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2003


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Concentration Inequalities for Sub-Additive Functions Using the Entropy Method

Bousquet, O.

Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

Abstract
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of exponential moments for these increments. As a consequence of these general inequalities, we obtain refinements of Talagrand's inequality for empirical processes and new bounds for randomized empirical processes. These results are obtained by further developing the entropy method introduced by Ledoux.

ei

PostScript [BibTex]

2003


PostScript [BibTex]


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On the Complexity of Learning the Kernel Matrix

Bousquet, O., Herrmann, D.

In Advances in Neural Information Processing Systems 15, pages: 399-406, (Editors: Becker, S. , S. Thrun, K. Obermayer), The MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We investigate data based procedures for selecting the kernel when learning with Support Vector Machines. We provide generalization error bounds by estimating the Rademacher complexities of the corresponding function classes. In particular we obtain a complexity bound for function classes induced by kernels with given eigenvectors, i.e., we allow to vary the spectrum and keep the eigenvectors fix. This bound is only a logarithmic factor bigger than the complexity of the function class induced by a single kernel. However, optimizing the margin over such classes leads to overfitting. We thus propose a suitable way of constraining the class. We use an efficient algorithm to solve the resulting optimization problem, present preliminary experimental results, and compare them to an alignment-based approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Cluster Kernels for Semi-Supervised Learning

Chapelle, O., Weston, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 15, pages: 585-592, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Mismatch String Kernels for SVM Protein Classification

Leslie, C., Eskin, E., Weston, J., Noble, W.

In Advances in Neural Information Processing Systems 15, pages: 1417-1424, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m mismatches, and do not rely on any generative model for the positive training sequences. We compute the kernels efficiently using a mismatch tree data structure and report experiments on a benchmark SCOP dataset, where we show that the mismatch kernel used with an SVM classifier performs as well as the Fisher kernel, the most successful method for remote homology detection, while achieving considerable computational savings.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Dependency Estimation

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.

In Advances in Neural Information Processing Systems 15, pages: 873-880, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model

Franz, MO., Chahl, JS.

In Advances in Neural Information Processing Systems 15, pages: 1319-1326, (Editors: Becker, S., S. Thrun and K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Clustering with the Fisher score

Tsuda, K., Kawanabe, M., Müller, K.

In Advances in Neural Information Processing Systems 15, pages: 729-736, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
Recently the Fisher score (or the Fisher kernel) is increasingly used as a feature extractor for classification problems. The Fisher score is a vector of parameter derivatives of loglikelihood of a probabilistic model. This paper gives a theoretical analysis about how class information is preserved in the space of the Fisher score, which turns out that the Fisher score consists of a few important dimensions with class information and many nuisance dimensions. When we perform clustering with the Fisher score, K-Means type methods are obviously inappropriate because they make use of all dimensions. So we will develop a novel but simple clustering algorithm specialized for the Fisher score, which can exploit important dimensions. This algorithm is successfully tested in experiments with artificial data and real data (amino acid sequences).

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Marginalized Kernels between Labeled Graphs

Kashima, H., Tsuda, K., Inokuchi, A.

In 20th International Conference on Machine Learning, pages: 321-328, (Editors: Faucett, T. and N. Mishra), 20th International Conference on Machine Learning, August 2003 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Sparse Gaussian Processes: inference, subspace identification and model selection

Csato, L., Opper, M.

In Proceedings, pages: 1-6, (Editors: Van der Hof, , Wahlberg), The Netherlands, 13th IFAC Symposium on System Identifiaction, August 2003, electronical version; Index ThA02-2 (inproceedings)

Abstract
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent function. The inference is carried out using the Bayesian online learning and its extension to the more general iterative approach which we call TAP/EP learning. Sparsity is introduced in this context to make the TAP/EP method applicable to large datasets. We address the prohibitive scaling of the number of parameters by defining a subset of the training data that is used as the support the GP, thus the number of required parameters is independent of the training set, similar to the case of ``Support--‘‘ or ``Relevance--Vectors‘‘. An advantage of the full probabilistic treatment is that allows the computation of the marginal data likelihood or evidence, leading to hyper-parameter estimation within the GP inference. An EM algorithm to choose the hyper-parameters is proposed. The TAP/EP learning is the E-step and the M-step then updates the hyper-parameters. Due to the sparse E-step the resulting algorithm does not involve manipulation of large matrices. The presented algorithm is applicable to a wide variety of likelihood functions. We present results of applying the algorithm on classification and nonstandard regression problems for artificial and real datasets.

ei

PDF GZIP [BibTex]

PDF GZIP [BibTex]


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Adaptive, Cautious, Predictive control with Gaussian Process Priors

Murray-Smith, R., Sbarbaro, D., Rasmussen, CE., Girard, A.

In Proceedings of the 13th IFAC Symposium on System Identification, pages: 1195-1200, (Editors: Van den Hof, P., B. Wahlberg and S. Weiland), Proceedings of the 13th IFAC Symposium on System Identification, August 2003 (inproceedings)

Abstract
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.

ei

PDF [BibTex]

PDF [BibTex]


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On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, MA.

In Humanoids Proceedings, pages: 0-0, Humanoids Proceedings, July 2003, electronical version (inproceedings)

Abstract
In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modelling and synthesis of complex sequences of human movements that contain movement elements with variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

ei

PDF [BibTex]

PDF [BibTex]


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Statistical Learning Theory, Capacity and Complexity

Schölkopf, B.

Complexity, 8(4):87-94, July 2003 (article)

Abstract
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

ei

Web DOI [BibTex]


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Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.

Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., Asai, K.

Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

Abstract
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

ei

DOI [BibTex]

DOI [BibTex]


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A case based comparison of identification with neural network and Gaussian process models.

Kocijan, J., Banko, B., Likar, B., Girard, A., Murray-Smith, R., Rasmussen, CE.

In Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003, 1, pages: 137-142, (Editors: Ruano, E.A.), Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS, April 2003 (inproceedings)

Abstract
In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian process prior approach is a representative of non-parametric modelling approaches. It was compared on a pH process modelling case study. The purpose of modelling was to use the model for control design. The comparison revealed that even though Gaussian process models can be effectively used for modelling dynamic systems caution has to be axercised when signals are selected.

ei

PDF [BibTex]

PDF [BibTex]


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On-Line One-Class Support Vector Machines. An Application to Signal Segmentation

Gretton, A., Desobry, ..

In IEEE ICASSP Vol. 2, pages: 709-712, IEEE ICASSP, April 2003 (inproceedings)

Abstract
In this paper, we describe an efficient algorithm to sequentially update a density support estimate obtained using one-class support vector machines. The solution provided is an exact solution, which proves to be far more computationally attractive than a batch approach. This deterministic technique is applied to the problem of audio signal segmentation, with simulations demonstrating the computational performance gain on toy data sets, and the accuracy of the segmentation on audio signals.

ei

PostScript [BibTex]

PostScript [BibTex]


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The Kernel Mutual Information

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

In IEEE ICASSP Vol. 4, pages: 880-883, IEEE ICASSP, April 2003 (inproceedings)

Abstract
We introduce a new contrast function, the kernel mutual information (KMI), to measure the degree of independence of continuous random variables. This contrast function provides an approximate upper bound on the mutual information, as measured near independence, and is based on a kernel density estimate of the mutual information between a discretised approximation of the continuous random variables. 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.

ei

PostScript [BibTex]

PostScript [BibTex]


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Tractable Inference for Probabilistic Data Models

Csato, L., Opper, M., Winther, O.

Complexity, 8(4):64-68, April 2003 (article)

Abstract
We present an approximation technique for probabilistic data models with a large number of hidden variables, based on ideas from statistical physics. We give examples for two nontrivial applications. © 2003 Wiley Periodicals, Inc.

ei

PDF GZIP Web [BibTex]

PDF GZIP Web [BibTex]


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Feature selection and transduction for prediction of molecular bioactivity for drug design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

Bioinformatics, 19(6):764-771, April 2003 (article)

Abstract
Motivation: In drug discovery a key task is to identify characteristics that separate active (binding) compounds from inactive (non-binding) ones. An automated prediction system can help reduce resources necessary to carry out this task. Results: Two methods for prediction of molecular bioactivity for drug design are introduced and shown to perform well in a data set previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001. The data is characterized by very few positive examples, a very large number of features (describing three-dimensional properties of the molecules) and rather different distributions between training and test data. Two techniques are introduced specifically to tackle these problems: a feature selection method for unbalanced data and a classifier which adapts to the distribution of the the unlabeled test data (a so-called transductive method). We show both techniques improve identification performance and in conjunction provide an improvement over using only one of the techniques. Our results suggest the importance of taking into account the characteristics in this data which may also be relevant in other problems of a similar type.

ei

Web [BibTex]


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Dynamic movement primitives - A framework for motor control in humans and humanoid robots

Schaal, S.

In The International Symposium on Adaptive Motion of Animals and Machines, Kyoto, Japan, March 4-8, 2003, March 2003, clmc (inproceedings)

Abstract
Sensory-motor integration is one of the key issues in robotics. In this paper, we propose an approach to rhythmic arm movement control that is synchronized with an external signal based on exploiting a simple neural oscillator network. Trajectory generation by the neural oscillator is a biologically inspired method that can allow us to generate a smooth and continuous trajectory. The parameter tuning of the oscillators is used to generate a synchronized movement with wide intervals. We adopted the method for the drumming task as an example task. By using this method, the robot can realize synchronized drumming with wide drumming intervals in real time. The paper also shows the experimental results of drumming by a humanoid robot.

am

link (url) [BibTex]

link (url) [BibTex]


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Use of the Zero-Norm with Linear Models and Kernel Methods

Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.

Journal of Machine Learning Research, 3, pages: 1439-1461, March 2003 (article)

Abstract
We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature selection, minimizing training error and ensuring sparsity in solutions. We derive a simple but practical method for achieving these goals and discuss its relationship to existing techniques of minimizing the zero-norm. The method boils down to implementing a simple modification of vanilla SVM, namely via an iterative multiplicative rescaling of the training data. Applications we investigate which aid our discussion include variable and feature selection on biological microarray data, and multicategory classification.

ei

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


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Hierarchical Spatio-Temporal Morphable Models for Representation of complex movements for Imitation Learning

Ilg, W., Bakir, GH., Franz, MO., Giese, M.

In 11th International Conference on Advanced Robotics, (2):453-458, (Editors: Nunes, U., A. de Almeida, A. Bejczy, K. Kosuge and J.A.T. Machado), 11th International Conference on Advanced Robotics, January 2003 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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An Introduction to Variable and Feature Selection.

Guyon, I., Elisseeff, A.

Journal of Machine Learning, 3, pages: 1157-1182, 2003 (article)

ei

[BibTex]

[BibTex]


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Feature Selection for Support Vector Machines by Means of Genetic Algorithms

Fröhlich, H., Chapelle, O., Schölkopf, B.

In 15th IEEE International Conference on Tools with AI, pages: 142-148, 15th IEEE International Conference on Tools with AI, 2003 (inproceedings)

ei

[BibTex]

[BibTex]


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Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

Quiñonero-Candela, J., Girard, A., Larsen, J., Rasmussen, CE.

In IEEE International Conference on Acoustics, Speech and Signal Processing, 2, pages: 701-704, IEEE International Conference on Acoustics, Speech and Signal Processing, 2003 (inproceedings)

Abstract
The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables improved estimates of forecasted values and their uncertainties. In this paper we focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaussian Process and the Relevance Vector Machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting. The capability of the method is demonstrated for forecasting of time-series and compared to approximate methods.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Unsupervised Clustering of Images using their Joint Segmentation

Seldin, Y., Starik, S., Werman, M.

In The 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV 2003), pages: 1-24, 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV), 2003 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Methods and Their Applications to Signal Processing

Bousquet, O., Perez-Cruz, F.

In Proceedings. (ICASSP ‘03), Special Session on Kernel Methods, pages: 860 , ICASSP, 2003 (inproceedings)

Abstract
Recently introduced in Machine Learning, the notion of kernels has drawn a lot of interest as it allows to obtain non-linear algorithms from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the Support Vector Machines has produced significant progress. The successes of such algorithms is now spreading as they are applied to more and more domains. Many Signal Processing problems, by their non-linear and high-dimensional nature may benefit from such techniques. We give an overview of kernel methods and their recent applications.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Predictive control with Gaussian process models

Kocijan, J., Murray-Smith, R., Rasmussen, CE., Likar, B.

In Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool, pages: 352-356, (Editors: Zajc, B. and M. Tkal), Proceedings of IEEE Region 8 Eurocon: Computer as a Tool, 2003 (inproceedings)

Abstract
This paper describes model-based predictive control based on Gaussian processes.Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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New Approaches to Statistical Learning Theory

Bousquet, O.

Annals of the Institute of Statistical Mathematics, 55(2):371-389, 2003 (article)

Abstract
We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms.

ei

PostScript [BibTex]

PostScript [BibTex]


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Distance-based classification with Lipschitz functions

von Luxburg, U., Bousquet, O.

In Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, pages: 314-328, (Editors: Schölkopf, B. and M.K. Warmuth), Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, 2003 (inproceedings)

Abstract
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a corresponding notion of margin such that the decision function separates the training points with a large margin. It will turn out that using Lipschitz functions as decision functions, the inverse of the Lipschitz constant can be interpreted as the size of a margin. In order to construct a clean mathematical setup we isometrically embed the given metric space into a Banach space and the space of Lipschitz functions into its dual space. Our approach leads to a general large margin algorithm for classification in metric spaces. To analyze this algorithm, we first prove a representer theorem. It states that there exists a solution which can be expressed as linear combination of distances to sets of training points. Then we analyze the Rademacher complexity of some Lipschitz function classes. The generality of the Lipschitz approach can be seen from the fact that several well-known algorithms are special cases of the Lipschitz algorithm, among them the support vector machine, the linear programming machine, and the 1-nearest neighbor classifier.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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New electro-optic effect: Sum-frequency generation from optically active liquids in the presence of a dc electric field

Fischer, P., Buckingham, A., Beckwitt, K., Wiersma, D., Wise, F.

PHYSICAL REVIEW LETTERS, 91(17), 2003 (article)

Abstract
We report the observation of sum-frequency signals that depend linearly on an applied electrostatic field and that change sign with the handedness of an optically active solute. This recently predicted chiral electro-optic effect exists in the electric-dipole approximation. The static electric field gives rise to an electric-field-induced sum-frequency signal (an achiral third-order process) that interferes with the chirality-specific sum-frequency at second order. The cross-terms linear in the electrostatic field constitute the effect and may be used to determine the absolute sign of second- and third-order nonlinear-optical susceptibilities in isotropic media.

pf

DOI [BibTex]

DOI [BibTex]


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Chiral and achiral contributions to sum-frequency generation from optically active solutions of binaphthol

Fischer, P., Wise, F., Albrecht, A.

JOURNAL OF PHYSICAL CHEMISTRY A, 107(40):8232-8238, 2003 (article)

Abstract
The nonlinear sum- and difference-frequency generation spectroscopies can be probes of molecular chirality in optically active systems. We present a tensorial analysis of the chirality-specific electric-dipolar sum-frequency-generation susceptibility and the achiral electric-quadrupolar and magnetic-dipolar nonlinearities at second order in isotropic media. The chiral and achiral contributions to the sum-frequency signal from the bulk of optically active solutions of 1,1'-bi-2-naphthol (2,2'-dehydroxy-1,1'-binaphthyl) can be distinguished, and the former dominates. Ab initio computations reveal the dramatic resonance enhancement that the isotropic component of the electric-dipolar three-wave mixing hyperpolarizability experiences. Away from resonance its magnitude rapidly decreases, as-unlike the vector component-it is zero in the static limit. The dispersion of the first hyperpolarizability is computed by a configuration interaction singles sum-over-states approach with explicit regard to the Franck-Condon active vibrational substructure for all resonant electronic states.

pf

DOI [BibTex]

DOI [BibTex]


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Synthetic gecko foot-hair micro/nano-structures as dry adhesives

Sitti, M., Fearing, R. S.

Journal of adhesion science and technology, 17(8):1055-1073, Taylor & Francis Group, 2003 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Teleoperated touch feedback from the surfaces at the nanoscale: modeling and experiments

Sitti, M., Hashimoto, H.

IEEE/ASME transactions on mechatronics, 8(2):287-298, IEEE, 2003 (article)

pi

[BibTex]

[BibTex]


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Bayesian backfitting

D’Souza, A., Vijayakumar, S., Schaal, S.

In Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003), Irvine, CA, May 2003, 2003, clmc (inproceedings)

Abstract
We present an algorithm aimed at addressing both computational and analytical intractability of Bayesian regression models which operate in very high-dimensional, usually underconstrained spaces. Several domains of research frequently provide such datasets, including chemometrics [2], and human movement analysis [1]. The literature in nonparametric statistics provides interesting solutions such as Backfitting [3] and Partial Least Squares [4], which are extremely robust and efficient, yet lack a probabilistic interpretation that could place them in the context of current research in statistical learning algorithms that emphasize the estimation of confidence, posterior distributions, and model complexity. In order to achieve numerical robustness and low computational cost, we first derive a novel Bayesian interpretation of Backfitting (BB) as a computationally efficient regression algorithm. BBÕs learning complexity scales linearly with the input dimensionality by decoupling inference among individual input dimensions. We embed BB in an efficient, locally variational model selection mechanism that automatically grows the number of backfitting experts in a mixture-of-experts regression model. We demonstrate the effectiveness of the algorithm in performing principled regularization of model complexity when fitting nonlinear manifolds while avoiding the numerical hazards associated with highly underconstrained problems. We also note that this algorithm appears applicable in various areas of neural computation, e.g., in abstract models of computational neuroscience, or implementations of statistical learning on artificial systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Reinforcement learning for humanoid robotics

Peters, J., Vijayakumar, S., Schaal, S.

In IEEE-RAS International Conference on Humanoid Robots (Humanoids2003), Karlsruhe, Germany, Sept.29-30, 2003, clmc (inproceedings)

Abstract
Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, `vanilla' policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. `Vanilla' policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade (2002) is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high dimensionally continuous state-action systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Mixing in Cu/Ge system by swift heavy ions

Kumar, S., Chauhan, R. S., Singh, R. P., Kabiraj, D., Sahoo, P. K., Rumbolz, C., Srivastava, S. K., Bolse, W., Avasthi, D. K.

{Nuclear Instruments \& Methods in Physics Research Section B-Beam Interactions with Materials and Atoms}, 212, pages: 242-245, 2003 (article)

mms

[BibTex]

[BibTex]


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Magnetic properties of [NdFeBx/Nbz]n multilayer films

Tsai, J. L., Chin, T. S., Yao, Y. D., Melsheimer, A., Fischer, S. F., Dragon, T., Kelsch, M., Kronmüller, H.

{Journal of Applied Physics}, 93(10):6915-6917, 2003 (article)

mms

[BibTex]

[BibTex]


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Analysis of the temperature dependence of the coercive field of Sm2Co17 based magnets

Kronmüller, H., Goll, D.

{Scripta Materialia}, 48(7):833-838, 2003 (article)

mms

[BibTex]


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NMR studies of hydrogen motion in nanostructured hydrogen-graphite systems

Majer, G., Stanik, E., Orimo, S.

{Journal of Alloys and Compounds}, 356-357, pages: 617-621, 2003 (article)

mms

[BibTex]

[BibTex]


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Hydrogen diffusion in metallic and nanostructured materials

Majer, G., Eberle, U., Kimmerle, F., Stanik, E., Orimo, S.

{Physica B}, 328, pages: 81-89, 2003 (article)

mms

[BibTex]

[BibTex]


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Grain boundary phase transitions in the Al-Mg system and their influence on high-strain rate superplasticity

Straumal, B. B., Lopez, G. A., Mittemeijer, E. J., Gust, W., Zhilyaev, A. P.

In 216-217, pages: 307-312, Moscow, Russia, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Electron density-of-states and the metal-insulator transition in LaHx

Barnes, R. G., Chang, C. T., Majer, G., Kaess, U.

{Journal of Alloys and Compounds}, 356-357, pages: 137-141, 2003 (article)

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[BibTex]

[BibTex]


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Electronic sputtering from HOPG: A study of angular dependence

Tripathi, A., Khan, S. A., Srivastava, S. K., Kumar, M., Kumar, S., Rao, S. V. S. N., Lakshmi, G. B. V. S., Siddiqui, A. M., Bajwa, N., Nagaraja, H. S., Mittal, V. K., Szökefalvi, A., Kurth, M., Pandey, A. C., Avasthi, D. K., Carstanjen, H. D.

{Nuclear Instruments and Methods B}, 212, pages: 402-406, 2003 (article)

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[BibTex]

[BibTex]


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High aspect ratio polymer micro/nano-structure manufacturing using nanoembossing, nanomolding and directed self-assembly

Sitti, M.

In ASME 2003 International Mechanical Engineering Congress and Exposition, pages: 293-297, 2003 (inproceedings)

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[BibTex]

[BibTex]


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Nsf workshop on future directions in nano-scale systems, dynamics and control

Sitti, M.

In Automatic Control Conference (ACC), 2003 (inproceedings)

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[BibTex]

[BibTex]


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3-D nano-fiber manufacturing by controlled pulling of liquid polymers using nano-probes

Nain, A. S., Sitti, M.

In Nanotechnology, 2003. IEEE-NANO 2003. 2003 Third IEEE Conference on, 1, pages: 60-63, 2003 (inproceedings)

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[BibTex]

[BibTex]