Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Secondary metabolic pathway in plant is important for finding druggable candidate enzymes. However, there are many enzymes whose functions are still undiscovered especially in organism-specific metabolic pathways. We propose reaction graph kernels for automatically assigning the EC numbers to unknown enzymatic reactions in a metabolic network. Experiments are carried out on KEGG/REACTION database and our method successfully predicted the first three digits of the EC number with 83% accuracy.We also exhaustively predicted missing enzymatic functions in the plant secondary metabolism pathways, and evaluated our results in biochemical validity.

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2007

Saigo, H., Hattori, M., Tsuda, K. Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Positional Oligomer Importance Matrices

Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract

At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences, above all of DNA and proteins. In many cases, the most accurate classifiers are obtained by training SVMs with complex sequence kernels, for instance for transcription starts or splice sites. However, an often criticized downside of SVMs with complex kernels is that it is very hard for humans to understand the learned decision rules and to derive biological insights from them. To close this gap, we introduce the concept of positional oligomer importance matrices (POIMs) and develop an efficient algorithm for their computation. We demonstrate how they overcome the limitations of sequence logos, and how they can be used to find relevant motifs for different biological phenomena in a straight-forward way. Note that the concept of POIMs is not limited to interpreting SVMs, but is applicable to general k−mer based scoring systems.

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Sonnenburg, S., Zien, A., Philips, P., Rätsch, G. Positional Oligomer Importance Matrices NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Machine Learning Algorithms for Polymorphism Detection

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G. Machine Learning Algorithms for Polymorphism Detection NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Zien, A., Ong, C.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract

Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions.We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We utilize an extension of the multiclass support vector machine (SVM)method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets, and show that we perform better than the current state of the art. Furthermore, our method provides some insights as to which features are most useful for determining subcellular localization, which are in agreement with biological reasoning.

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Zien, A., Ong, C. An Automated Combination of Kernels for Predicting Protein Subcellular Localization NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration

Hill, NJ.

Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

Abstract

I‘ll present a perspective on Brain-Computer Interface development from T{\"u}bingen. Some of the benefits promised by BCI technology lie in the near foreseeable future, and some further away. Our motivation is to make BCI technology feasible for the people who could benefit from what it has to offer soon: namely, people in the "completely locked-in" state. I‘ll mention some of the challenges of working with this user group, and explain the specific directions they have motivated us to take in developing experimental methods, algorithms, and software.

[BibTex]

Hill, NJ. Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

[BibTex]

Policy Learning for Robotics

Peters, J.

14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

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Peters, J. Policy Learning for Robotics 14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

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Hilbert Space Representations of Probability Distributions

Gretton, A.

2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

Abstract

Many problems in unsupervised learning require the analysis of features of probability distributions. At the most fundamental level, we might wish to determine whether two distributions are the same, based on samples from each - this is known as the two-sample or homogeneity problem. We use kernel methods to address this problem, by mapping probability distributions to elements in a reproducing kernel Hilbert space (RKHS). Given a sufficiently rich RKHS, these representations are unique: thus comparing feature space representations allows us to compare distributions without ambiguity. Applications include testing whether cancer subtypes are distinguishable on the basis of DNA microarray data, and whether low frequency oscillations measured at an electrode in the cortex have a different distribution during a neural spike. A more difficult problem is to discover whether two random variables drawn from a joint distribution are independent. It turns out that any dependence between pairs of random variables can be encoded in a cross-covariance operator between appropriate RKHS representations of the variables, and we may test independence by looking at a norm of the operator. We demonstrate this independence test by establishing dependence between an English text and its French translation, as opposed to French text on the same topic but otherwise unrelated. Finally, we show that this operator norm is itself a difference in feature means.

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Gretton, A. Hilbert Space Representations of Probability Distributions 2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

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Regression with Intervals

Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A.

International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

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Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A. Regression with Intervals International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

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Bayesian Estimators for Robins-Ritovs Problem

Harmeling, S., Toussaint, M.

(EDI-INF-RR-1189), School of Informatics, University of Edinburgh, October 2007 (techreport)

Abstract

Bayesian or likelihood-based approaches to data analysis became very popular in the ﬁeld of Machine Learning. However, there exist theoretical results which question the general applicability of such approaches; among those a result by Robins and Ritov which introduce a speciﬁc example for which they prove that a likelihood-based estimator will fail (i.e. it does for certain cases not converge to a true parameter estimate, even given inﬁnite data). In this paper we consider various approaches to formulate likelihood-based estimators in this example, basically by considering various extensions of the presumed generative model of the data. We can derive estimators which are very similar to the classical Horvitz-Thompson and which also account for a priori knowledge of an observation probability function.

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Harmeling, S., Toussaint, M. Bayesian Estimators for Robins-Ritovs Problem (EDI-INF-RR-1189), School of Informatics, University of Edinburgh, October 2007 (techreport)

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MR-Based PET Attenuation Correction: Method and Validation

Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B.

Joint Molecular Imaging Conference, September 2007 (talk)

Abstract

PET/MR combines the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET). For quantitative PET information, correction of tissue photon attenuation is mandatory. Usually in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating source, or from the CT scan in case of combined PET/CT. In the case of a PET/MR scanner, there is insufficient space for the rotating source and ideally one would want to calculate the attenuation map from the MR image instead. Since MR images provide information about proton density of the different tissue types, it is not trivial to use this data for PET attenuation correction. We present a method for predicting the PET attenuation map from a given the MR image, using a combination of atlas-registration and recognition of local patterns. Using "leave one out cross validation" we show on a database of 16 MR-CT image pairs that our method reliably allows estimating the CT image from the MR image. Subsequently, as in PET/CT, the PET attenuation map can be predicted from the CT image. On an additional dataset of MR/CT/PET triplets we quantitatively validate that our approach allows PET quantification with an error that is smaller than what would be clinically significant. We demonstrate our approach on T1-weighted human brain scans. However, the presented methods are more general and current research focuses on applying the established methods to human whole body PET/MRI applications.

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Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B. MR-Based PET Attenuation Correction: Method and Validation Joint Molecular Imaging Conference, September 2007 (talk)

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Learning with Transformation Invariant Kernels

Walder, C., Chapelle, O.

(165), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2007 (techreport)

Abstract

Abstract. This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation invariant, only conditionally positive definite (c.p.d.) ones. Accordingly, we discuss the c.p.d. case and provide some novel analysis, including an elementary derivation of a c.p.d. representer theorem. On the practical side, we give a support vector machine (s.v.m.) algorithm for arbitrary c.p.d. kernels. For the thin-plate kernel this leads to a classifier with only one parameter (the amount of regularisation), which we demonstrate to be as effective as an s.v.m. with the Gaussian kernel, even though the Gaussian involves a second parameter (the length scale).

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Walder, C., Chapelle, O. Learning with Transformation Invariant Kernels (165), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2007 (techreport)

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Scalable Semidefinite Programming using Convex Perturbations

Kulis, B., Sra, S., Jegelka, S.

(TR-07-47), University of Texas, Austin, TX, USA, September 2007 (techreport)

Abstract

Several important machine learning problems can be modeled and solved via semidefinite programs. Often, researchers invoke off-the-shelf software for the associated optimization, which can be inappropriate for many applications due to computational and storage requirements. In this paper, we introduce the use of convex perturbations for semidefinite programs (SDPs). Using a particular perturbation function, we arrive at an algorithm for SDPs that has several advantages over existing techniques: a) it is simple, requiring only a few lines of MATLAB, b) it is a first-order method which makes it scalable, c) it can easily exploit the structure of a particular SDP to gain efficiency (e.g., when the constraint matrices are low-rank). We demonstrate on several machine learning applications that the proposed algorithm is effective in finding fast approximations to large-scale SDPs.

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Kulis, B., Sra, S., Jegelka, S. Scalable Semidefinite Programming using Convex Perturbations (TR-07-47), University of Texas, Austin, TX, USA, September 2007 (techreport)

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Bayesian methods for NMR structure determination

Habeck, M.

29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

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Habeck, M. Bayesian methods for NMR structure determination 29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

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Sparse Multiscale Gaussian Process Regression

Walder, C., Kim, K., Schölkopf, B.

(162), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

Abstract

Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with arbitrary diagonal covariance matrices (or length scales). For a fixed number of basis functions and any given criteria, this additional flexibility permits approximations no worse and typically better than was previously possible. Although we focus on g.p. regression, the central idea is applicable to all kernel based algorithms, such as the support vector machine. We perform gradient based optimisation of the marginal likelihood, which costs O(m2n) time where n is the number of data points, and compare the method to various other sparse g.p. methods. Our approach outperforms the other methods, particularly for the case of very few basis functions, i.e. a very high sparsity ratio.

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Walder, C., Kim, K., Schölkopf, B. Sparse Multiscale Gaussian Process Regression (162), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

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Efficient Subwindow Search for Object Localization

Blaschko, M., Hofmann, T., Lampert, C.

(164), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

Abstract

Recent years have seen huge advances in object recognition from images. Recognition rates beyond 95% are the rule rather than the exception on many datasets. However, most state-of-the-art methods can only decide if an object is present or not. They are not able to provide information on the object location or extent within in the image. We report on a simple yet powerful scheme that extends many existing recognition methods to also perform localization of object bounding boxes. This is achieved by maximizing the classification score over all possible subrectangles in the image. Despite the impression that this would be computationally intractable, we show that in many situations efficient algorithms exist which solve a generalized maximum subrectangle problem. We show how our method is applicable to a variety object detection frameworks and demonstrate its performance by applying it to the popular bag of visual words model, achieving competitive results on the PASCAL VOC 2006 dataset.

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Blaschko, M., Hofmann, T., Lampert, C. Efficient Subwindow Search for Object Localization (164), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

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Collaborative Filtering via Ensembles of Matrix Factorizations

Wu, M.

KDD Cup and Workshop, August 2007 (talk)

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Wu, M. Collaborative Filtering via Ensembles of Matrix Factorizations KDD Cup and Workshop, August 2007 (talk)

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Thinking Out Loud: Research and Development of Brain Computer Interfaces

Hill, NJ.

Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

Abstract

My principal interest is in applying machine-learning methods to the development of Brain-Computer Interfaces (BCI). This involves the classification of a user‘s intentions or mental states, or regression against some continuous intentional control signal, using brain signals obtained for example by EEG, ECoG or MEG. The long-term aim is to develop systems that a completely paralysed person (such as someone suffering from advanced Amyotrophic Lateral Sclerosis) could use to communicate. Such systems have the potential to improve the lives of many people who would be otherwise completely unable to communicate, but they are still very much in the research and development stages.

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Hill, NJ. Thinking Out Loud: Research and Development of Brain Computer Interfaces Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

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Local Learning Algorithms for Transductive Classification, Clustering and Data Projection

Wu, M.

Yahoo Inc., July 2007 (talk)

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Wu, M. Local Learning Algorithms for Transductive Classification, Clustering and Data Projection Yahoo Inc., July 2007 (talk)

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Dirichlet Process Mixtures of Factor Analysers

Görür, D., Rasmussen, C.

Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

Abstract

Mixture of factor analysers (MFA) is a well-known model that combines the dimensionality reduction technique of Factor Analysis (FA) with mixture modeling. The key issue in MFA is deciding on the latent dimension and the number of mixture components to be used. The Bayesian treatment of MFA has been considered by Beal and Ghahramani (2000) using variational approximation and by Fokoué and Titterington (2003) using birth-and death Markov chain Monte Carlo (MCMC). Here, we present the nonparametric MFA model utilizing a Dirichlet process (DP) prior on the component parameters (that is, the factor loading matrix and the mean vector of each component) and describe an MCMC scheme for inference. The clustering property of the DP provides automatic selection of the number of mixture components. The latent dimensionality of each component is inferred by automatic relevance determination (ARD). Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging clustering problem. We apply our model for clustering the waveforms recorded from the cortex of a macaque monkey.

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Görür, D., Rasmussen, C. Dirichlet Process Mixtures of Factor Analysers Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

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New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams

Hill, N., Raths, C.

Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

Abstract

When considering Brain-Computer Interface (BCI) development for patients in the most severely paralysed states, there is considerable motivation to move away from BCI systems based on either motor cortex activity, or on visual stimuli. Together these account for most of current BCI research. I present the results of our recent exploration of new auditory- and tactile-stimulus-driven BCIs. The talk includes a tutorial on the construction and interpretation of classifiers which extract spatio-temporal features from event-related potential data. The effects and implications of whitening are discussed, and preliminary results on the effectiveness of a low-rank constraint (Tomioka and Aihara 2007) are shown.

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Hill, N., Raths, C. New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

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Towards Motor Skill Learning in Robotics

Peters, J.

Interactive Robot Learning - RSS workshop, June 2007 (talk)

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Peters, J. Towards Motor Skill Learning in Robotics Interactive Robot Learning - RSS workshop, June 2007 (talk)

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Transductive Support Vector Machines for Structured Variables

Zien, A., Brefeld, U., Scheffer, T.

International Conference on Machine Learning (ICML), June 2007 (talk)

Abstract

We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.

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Zien, A., Brefeld, U., Scheffer, T. Transductive Support Vector Machines for Structured Variables International Conference on Machine Learning (ICML), June 2007 (talk)

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Pattern detection

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S.

United States Patent, No 7236626, June 2007 (patent)

[BibTex]

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S. Pattern detection United States Patent, No 7236626, June 2007 (patent)

[BibTex]

Impact of target-to-target interval on classification performance in the P300 speller

Martens, S., Hill, J., Farquhar, J., Schölkopf, B.

Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

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Martens, S., Hill, J., Farquhar, J., Schölkopf, B. Impact of target-to-target interval on classification performance in the P300 speller Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

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Cluster Identification in Nearest-Neighbor Graphs

Maier, M., Hein, M., von Luxburg, U.

(163), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, May 2007 (techreport)

Abstract

Assume we are given a sample of points from some underlying distribution which contains several distinct clusters. Our goal is to construct a neighborhood graph on the sample points such that clusters are ``identified‘‘: that is, the subgraph induced by points from the same cluster is connected, while subgraphs corresponding to different clusters are not connected to each other. We derive bounds on the probability that cluster identification is successful, and use them to predict ``optimal‘‘ values of k for the mutual and symmetric k-nearest-neighbor graphs. We point out different properties of the mutual and symmetric nearest-neighbor graphs related to the cluster identification problem.

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Maier, M., Hein, M., von Luxburg, U. Cluster Identification in Nearest-Neighbor Graphs (163), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, May 2007 (techreport)

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Benchmarking of Policy Gradient Methods

Peters, J.

ADPRL Workshop, April 2007 (talk)

[BibTex]

Peters, J. Benchmarking of Policy Gradient Methods ADPRL Workshop, April 2007 (talk)

[BibTex]

Exploring model selection techniques for nonlinear dimensionality reduction

Harmeling, S.

(EDI-INF-RR-0960), School of Informatics, University of Edinburgh, March 2007 (techreport)

Abstract

Nonlinear dimensionality reduction (NLDR) methods have become useful tools for practitioners who are faced with the analysis of high-dimensional data. Of course, not all NLDR methods are equally applicable to a particular dataset at hand. Thus it would be useful to come up with model selection criteria that help to choose among diﬀerent NLDR algorithms. This paper explores various approaches to this problem and evaluates them on controlled data sets. Comprehensive experiments will show that model selection scores based on stability are not useful, while scores based on Gaussian processes are helpful for the NLDR problem.

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Harmeling, S. Exploring model selection techniques for nonlinear dimensionality reduction (EDI-INF-RR-0960), School of Informatics, University of Edinburgh, March 2007 (techreport)

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Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach

Chiappa, S., Barber, D.

(161), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, March 2007 (techreport)

Abstract

We describe two related models to cluster multidimensional time-series under the assumption of an underlying linear Gaussian dynamical process. In the first model, times-series are assigned to the same cluster when they show global similarity in their dynamics, while in the second model times-series are assigned to the same cluster when they show simultaneous similarity. Both models are based on Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models in order to (semi) automatically determine an appropriate number of components in the mixture, and to additionally bias the components to a parsimonious parameterization. The resulting models are formally intractable and to deal with this we describe a deterministic approximation based on a novel implementation of Variational Bayes.

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Chiappa, S., Barber, D. Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach (161), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, March 2007 (techreport)

PDF [BibTex]

Modeling data using directional distributions: Part II

Sra, S., Jain, P., Dhillon, I.

(TR-07-05), University of Texas, Austin, TX, USA, February 2007 (techreport)

Abstract

High-dimensional data is central to most data mining applications, and only recently has it been modeled via directional distributions. In [Banerjee et al., 2003] the authors introduced the use of the von Mises-Fisher (vMF) distribution for modeling high-dimensional directional data, particularly for text and gene expression analysis. The vMF distribution is one of the simplest directional distributions. TheWatson, Bingham, and Fisher-Bingham distributions provide distri- butions with an increasing number of parameters and thereby commensurately increased modeling power. This report provides a followup study to the initial development in [Banerjee et al., 2003] by presenting Expectation Maximization (EM) procedures for estimating parameters of a mixture of Watson (moW) distributions. The numerical challenges associated with parameter estimation for both of these distributions are significantly more difficult than for the vMF distribution. We develop new numerical approximations for estimating the parameters permitting us to model real- life data more accurately. Our experimental results establish that for certain data sets improved modeling power translates into better results.

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Sra, S., Jain, P., Dhillon, I. Modeling data using directional distributions: Part II (TR-07-05), University of Texas, Austin, TX, USA, February 2007 (techreport)

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Automatic 3D Face Reconstruction from Single Images or Video

Breuer, P., Kim, K., Kienzle, W., Blanz, V., Schölkopf, B.

(160), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2007 (techreport)

Abstract

This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression-and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high-resolution 3D surface model.

PDF [BibTex]

Breuer, P., Kim, K., Kienzle, W., Blanz, V., Schölkopf, B. Automatic 3D Face Reconstruction from Single Images or Video (160), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2007 (techreport)

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New Margin- and Evidence-Based Approaches for EEG Signal Classification

Hill, N., Farquhar, J.

Invited talk at the FaSor Jahressymposium, February 2007 (talk)

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Hill, N., Farquhar, J. New Margin- and Evidence-Based Approaches for EEG Signal Classification Invited talk at the FaSor Jahressymposium, February 2007 (talk)

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Relative Entropy Policy Search

Peters, J.

CLMC Technical Report: TR-CLMC-2007-2, Computational Learning and Motor Control Lab, Los Angeles, CA, 2007, clmc (techreport)

Abstract

This technical report describes a cute idea of how to create new policy search approaches. It directly relates to the Natural Actor-Critic methods but allows the derivation of one shot solutions. Future work may include the application to interesting problems.

PDF link (url) [BibTex]

Peters, J. Relative Entropy Policy Search CLMC Technical Report: TR-CLMC-2007-2, Computational Learning and Motor Control Lab, Los Angeles, CA, 2007, clmc (techreport)

PDF link (url) [BibTex]

2002

Kernel Dependency Estimation

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

(98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

Abstract

We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. Output kernels also make it possible to encode prior information and/or invariances in the loss function in an elegant way. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images.

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2002

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V. Kernel Dependency Estimation (98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

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Global Geometry of SVM Classifiers

Zhou, D., Xiao, B., Zhou, H., Dai, R.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)

Abstract

We construct an geometry framework for any norm Support Vector Machine (SVM) classifiers. Within this framework, separating hyperplanes, dual descriptions and solutions of SVM classifiers are constructed by a purely geometric fashion. In contrast with the optimization theory used in SVM classifiers, we have no complicated computations any more. Each step in our theory is guided by elegant geometric intuitions.

PDF PostScript [BibTex]

Zhou, D., Xiao, B., Zhou, H., Dai, R. Global Geometry of SVM Classifiers Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)

PDF PostScript [BibTex]

Computationally Efficient Face Detection

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

(MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

Web [BibTex]

Romdhani, S., Torr, P., Schölkopf, B., Blake, A. Computationally Efficient Face Detection (MSR-TR-2002-69), Microsoft Research, June 2002 (techreport)

Web [BibTex]

Kernel-based nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.

EU-Project BLISS, January 2002 (techreport)

GZIP [BibTex]

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K. Kernel-based nonlinear blind source separation EU-Project BLISS, January 2002 (techreport)

GZIP [BibTex]

Transductive Learning: Motivation, Models, Algorithms

Bousquet, O.

January 2002 (talk)

PDF [BibTex]

Bousquet, O. Transductive Learning: Motivation, Models, Algorithms January 2002 (talk)

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A compression approach to support vector model selection

von Luxburg, U., Bousquet, O., Schölkopf, B.

(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

Abstract

In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds we construct ``compression coefficients'' for SVMs, which measure the amount by which the training labels can be compressed by some classification hypothesis. The main idea is to relate the coding precision of this hypothesis to the width of the margin of the SVM. The compression coefficients connect well known quantities such as the radius-margin ratio R^2/rho^2, the eigenvalues of the kernel matrix and the number of support vectors. To test whether they are useful in practice we ran model selection experiments on several real world datasets. As a result we found that compression coefficients can fairly accurately predict the parameters for which the test error is minimized.

[BibTex]

von Luxburg, U., Bousquet, O., Schölkopf, B. A compression approach to support vector model selection (101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)

[BibTex]

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.

Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

Web [BibTex]

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B. Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2002 (techreport)

Web [BibTex]

Observations on the Nyström Method for Gaussian Process Prediction

Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

Abstract

A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nystr{\"o}m method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nystr{\"o}m method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nystr{\"o}m approximation would be expected to provide a good approximation to exact GP regression.

PostScript [BibTex]

Williams, C., Rasmussen, C., Schwaighofer, A., Tresp, V. Observations on the Nyström Method for Gaussian Process Prediction Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2002 (techreport)

PostScript [BibTex]

2001

Kernel Methods for Extracting Local Image Semantics

Bradshaw, B., Schölkopf, B., Platt, J.

(MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

Web [BibTex]

2001

Bradshaw, B., Schölkopf, B., Platt, J. Kernel Methods for Extracting Local Image Semantics (MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

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Calibration of Digital Amateur Cameras

Urbanek, M., Horaud, R., Sturm, P.

(RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Web [BibTex]

Urbanek, M., Horaud, R., Sturm, P. Calibration of Digital Amateur Cameras (RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Web [BibTex]