BibTex]

Schweikert, G., Zeller, G., Zien, A., Ong, C., de Bona, F., Sonnenburg, S., Phillips, P., Rätsch, G. Ab-initio gene finding using machine learning NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

Web [BibTex]

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Graph boosting for molecular QSAR analysis

Saigo, H., Kadowaki, T., Kudo, T., Tsuda, K.

NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

Abstract
We propose a new boosting method that systematically combines graph mining and mathematical programming-based machine learning. Informative and interpretable subgraph features are greedily found by a series of graph mining calls. Due to our mathematical programming formulation, subgraph features and pre-calculated real-valued features are seemlessly integrated. We tested our algorithm on a quantitative structure-activity relationship (QSAR) problem, which is basically a regression problem when given a set of chemical compounds. In benchmark experiments, the prediction accuracy of our method favorably compared with the best results reported on each dataset.

Web [BibTex]

Saigo, H., Kadowaki, T., Kudo, T., Tsuda, K. Graph boosting for molecular QSAR analysis NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

Web [BibTex]

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Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions

Sun, X., Janzing, D., Schölkopf, B.

NIPS Workshop on Causality and Feature Selection, December 2006 (talk)

Abstract
We propose a new approach to infer the causal structure that has generated the observed statistical dependences among n random variables. The idea is that the factorization of the joint measure of cause and effect into P(cause)P(effect|cause) leads typically to simpler conditionals than non-causal factorizations. To evaluate the complexity of the conditionals we have tried two methods. First, we have compared them to those which maximize the conditional entropy subject to the observed first and second moments since we consider the latter as the simplest conditionals. Second, we have fitted the data with conditional probability measures being exponents of functions in an RKHS space and defined the complexity by a Hilbert-space semi-norm. Such a complexity measure has several properties that are useful for our purpose. We describe some encouraging results with both methods applied to real-world data. Moreover, we have combined constraint-based approaches to causal discovery (i.e., methods using only information on conditional statistical dependences) with our method in order to distinguish between causal hypotheses which are equivalent with respect to the imposed independences. Furthermore, we compare the performance to Bayesian approaches to causal inference.

Web [BibTex]

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Learning Optimal EEG Features Across Time, Frequency and Space

Farquhar, J., Hill, J., Schölkopf, B.

NIPS Workshop on Current Trends in Brain-Computer Interfacing, December 2006 (talk)

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Farquhar, J., Hill, J., Schölkopf, B. Learning Optimal EEG Features Across Time, Frequency and Space NIPS Workshop on Current Trends in Brain-Computer Interfacing, December 2006 (talk)

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Semi-Supervised Learning

Zien, A.

Advanced Methods in Sequence Analysis Lectures, November 2006 (talk)

Web [BibTex]

Zien, A. Semi-Supervised Learning Advanced Methods in Sequence Analysis Lectures, November 2006 (talk)

Web [BibTex]

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A Machine Learning Approach for Determining the PET Attenuation Map from Magnetic Resonance Images

Hofmann, M., Steinke, F., Judenhofer, M., Claussen, C., Schölkopf, B., Pichler, B.

IEEE Medical Imaging Conference, November 2006 (talk)

Abstract
A promising new combination in multimodality imaging is MR-PET, where the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET) are combined. Although many technical problems have recently been solved, it is still an open problem to determine the attenuation map from the available MR scan, as the MR intensities are not directly related to the attenuation values. One standard approach is an atlas registration where the atlas MR image is aligned with the patient MR thus also yielding an attenuation image for the patient. We also propose another approach, which to our knowledge has not been tried before: Using Support Vector Machines we predict the attenuation value directly from the local image information. We train this well-established machine learning algorithm using small image patches. Although both approaches sometimes yielded acceptable results, they also showed their specific shortcomings: The registration often fails with large deformations whereas the prediction approach is problematic when the local image structure is not characteristic enough. However, the failures often do not coincide and integration of both information sources is promising. We therefore developed a combination method extending Support Vector Machines to use not only local image structure but also atlas registered coordinates. We demonstrate the strength of this combination approach on a number of examples.

[BibTex]

Hofmann, M., Steinke, F., Judenhofer, M., Claussen, C., Schölkopf, B., Pichler, B. A Machine Learning Approach for Determining the PET Attenuation Map from Magnetic Resonance Images IEEE Medical Imaging Conference, November 2006 (talk)

[BibTex]

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Semi-Supervised Support Vector Machines and Application to Spam Filtering

Zien, A.

ECML Discovery Challenge Workshop, September 2006 (talk)

Abstract
After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a few popular training strategies are briefly presented. Then the assumptions underlying semi-supervised learning are reviewed. Finally, two modern TSVM optimization techniques are applied to the spam filtering data sets of the workshop; it is shown that they can achieve excellent results, if the problem of the data being non-iid can be handled properly.

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Semi-Supervised Learning

Chapelle, O., Schölkopf, B., Zien, A.

pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Abstract
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

Web [BibTex]

Chapelle, O., Schölkopf, B., Zien, A. Semi-Supervised Learning pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Web [BibTex]

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Inferential Structure Determination: Probabilistic determination and validation of NMR structures

Habeck, M.

Gordon Research Conference on Computational Aspects of Biomolecular NMR, September 2006 (talk)

Web [BibTex]

Habeck, M. Inferential Structure Determination: Probabilistic determination and validation of NMR structures Gordon Research Conference on Computational Aspects of Biomolecular NMR, September 2006 (talk)

Web [BibTex]

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Machine Learning Algorithms for Polymorphism Detection

Schweikert, G., Zeller, G., Clark, R., Ossowski, S., Warthmann, N., Shinn, P., Frazer, K., Ecker, J., Huson, D., Weigel, D., Schölkopf, B., Rätsch, G.

2nd ISCB Student Council Symposium, August 2006 (talk)

Abstract
Analyzing resequencing array data using machine learning, we obtain a genome-wide inventory of polymorphisms in 20 wild strains of Arabidopsis thaliana, including 750,000 single nucleotide poly- morphisms (SNPs) and thousands of highly polymorphic regions and deletions. We thus provide an unprecedented resource for the study of natural variation in plants.

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Schweikert, G., Zeller, G., Clark, R., Ossowski, S., Warthmann, N., Shinn, P., Frazer, K., Ecker, J., Huson, D., Weigel, D., Schölkopf, B., Rätsch, G. Machine Learning Algorithms for Polymorphism Detection 2nd ISCB Student Council Symposium, August 2006 (talk)

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Inferential structure determination: Overview and new developments

Habeck, M.

Sixth CCPN Annual Conference: Efficient and Rapid Structure Determination by NMR, July 2006 (talk)

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Habeck, M. Inferential structure determination: Overview and new developments Sixth CCPN Annual Conference: Efficient and Rapid Structure Determination by NMR, July 2006 (talk)

Web [BibTex]

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MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models

Rasmussen, C., Görür, D.

ICML Workshop on Learning with Nonparametric Bayesian Methods, June 2006 (talk)

Abstract
We compare the predictive accuracy of the Dirichlet Process Gaussian mixture models using conjugate and conditionally conjugate priors and show that better density models result from using the wider class of priors. We explore several MCMC schemes exploiting conditional conjugacy and show their computational merits on several multidimensional density estimation problems.

Web [BibTex]

Rasmussen, C., Görür, D. MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models ICML Workshop on Learning with Nonparametric Bayesian Methods, June 2006 (talk)

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Sampling for non-conjugate infinite latent feature models

Görür, D., Rasmussen, C.

(Editors: Bernardo, J. M.), 8th Valencia International Meeting on Bayesian Statistics (ISBA), June 2006 (talk)

Abstract
Latent variable models are powerful tools to model the underlying structure in data. Infinite latent variable models can be defined using Bayesian nonparametrics. Dirichlet process (DP) models constitute an example of infinite latent class models in which each object is assumed to belong to one of the, mutually exclusive, infinitely many classes. Recently, the Indian buffet process (IBP) has been defined as an extension of the DP. IBP is a distribution over sparse binary matrices with infinitely many columns which can be used as a distribution for non-exclusive features. Inference using Markov chain Monte Carlo (MCMC) in conjugate IBP models has been previously described, however requiring conjugacy restricts the use of IBP. We describe an MCMC algorithm for non-conjugate IBP models. Modelling the choice behaviour is an important topic in psychology, economics and related fields. Elimination by Aspects (EBA) is a choice model that assumes each alternative has latent features with associated weights that lead to the observed choice outcomes. We formulate a non-parametric version of EBA by using IBP as the prior over the latent binary features. We infer the features of objects that lead to the choice data by using our sampling scheme for inference.

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Görür, D., Rasmussen, C. Sampling for non-conjugate infinite latent feature models (Editors: Bernardo, J. M.), 8th Valencia International Meeting on Bayesian Statistics (ISBA), June 2006 (talk)

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An Inventory of Sequence Polymorphisms For Arabidopsis

Clark, R., Ossowski, S., Schweikert, G., Rätsch, G., Shinn, P., Zeller, G., Warthmann, N., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D.

17th International Conference on Arabidopsis Research, April 2006 (talk)

Abstract
We have used high-density oligonucleotide arrays to characterize common sequence variation in 20 wild strains of Arabidopsis thaliana that were chosen for maximal genetic diversity. Both strands of each possible SNP of the 119 Mb reference genome were represented on the arrays, which were hybridized with whole genome, isothermally amplified DNA to minimize ascertainment biases. Using two complementary approaches, a model based algorithm, and a newly developed machine learning method, we identified over 550,000 SNPs with a false discovery rate of ~ 0.03 (average of 1 SNP for every 216 bp of the genome). A heuristic algorithm predicted in addition ~700 highly polymorphic or deleted regions per accession. Over 700 predicted polymorphisms with major functional effects (e.g., premature stop codons, or deletions of coding sequence) were validated by dideoxy sequencing. Using this data set, we provide the first systematic description of the types of genes that harbor major effect polymorphisms in natural populations at moderate allele frequencies. The data also provide an unprecedented resource for the study of genetic variation in an experimentally tractable, multicellular model organism.

[BibTex]

Clark, R., Ossowski, S., Schweikert, G., Rätsch, G., Shinn, P., Zeller, G., Warthmann, N., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D. An Inventory of Sequence Polymorphisms For Arabidopsis 17th International Conference on Arabidopsis Research, April 2006 (talk)

[BibTex]

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Gaussian Processes for Machine Learning

Rasmussen, CE., Williams, CKI.

pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Abstract
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Web [BibTex]

Rasmussen, CE., Williams, CKI. Gaussian Processes for Machine Learning pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Web [BibTex]

2003

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Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

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2003

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

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Remarks on Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

PDF [BibTex]

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

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Rademacher and Gaussian averages in Learning Theory

Bousquet, O.

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

PDF [BibTex]

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

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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]

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]

2002

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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Schölkopf, B., Smola, A.

pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

Abstract
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Web [BibTex]

2002

Schölkopf, B., Smola, A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

Web [BibTex]