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2006


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Statistical Convergence of Kernel CCA

Fukumizu, K., Bach, F., Gretton, A.

In Advances in neural information processing systems 18, pages: 387-394, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract
While kernel canonical correlation analysis (kernel CCA) has been applied in many problems, the asymptotic convergence of the functions estimated from a finite sample to the true functions has not yet been established. This paper gives a rigorous proof of the statistical convergence of kernel CCA and a related method (NOCCO), which provides a theoretical justification for these methods. The result also gives a sufficient condition on the decay of the regularization coefficient in the methods to ensure convergence.

ei

PDF Web [BibTex]

2006


PDF Web [BibTex]


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Products of "Edge-perts"

Gehler, PV., Welling, M.

In Advances in neural information processing systems 18, pages: 419-426, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract
Images represent an important and abundant source of data. Understanding their statistical structure has important applications such as image compression and restoration. In this paper we propose a particular kind of probabilistic model, dubbed the “products of edge-perts model” to describe the structure of wavelet transformed images. We develop a practical denoising algorithm based on a single edge-pert and show state-ofthe-art denoising performance on benchmark images.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Assessing Approximations for Gaussian Process Classification

Kuss, M., Rasmussen, C.

In Advances in neural information processing systems 18, pages: 699-706, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

Abstract
Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplace‘s method and Expectation Propagation (EP) focusing on marginal likelihood estimates and predictive performance. We explain theoretically and corroborate empirically that EP is superior to Laplace. We also compare to a sophisticated MCMC scheme and show that EP is surprisingly accurate.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Learning an Interest Operator from Human Eye Movements

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M.

In CVPWR 2006, pages: page 24, (Editors: C Schmid and S Soatto and C Tomasi), IEEE Computer Society, Los Alamitos, CA, USA, 2006 Conference on Computer Vision and Pattern Recognition Workshop, April 2006 (inproceedings)

Abstract
We present an approach for designing interest operators that are based on human eye movement statistics. In contrast to existing methods which use hand-crafted saliency measures, we use machine learning methods to infer an interest operator directly from eye movement data. That way, the operator provides a measure of biologically plausible interestingness. We describe the data collection, training, and evaluation process, and show that our learned saliency measure significantly accounts for human eye movements. Furthermore, we illustrate connections to existing interest operators, and present a multi-scale interest point detector based on the learned function.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Evaluating Predictive Uncertainty Challenge

Quinonero Candela, J., Rasmussen, C., Sinz, F., Bousquet, O., Schölkopf, B.

In Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, pages: 1-27, (Editors: J Quiñonero Candela and I Dagan and B Magnini and F d’Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop (MLCW), April 2006 (inproceedings)

Abstract
This Chapter presents the PASCAL Evaluating Predictive Uncertainty Challenge, introduces the contributed Chapters by the participants who obtained outstanding results, and provides a discussion with some lessons to be learnt. The Challenge was set up to evaluate the ability of Machine Learning algorithms to provide good “probabilistic predictions”, rather than just the usual “point predictions” with no measure of uncertainty, in regression and classification problems. Parti-cipants had to compete on a number of regression and classification tasks, and were evaluated by both traditional losses that only take into account point predictions and losses we proposed that evaluate the quality of the probabilistic predictions.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Estimating Predictive Variances with Kernel Ridge Regression

Cawley, G., Talbot, N., Chapelle, O.

In MLCW 2005, pages: 56-77, (Editors: Quinonero-Candela, J. , I. Dagan, B. Magnini, F. D‘Alché-Buc), Springer, Berlin, Germany, First PASCAL Machine Learning Challenges Workshop, April 2006 (inproceedings)

Abstract
In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal sources of this uncertainty: the noise process contaminating the data and the uncertainty in estimating the model parameters based on a limited sample of training data. Both of them can be summarised in the predictive variance which can then be used to give confidence intervals. In this paper, we present various schemes for providing predictive variances for kernel ridge regression, especially in the case of a heteroscedastic regression, where the variance of the noise process contaminating the data is a smooth function of the explanatory variables. The use of leave-one-out cross-validation is shown to eliminate the bias inherent in estimates of the predictive variance. Results obtained on all three regression tasks comprising the predictive uncertainty challenge demonstrate the value of this approach.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Machine Learning Methods For Estimating Operator Equations

Steinke, F., Schölkopf, B.

In Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), pages: 6, (Editors: B Ninness and H Hjalmarsson), Elsevier, Oxford, United Kingdom, 14th IFAC Symposium on System Identification (SYSID), March 2006 (inproceedings)

Abstract
We consider the problem of fitting a linear operator induced equation to point sampled data. In order to do so we systematically exploit the duality between minimizing a regularization functional derived from an operator and kernel regression methods. Standard machine learning model selection algorithms can then be interpreted as a search of the equation best fitting given data points. For many kernels this operator induced equation is a linear differential equation. Thus, we link a continuous-time system identification task with common machine learning methods. The presented link opens up a wide variety of methods to be applied to this system identification problem. In a series of experiments we demonstrate an example algorithm working on non-uniformly spaced data, giving special focus to the problem of identifying one system from multiple data recordings.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Implicit Volterra and Wiener Series for Higher-Order Image Analysis

Franz, M., Schölkopf, B.

In Advances in Data Analysis: Proceedings of the 30th Annual Conference of The Gesellschaft für Klassifikation, 30, pages: 1, March 2006 (inproceedings)

Abstract
The computation of classical higher-order statistics such as higher-order moments or spectra is difficult for images due to the huge number of terms to be estimated and interpreted. We propose an alternative approach in which multiplicative pixel interactions are described by a series of Wiener functionals. Since the functionals are estimated implicitly via polynomial kernels, the combinatorial explosion associated with the classical higher-order statistics is avoided. In addition, the kernel framework allows for estimating infinite series expansions and for the regularized estimation of the Wiener series. First results show that image structures such as lines or corners can be predicted correctly, and that pixel interactions up to the order of five play an important role in natural images.

ei

PDF [BibTex]

PDF [BibTex]


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Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

Sun, X., Janzing, D., Schölkopf, B.

In Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, pages: 1-11, ISAIM, January 2006 (inproceedings)

Abstract
We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the so-called Markov kernels") for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In this paper, we discuss our inference rule for causal relationships between two variables in detail, apply it to a real-world temperature data set with known causality and show that our method provides a correct result for the example.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Learning operational space control

Peters, J., Schaal, S.

In Robotics: Science and Systems II (RSS 2006), pages: 255-262, (Editors: Gaurav S. Sukhatme and Stefan Schaal and Wolfram Burgard and Dieter Fox), Cambridge, MA: MIT Press, RSS , 2006, clmc (inproceedings)

Abstract
While operational space control is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in complex robots, e.g., humanoid robots. In such cases, learning control methods can offer an interesting alternative to analytical control algorithms. However, the resulting learning problem is ill-defined as it requires to learn an inverse mapping of a usually redundant system, which is well known to suffer from the property of non-covexity of the solution space, i.e., the learning system could generate motor commands that try to steer the robot into physically impossible configurations. A first important insight for this paper is that, nevertheless, a physically correct solution to the inverse problem does exits when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on a recent insight that many operational space controllers can be understood in terms of a constraint optimal control problem. The cost function associated with this optimal control problem allows us to formulate a learning algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the view of machine learning, the learning problem corresponds to a reinforcement learning problem that maximizes an immediate reward and that employs an expectation-maximization policy search algorithm. Evaluations on a three degrees of freedom robot arm illustrate the feasability of our suggested approach.

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link (url) [BibTex]

link (url) [BibTex]


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Reinforcement Learning for Parameterized Motor Primitives

Peters, J., Schaal, S.

In Proceedings of the 2006 International Joint Conference on Neural Networks, pages: 73-80, IJCNN, 2006, clmc (inproceedings)

Abstract
One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the "building blocks of movement generation", called motor primitives. Motor primitives, as used in this paper, are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties. While a lot of progress has been made in teaching parameterized motor primitives using supervised or imitation learning, the self-improvement by interaction of the system with the environment remains a challenging problem. In this paper, we evaluate different reinforcement learning approaches for improving the performance of parameterized motor primitives. For pursuing this goal, we highlight the difficulties with current reinforcement learning methods, and outline both established and novel algorithms for the gradient-based improvement of parameterized policies. We compare these algorithms in the context of motor primitive learning, and show that our most modern algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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From Motor Babbling to Purposive Actions: Emerging Self-exploration in a Dynamical Systems Approach to Early Robot Development

Der, R., Martius, G.

In Proc. From Animals to Animats 9, SAB 2006, 4095, pages: 406-421, LNCS, Springer, 2006 (inproceedings)

Abstract
Self-organization and the phenomenon of emergence play an essential role in living systems and form a challenge to artificial life systems. This is not only because systems become more lifelike, but also since self-organization may help in reducing the design efforts in creating complex behavior systems. The present paper studies self-exploration based on a general approach to the self-organization of behavior, which has been developed and tested in various examples in recent years. This is a step towards autonomous early robot development. We consider agents under the close sensorimotor coupling paradigm with a certain cognitive ability realized by an internal forward model. Starting from tabula rasa initial conditions we overcome the bootstrapping problem and show emerging self-exploration. Apart from that, we analyze the effect of limited actions, which lead to deprivation of the world model. We show that our paradigm explicitly avoids this by producing purposive actions in a natural way. Examples are given using a simulated simple wheeled robot and a spherical robot driven by shifting internal masses.

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[BibTex]

[BibTex]


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Ab-initio calculations: I. Basic principles of the density functional electron theory and combination with phenomenological theories

Fähnle, M.

In Structural defects in ordered alloys and intermetallics. Characterization and modelling, pages: IX-1-IX-10, COST and CNRS, Bonascre [Ariege, France], 2006 (inproceedings)

mms

[BibTex]

[BibTex]


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Hard magnetic FePt thin films and nanostructures in L1(0) phases

Goll, D., Breitling, A., Goo, N. H., Sigle, W., Hirscher, M., Schütz, G.

In 13, pages: 97-101, Beijing, PR China, 2006 (inproceedings)

mms

[BibTex]

[BibTex]


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Miniature endoscopic capsule robot using biomimetic micro-patterned adhesives

Karagozler, M. E., Cheung, E., Kwon, J., Sitti, M.

In Biomedical Robotics and Biomechatronics, 2006. BioRob 2006. The First IEEE/RAS-EMBS International Conference on, pages: 105-111, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Ab-initio calculations: II. Application to atomic defects, phase diagrams, dislocations

Fähnle, M.

In Structural defects in ordered alloys and intermetallics. Characterization and modelling, pages: XIV-1-XIV-11, COST and CNRS, Bonascre [Ariege, France], 2006 (inproceedings)

mms

[BibTex]

[BibTex]


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Toward micro wall-climbing robots using biomimetic fibrillar adhesives

Greuter, M., Shah, G., Caprari, G., Tâche, F., Siegwart, R., Sitti, M.

In Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005), pages: 39-46, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Geckobot: A gecko inspired climbing robot using elastomer adhesives

Unver, O., Uneri, A., Aydemir, A., Sitti, M.

In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages: 2329-2335, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Towards hybrid swimming microrobots: bacteria assisted propulsion of polystyrene beads

Behkam, B., Sitti, M.

In Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE, pages: 2421-2424, 2006 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Movement generation using dynamical systems : a humanoid robot performing a drumming task

Degallier, S., Santos, C. P., Righetti, L., Ijspeert, A.

In 2006 6th IEEE-RAS International Conference on Humanoid Robots, pages: 512-517, IEEE, Genova, Italy, 2006 (inproceedings)

Abstract
The online generation of trajectories in humanoid robots remains a difficult problem. In this contribution, we present a system that allows the superposition, and the switch between, discrete and rhythmic movements. Our approach uses nonlinear dynamical systems for generating trajectories online and in real time. Our goal is to make use of attractor properties of dynamical systems in order to provide robustness against small perturbations and to enable online modulation of the trajectories. The system is demonstrated on a humanoid robot performing a drumming task.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Soft microcontact printing with force control using microrobotic assembly based templates

Tafazzoli, A., Sitti, M.

In Advanced Motion Control, 2006. 9th IEEE International Workshop on, pages: 500-505, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Modeling of the supporting legs for designing biomimetic water strider robots

Song, Y. S., Suhr, S. H., Sitti, M.

In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages: 2303-2310, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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A novel water running robot inspired by basilisk lizards

Floyd, S., Keegan, T., Palmisano, J., Sitti, M.

In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pages: 5430-5436, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Residual stress analysis in reed pipe brass tongues of historic organs

Manescu, A., Giuliani, A., Fiori, F., Baretzky, B.

In Residual Stresses VII. 7th Europen Conference on Residual Stresses (ECRS7), pages: 969-974, Trans Tech, Berlin [Germany], 2006 (inproceedings)

mms

[BibTex]

[BibTex]


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Design methodologies for central pattern generators: an application to crawling humanoids

Righetti, L., Ijspeert, A.

In Proceedings of Robotics: Science and Systems, Philadelphia, USA, August 2006 (inproceedings)

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Force-controlled microcontact printing using microassembled particle templates

Tafazzoli, A., Pawashe, C., Sitti, M.

In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages: 263-268, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Waalbot: An agile small-scale wall climbing robot utilizing pressure sensitive adhesives

Murphy, M. P., Tso, W., Tanzini, M., Sitti, M.

In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pages: 3411-3416, 2006 (inproceedings)

pi

[BibTex]

[BibTex]


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Programmable central pattern generators: an application to biped locomotion control

Righetti, L., Ijspeert, A.

In Proceedings of the IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., pages: 1585-1590, IEEE, 2006 (inproceedings)

mg

[BibTex]

[BibTex]


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High-pressure influence on the kinetics of grain boundary segregation in the Cu-Bi system

Chang, L.-S., Straumal, B., Rabkin, E., Lojkowski, W., Gust, W.

In 258-260, pages: 390-396, Aveiro (Portugal), 2006 (inproceedings)

mms

[BibTex]

[BibTex]


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Geometric Image Synthesis

Alhaija, H. A., Mustikovela, S. K., Geiger, A., Rother, C.

(conference)

avg

Project Page [BibTex]

Project Page [BibTex]