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2020


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Community detection with node attributes in multilayer networks

Martina Contisciani, E. A. P., Bacco, C. D.

Nature Scientific Reports, 10, pages: 15736, September 2020 (article)

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[BibTex]

2020


[BibTex]


A little damping goes a long way: a simulation study of how damping influences task-level stability in running
A little damping goes a long way: a simulation study of how damping influences task-level stability in running

Heim, S., Millard, M., Mouel, C. L., Badri-Spröwitz, A.

Biology Letters, 16(9), September 2020 (article)

Abstract
It is currently unclear if damping plays a functional role in legged locomotion, and simple models often do not include damping terms. We present a new model with a damping term that is isolated from other parameters: that is, the damping term can be adjusted without retuning other model parameters for nominal motion. We systematically compare how increased damping affects stability in the face of unexpected ground-height perturbations. Unlike most studies, we focus on task-level stability: instead of observing whether trajectories converge towards a nominal limit-cycle, we quantify the ability to avoid falls using a recently developed mathematical measure. This measure allows trajectories to be compared quantitatively instead of only being separated into a binary classification of ‘stable' or ‘unstable'. Our simulation study shows that increased damping contributes significantly to task-level stability; however, this benefit quickly plateaus after only a small amount of damping. These results suggest that the low intrinsic damping values observed experimentally may have stability benefits and are not simply minimized for energetic reasons. All Python code and data needed to generate our results are available open source.

dlg ics

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Effective Viscous Damping Enables Morphological Computation in Legged Locomotion
Effective Viscous Damping Enables Morphological Computation in Legged Locomotion

Mo, A., Izzi, F., Haeufle, D. F. B., Badri-Spröwitz, A.

Frontiers Robots and Ai, 7:110, August 2020 (article)

Abstract
Muscle models and animal observations suggest that physical damping is beneficial for stabilization. Still, only a few implementations of mechanical damping exist in compliant robotic legged locomotion. It remains unclear how physical damping can be exploited for locomotion tasks, while its advantages as sensor-free, adaptive force- and negative work-producing actuators are promising. In a simplified numerical leg model, we studied the energy dissipation from viscous and Coulomb damping during vertical drops with ground-level perturbations. A parallel spring-damper is engaged between touch-down and mid-stance, and its damper auto-disengages during mid-stance and takeoff. Our simulations indicate that an adjustable and viscous damper is desired. In hardware we explored effective viscous damping and adjustability and quantified the dissipated energy. We tested two mechanical, leg-mounted damping mechanisms; a commercial hydraulic damper, and a custom-made pneumatic damper. The pneumatic damper exploits a rolling diaphragm with an adjustable orifice, minimizing Coulomb damping effects while permitting adjustable resistance. Experimental results show that the leg-mounted, hydraulic damper exhibits the most effective viscous damping. Adjusting the orifice setting did not result in substantial changes of dissipated energy per drop, unlike adjusting damping parameters in the numerical model. Consequently, we also emphasize the importance of characterizing physical dampers during real legged impacts to evaluate their effectiveness for compliant legged locomotion.

dlg

Youtube link (url) DOI [BibTex]

Youtube link (url) DOI [BibTex]


Event-triggered Learning
Event-triggered Learning

Solowjow, F., Trimpe, S.

Automatica, 117, Elsevier, July 2020 (article)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


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Phenomenal Causality and Sensory Realism

Meding, K., Bruijns, S. A., Schölkopf, B., Berens, P., Wichmann, F. A.

i-Perception, 11(3):1-16, June 2020 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study

Neumann-Brosig, M., Marco, A., Schwarzmann, D., Trimpe, S.

IEEE Transactions on Control Systems Technology, 28(3):730-740, May 2020 (article)

Abstract
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a user-defined cost. The probabilistic model is updated with data, which is obtained by testing a set of parameters on the physical system and evaluating the cost. In order to learn fast, the Bayesian optimization algorithm selects the next parameters to evaluate in a systematic way, for example, by maximizing information gain about the optimum. The algorithm thus iteratively finds the globally optimal parameters with only few experiments. Taking throttle valve control as a representative industrial control example, the proposed auto-tuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed auto-tuning framework is flexible and can handle different control structures and objectives.

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arXiv (PDF) DOI Project Page [BibTex]

arXiv (PDF) DOI Project Page [BibTex]


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Variational Bayes In Private Settings (VIPS)

Park, M., Foulds, J., Chaudhuri, K., Welling, M.

Journal of Artificial Intelligence Research, 68, pages: 109-157, May 2020 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Adaptation and Robust Learning of Probabilistic Movement Primitives

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

IEEE Transactions on Robotics, 36(2):366-379, IEEE, March 2020 (article)

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arXiv DOI Project Page [BibTex]

arXiv DOI Project Page [BibTex]


Trunk pitch oscillations for energy trade-offs in bipedal running birds and robots
Trunk pitch oscillations for energy trade-offs in bipedal running birds and robots

Drama, Ö., Badri-Spröwitz, A.

Bioinspiration & Biomimetics, 15(3), March 2020 (article)

Abstract
Bipedal animals have diverse morphologies and advanced locomotion abilities. Terrestrial birds, in particular, display agile, efficient, and robust running motion, in which they exploit the interplay between the body segment masses and moment of inertias. On the other hand, most legged robots are not able to generate such versatile and energy-efficient motion and often disregard trunk movements as a means to enhance their locomotion capabilities. Recent research investigated how trunk motions affect the gait characteristics of humans, but there is a lack of analysis across different bipedal morphologies. To address this issue, we analyze avian running based on a spring-loaded inverted pendulum model with a pronograde (horizontal) trunk. We use a virtual point based control scheme and modify the alignment of the ground reaction forces to assess how our control strategy influences the trunk pitch oscillations and energetics of the locomotion. We derive three potential key strategies to leverage trunk pitch motions that minimize either the energy fluctuations of the center of mass or the work performed by the hip and leg. We suggest how these strategies could be used in legged robotics.

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Youtube Video link (url) DOI [BibTex]

Youtube Video link (url) DOI [BibTex]


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DeepMAsED: evaluating the quality of metagenomic assemblies

Mineeva*, O., Rojas-Carulla*, M., Ley, R. E., Schölkopf, B. Y. N. D.

Bioinformatics, 36(10):3011-3017, Febuary 2020, *equal contribution (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Sliding Mode Control with Gaussian Process Regression for Underwater Robots

Lima, G. S., Trimpe, S., Bessa, W. M.

Journal of Intelligent & Robotic Systems, January 2020 (article)

ics

DOI [BibTex]

DOI [BibTex]


Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks

Beuchert, J., Solowjow, F., Raisch, J., Trimpe, S., Seel, T.

IEEE Control Systems Letters, 4(1):103-108, January 2020 (article)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Real Time Trajectory Prediction Using Deep Conditional Generative Models
Real Time Trajectory Prediction Using Deep Conditional Generative Models

Gomez-Gonzalez, S., Prokudin, S., Schölkopf, B., Peters, J.

IEEE Robotics and Automation Letters, 5(2):970-976, IEEE, January 2020 (article)

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arXiv DOI [BibTex]

arXiv DOI [BibTex]


Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems
Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

Baumann, D., Mager, F., Zimmerling, M., Trimpe, S.

IEEE Control Systems Letters, 4(1):127-132, January 2020 (article)

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


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An Adaptive Optimizer for Measurement-Frugal Variational Algorithms

Kübler, J. M., Arrasmith, A., Cincio, L., Coles, P. J.

Quantum, 4, pages: 263, 2020 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Counterfactual Mean Embedding

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukatat, S.

Journal of Machine Learning Research, 2020 (article) Accepted

ei

[BibTex]

[BibTex]


Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges
Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges

Baumann, D., Mager, F., Wetzker, U., Thiele, L., Zimmerling, M., Trimpe, S.

Proceedings of the IEEE, 2020 (article) Accepted

ics

arXiv DOI [BibTex]

arXiv DOI [BibTex]


Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage
Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage

Haksar, R. N., Trimpe, S., Schwager, M.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

ics

DOI [BibTex]

DOI [BibTex]


Postural Stability in Human Running with Step-down Perturbations: An Experimental and Numerical Study
Postural Stability in Human Running with Step-down Perturbations: An Experimental and Numerical Study

Oezge Drama, , Johanna Vielemeyer, , Alexander Badri-Spröwitz, , Müller, R.

2020 (article) In revision

Abstract
Postural stability is one of the most crucial elements in bipedal locomotion. Bipeds are dynamically unstable and need to maintain their trunk upright against the rotations induced by the ground reaction forces (GRFs), especially when running. Gait studies report that the GRF vectors focus around a virtual point above the center of mass (VPA), while the trunk moves forward in pitch axis during the stance phase of human running. However, a recent simulation study suggests that a virtual point below the center of mass (VPB) might be present in human running, since a VPA yields backward trunk rotation during the stance phase. In this work, we perform a gait analysis to investigate the existence and location of the VP in human running at 5 m s−1, and support our findings numerically using the spring-loaded inverted pendulum model with a trunk (TSLIP). We extend our analysis to include perturbations in terrain height (visible and camouflaged), and investigate the response of the VP mechanism to step-down perturbations both experimentally and numerically. Our experimental results show that the human running gait displays a VPB of ≈ −30 cm and a forward trunk motion during the stance phase. The camouflaged step-down perturbations affect the location of the VPB. Our simulation results suggest that the VPB is able to encounter the step-down perturbations and bring the system back to its initial equilibrium state.

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link (url) [BibTex]

link (url) [BibTex]


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Causal Discovery from Heterogeneous/Nonstationary Data

Huang, B., Zhang, K., J., Z., Ramsey, J., Sanchez-Romero, R., Glymour, C., Schölkopf, B.

Journal of Machine Learning Research, 21(89):1-53, 2020 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

Abstract
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

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arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Event-triggered Learning for Linear Quadratic Control
Event-triggered Learning for Linear Quadratic Control

Schlüter, H., Solowjow, F., Trimpe, S.

IEEE Transactions on Automatic Control, 2020 (article) Accepted

ics

arXiv [BibTex]

arXiv [BibTex]

2005


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Kernel Methods for Measuring Independence

Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

Abstract
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound near independence on the Parzen window estimate of the mutual information. Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.

ei

PDF PostScript PDF [BibTex]

2005


PDF PostScript PDF [BibTex]


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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

Abstract
We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

ei

PDF [BibTex]

PDF [BibTex]


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Method and device for detection of splice form and alternative splice forms in DNA or RNA sequences

Rätsch, G., Sonnenburg, S., Müller, K., Schölkopf, B.

European Patent Application, International No PCT/EP2005/005783, December 2005 (patent)

ei

[BibTex]

[BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

Abstract
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We propose several embeddings and recall that an isometric embedding in a Banach space is always possible while an isometric embedding in a Hilbert space is only possible for certain metric spaces. As a result, we obtain a general maximum margin classification algorithm for arbitrary metric spaces (whose solution is approximated by an algorithm of Graepel. Interestingly enough, the embedding approach, when applied to a metric which can be embedded into a Hilbert space, yields the SVM algorithm, which emphasizes the fact that its solution depends on the metric and not on the kernel. Furthermore we give upper bounds of the capacity of the function classes corresponding to both embeddings in terms of Rademacher averages. Finally we compare the capacities of these function classes directly.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Selective integration of multiple biological data for supervised network inference

Kato, T., Tsuda, K., Asai, K.

Bioinformatics, 21(10):2488 , October 2005 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Assessing Approximate Inference for Binary Gaussian Process Classification

Kuss, M., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1679 , October 2005 (article)

Abstract
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

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

Journal of Machine Learning Research, 6, pages: 1345-1382, September 2005 (article)

Abstract
Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.

ei

PDF [BibTex]

PDF [BibTex]


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Support Vector Machines for 3D Shape Processing

Steinke, F., Schölkopf, B., Blanz, V.

Computer Graphics Forum, 24(3, EUROGRAPHICS 2005):285-294, September 2005 (article)

Abstract
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SVMachine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects.

ei

PDF [BibTex]

PDF [BibTex]


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Fast Protein Classification with Multiple Networks

Tsuda, K., Shin, H., Schölkopf, B.

Bioinformatics, 21(Suppl. 2):59-65, September 2005 (article)

Abstract
Support vector machines (SVM) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced Lanckriet et al (2004). In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n^3), and produces a dense matrix of size n x n. We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similarly to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Correlation of EEG spectral entropy with regional cerebral blood flow during sevoflurane and propofol anaesthesia

Maksimow, A., Kaisti, K., Aalto, S., Mäenpää, M., Jääskeläinen, S., Hinkka, S., Martens, SMM., Särkelä, M., Viertiö-Oja, H., Scheinin, H.

Anaesthesia, 60(9):862-869, September 2005 (article)

Abstract
ENTROPY index monitoring, based on spectral entropy of the electroencephalogram, is a promising new method to measure the depth of anaesthesia. We examined the association between spectral entropy and regional cerebral blood flow in healthy subjects anaesthetised with 2%, 3% and 4% end-expiratory concentrations of sevoflurane and 7.6, 12.5 and 19.0 microg.ml(-1) plasma drug concentrations of propofol. Spectral entropy from the frequency band 0.8-32 Hz was calculated and cerebral blood flow assessed using positron emission tomography and [(15)O]-labelled water at baseline and at each anaesthesia level. Both drugs induced significant reductions in spectral entropy and cortical and global cerebral blood flow. Midfrontal-central spectral entropy was associated with individual frontal and whole brain blood flow values across all conditions, suggesting that this novel measure of anaesthetic depth can depict global changes in neuronal activity induced by the drugs. The cortical areas of the most significant associations were remarkably similar for both drugs.

ei

DOI [BibTex]

DOI [BibTex]


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Iterative Kernel Principal Component Analysis for Image Modeling

Kim, K., Franz, M., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9):1351-1366, September 2005 (article)

Abstract
In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution a nd denoising performance are comparable to existing methods.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Analyzing microarray data using quantitative association rules

Georgii, E., Richter, L., Rückert, U., Kramer, S.

Bioinformatics, 21(Suppl. 2):123-129, September 2005 (article)

Abstract
Motivation: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. Results: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Large Margin Methods for Structured and Interdependent Output Variables

Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.

Journal of Machine Learning Research, 6, pages: 1453-1484, September 2005 (article)

Abstract
Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains involving different types of output spaces emphasize the breadth and generality of our approach.

ei

PDF [BibTex]

PDF [BibTex]


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Gene Expression Profiling of Serum- and Interleukin-1beta-Stimulated Primary Human Adult Articular Chondrocytes - A Molecular Analysis Based on Chondrocytes Isolated from One Donor

Aigner, T., McKenna, L., Zien, A., Fan, Z., Gebhard, P., Zimmer, R.

Cytokine, 31(3):227-240, August 2005 (article)

Abstract
In order to understand the cellular disease mechanisms of osteoarthritic cartilage degeneration it is of primary importance to understand both the anabolic and the catabolic processes going on in parallel in the diseased tissue. In this study, we have applied cDNA-array technology (Clontech) to study gene expression patterns of primary human normal adult articular chondrocytes isolated from one donor cultured under anabolic (serum) and catabolic (IL-1beta) conditions. Significant differences between the different in vitro cultures tested were detected. Overall, serum and IL-1beta significantly altered gene expression levels of 102 and 79 genes, respectively. IL-1beta stimulated the matrix metalloproteinases-1, -3, and -13 as well as members of its intracellular signaling cascade, whereas serum increased the expression of many cartilage matrix genes. Comparative gene expression analysis with previously published in vivo data (normal and osteoarthritic cartilage) showed significant differences of all in vitro s timulations compared to the changes detected in osteoarthritic cartilage in vivo. This investigation allowed us to characterize gene expression profiles of two classical anabolic and catabolic stimuli of human adult articular chondrocytes in vitro. No in vitro model appeared to be adequate to study overall gene expression alterations in osteoarthritic cartilage. Serum stimulated in vitro cultures largely reflected the results that were only consistent with the anabolic activation seen in osteoarthritic chondrocytes. In contrast, IL-1beta did not appear to be a good model for mimicking catabolic gene alterations in degenerating chondrocytes.

ei

Web [BibTex]


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Phenotypic characterization of chondrosarcoma-derived cell lines

Schorle, C., Finger, F., Zien, A., Block, J., Gebhard, P., Aigner, T.

Cancer Letters, 226(2):143-154, August 2005 (article)

Abstract
Gene expression profiling of three chondrosarcoma derived cell lines (AD, SM, 105KC) showed an increased proliferative activity and a reduced expression of chondrocytic-typical matrix products compared to primary chondrocytes. The incapability to maintain an adequate matrix synthesis as well as a notable proliferative activity at the same time is comparable to neoplastic chondrosarcoma cells in vivo which cease largely cartilage matrix formation as soon as their proliferative activity increases. Thus, the investigated cell lines are of limited value as substitute of primary chondrocytes but might have a much higher potential to investigate the behavior of neoplastic chondrocytes, i.e. chondrosarcoma biology.

ei

Web [BibTex]

Web [BibTex]


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Local Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

The Annals of Statistics, 33(4):1497-1537, August 2005 (article)

Abstract
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Inlier-based ICA with an application to superimposed images

Meinecke, F., Harmeling, S., Müller, K.

International Journal of Imaging Systems and Technology, 15(1):48-55, July 2005 (article)

Abstract
This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals).

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Learning the Kernel with Hyperkernels

Ong, CS., Smola, A., Williamson, R.

Journal of Machine Learning Research, 6, pages: 1043-1071, July 2005 (article)

Abstract
This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional. We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming formulation of the resulting optimization problem. Several recipes for constructing hyperkernels are provided, as well as the details of common machine learning problems. Experimental results for classification, regression and novelty detection on UCI data show the feasibility of our approach.

ei

PDF [BibTex]

PDF [BibTex]


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Image Reconstruction by Linear Programming

Tsuda, K., Rätsch, G.

IEEE Transactions on Image Processing, 14(6):737-744, June 2005 (article)

Abstract
One way of image denoising is to project a noisy image to the subspace of admissible images derived, for instance, by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion. We propose a new method to identify the noisy pixels by l1-norm penalization and to update the identified pixels only. The identification and updating of noisy pixels are formulated as one linear program which can be efficiently solved. In particular, one can apply the upsilon trick to directly specify the fraction of pixels to be reconstructed. Moreover, we extend the linear program to be able to exploit prior knowledge that occlusions often appear in contiguous blocks (e.g., sunglasses on faces). The basic idea is to penalize boundary points and interior points of the occluded area differently. We are also able to show the upsilon property for this extended LP leading to a method which is easy to use. Experimental results demonstrate the power of our approach.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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RASE: recognition of alternatively spliced exons in C.elegans

Rätsch, G., Sonnenburg, S., Schölkopf, B.

Bioinformatics, 21(Suppl. 1):i369-i377, June 2005 (article)

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Tsuda, K., Rätsch, G., Warmuth, M.

Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)

Abstract
We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: on-line learning with a simple square loss, and finding a symmetric positive definite matrix subject to linear constraints. The updates generalize the exponentiated gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials to preserve positive definiteness. Most importantly, we show how the derivation and the analyses of the original EG update and AdaBoost generalize to the non-diagonal case. We apply the resulting matrix exponentiated gradient (MEG) update and DefiniteBoost to the problem of learning a kernel matrix from distance measurements.

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PDF [BibTex]

PDF [BibTex]


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Protein function prediction via graph kernels

Borgwardt, KM., Ong, CS., Schönauer, S., Vishwanathan, ., Smola, AJ., Kriegel, H-P.

Bioinformatics, 21(Suppl. 1: ISMB 2005 Proceedings):i47-i56, June 2005 (article)

Abstract
Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.

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PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination

Rosas, P., Wagemans, J., Ernst, M., Wichmann, F.

Journal of the Optical Society of America A, 22(5):801-809, May 2005 (article)

Abstract
A number of models of depth cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum variance unbiased estimator that can be constructed from the available information. Here we test such models using visual and haptic depth information. Different texture types produce differences in slant discrimination performance, providing a means for testing a reliability-sensitive cue combination model using texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability, but fell short of statistically optimal combination—we find reliability-based re-weighting, but not statistically optimal cue combination.

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PDF Web [BibTex]

PDF Web [BibTex]


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Bayesian inference for psychometric functions

Kuss, M., Jäkel, F., Wichmann, F.

Journal of Vision, 5(5):478-492, May 2005 (article)

Abstract
In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer’s ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]