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2015


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Permutohedral Lattice CNNs

Kiefel, M., Jampani, V., Gehler, P. V.

In ICLR Workshop Track, ICLR, May 2015 (inproceedings)

Abstract
This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.

pdf link (url) [BibTex]

2015

pdf link (url) [BibTex]


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Adaptive information-theoretic bounded rational decision-making with parametric priors

Grau-Moya, J, Braun, DA

pages: 1-4, NIPS Workshop on Bounded Optimality and Rational Metareasoning, December 2015 (conference)

Abstract
Deviations from rational decision-making due to limited computational resources have been studied in the field of bounded rationality, originally proposed by Herbert Simon. There have been a number of different approaches to model bounded rationality ranging from optimality principles to heuristics. Here we take an information-theoretic approach to bounded rationality, where information-processing costs are measured by the relative entropy between a posterior decision strategy and a given fixed prior strategy. In the case of multiple environments, it can be shown that there is an optimal prior rendering the bounded rationality problem equivalent to the rate distortion problem for lossy compression in information theory. Accordingly, the optimal prior and posterior strategies can be computed by the well-known Blahut-Arimoto algorithm which requires the computation of partition sums over all possible outcomes and cannot be applied straightforwardly to continuous problems. Here we derive a sampling-based alternative update rule for the adaptation of prior behaviors of decision-makers and we show convergence to the optimal prior predicted by rate distortion theory. Importantly, the update rule avoids typical infeasible operations such as the computation of partition sums. We show in simulations a proof of concept for discrete action and environment domains. This approach is not only interesting as a generic computational method, but might also provide a more realistic model of human decision-making processes occurring on a fast and a slow time scale.

[BibTex]

[BibTex]


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Inference of Cause and Effect with Unsupervised Inverse Regression

Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B.

In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

Web PDF [BibTex]

Web PDF [BibTex]


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Distinguishing Cause from Effect Based on Exogeneity

Zhang, K., Zhang, J., Schölkopf, B.

In Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, pages: 261-271, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

[BibTex]

[BibTex]


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Identification of Time-Dependent Causal Model: A Gaussian Process Treatment

Huang, B., Zhang, K., Schölkopf, B.

In 24th International Joint Conference on Artificial Intelligence, Machine Learning Track, pages: 3561-3568, (Editors: Yang, Q. and Wooldridge, M.), AAAI Press, Palo Alto, California USA, IJCAI15, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Multi-Source Domain Adaptation: A Causal View

Zhang, K., Gong, M., Schölkopf, B.

In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pages: 3150-3157, AAAI Press, AAAI, 2015 (inproceedings)

Web PDF link (url) [BibTex]

Web PDF link (url) [BibTex]


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Learning of Non-Parametric Control Policies with High-Dimensional State Features

van Hoof, H., Peters, J., Neumann, G.

In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 995–1003, (Editors: Lebanon, G. and Vishwanathan, S.V.N. ), JMLR, AISTATS, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Towards a Learning Theory of Cause-Effect Inference

Lopez-Paz, D., Muandet, K., Schölkopf, B., Tolstikhin, I.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1452–1461, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

Web [BibTex]

Web [BibTex]


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BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease

Khatami, M., Schmidt-Wilcke, T., Sundgren, P., Abbasloo, A., Schölkopf, B., Schultz, T.

In 6th International Workshop on Machine Learning in Medical Imaging, 9352, pages: 52-60, Lecture Notes in Computer Science, (Editors: L. Zhou, L. Wang, Q. Wang and Y. Shi), Springer, MLMI, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Hierarchical Label Queries with Data-Dependent Partitions

Kpotufe, S., Urner, R., Ben-David, S.

In Proceedings of the 28th Conference on Learning Theory, 40, pages: 1176-1189, (Editors: Grünwald, P. and Hazan, E. and Kale, S. ), JMLR, COLT, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Semi-Autonomous 3rd-Hand Robot

Lopes, M., Peters, J., Piater, J., Toussaint, M., Baisero, A., Busch, B., Erkent, O., Kroemer, O., Lioutikov, R., Maeda, G., Mollard, Y., Munzer, T., Shukla, D.

In Workshop on Cognitive Robotics in Future Manufacturing Scenarios, European Robotics Forum, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Neural Adaptive Sequential Monte Carlo

Gu, S., Ghahramani, Z., Turner, R. E.

Advances in Neural Information Processing Systems 28, pages: 2629-2637, (Editors: Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett), 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (conference)

PDF Supplementary [BibTex]

PDF Supplementary [BibTex]


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Discovering Temporal Causal Relations from Subsampled Data

Gong, M., Zhang, K., Schölkopf, B., Tao, D., Geiger, P.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1898–1906, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Active Nearest Neighbors in Changing Environments

Berlind, C., Urner, R.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1870-1879, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D. ), JMLR, ICML, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Learning Inverse Dynamics Models with Contacts

Calandra, R., Ivaldi, S., Deisenroth, M., Rückert, E., Peters, J.

In IEEE International Conference on Robotics and Automation, pages: 3186-3191, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation

Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J.

In Proceedings of the International Symposium of Robotics Research, ISRR, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


Thumb xl 2016 peer grading
Peer grading in a course on algorithms and data structures

Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.

Workshop on Machine Learning for Education (ML4Ed) at the 32th International Conference on Machine Learning (ICML), 2015 (conference)

Arxiv [BibTex]

Arxiv [BibTex]


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Removing systematic errors for exoplanet search via latent causes

Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J.

In Proceedings of The 32nd International Conference on Machine Learning, 37, pages: 2218–2226, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D.), JMLR, ICML, 2015 (inproceedings)

Extended version on arXiv link (url) [BibTex]

Extended version on arXiv link (url) [BibTex]


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Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Geiger, P., Zhang, K., Schölkopf, B., Gong, M., Janzing, D.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1917–1925, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Brain-Computer Interfacing in Amyotrophic Lateral Sclerosis: Implications of a Resting-State EEG Analysis

Jayaram, V., Widmann, N., Förster, C., Fomina, T., Hohmann, M. R., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Proceedings of the 37th IEEE Conference for Engineering in Medicine and Biology, pages: 6979-6982, EMBC, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Identification of the Default Mode Network with Electroencephalography

Fomina, T., Hohmann, M. R., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of the 37th IEEE Conference for Engineering in Medicine and Biology, pages: 7566-7569, EMBC, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Towards Cognitive Brain-Computer Interfaces for Patients with Amyotrophic Lateral Sclerosis

Fomina, T., Schölkopf, B., Grosse-Wentrup, M.

In 7th Computer Science and Electronic Engineering Conference, pages: 77-80, Curran Associates, Inc., CEEC, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks

Kroemer, O., Daniel, C., Neumann, G., van Hoof, H., Peters, J.

In IEEE International Conference on Robotics and Automation, pages: 1503 - 1510, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

Abstract
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

Matlab research code link (url) [BibTex]

Matlab research code link (url) [BibTex]


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BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

Rothenhäusler, D., Heinze, C., Peters, J., Meinshausen, N.

Advances in Neural Information Processing Systems 28, pages: 1513-1521, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Particle Gibbs for Infinite Hidden Markov Models

Tripuraneni*, N., Gu*, S., Ge, H., Ghahramani, Z.

Advances in Neural Information Processing Systems 28, pages: 2395-2403, (Editors: Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett), 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015, *equal contribution (conference)

PDF [BibTex]

PDF [BibTex]


Thumb xl 2016 peer grading
Peer grading in a course on algorithms and data structures

Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.

Workshop on Crowdsourcing and Machine Learning (CrowdML) Workshop on Machine Learning for Education (ML4Ed) at at the 32th International Conference on Machine Learning (ICML), 2015 (conference)

Arxiv [BibTex]

Arxiv [BibTex]


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A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.

In 18th International Conference on Medical Image Computing and Computer Assisted Intervention, 9349, pages: 597-604, Lecture Notes in Computer Science, MICCAI, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Recent Methodological Advances in Causal Discovery and Inference

Spirtes, P., Zhang, K.

In 15th Conference on Theoretical Aspects of Rationality and Knowledge, pages: 23-35, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

[BibTex]

[BibTex]


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Learning Optimal Striking Points for A Ping-Pong Playing Robot

Huang, Y., Schölkopf, B., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 4587-4592, IROS, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Model-Based Relative Entropy Stochastic Search

Abdolmaleki, A., Peters, J., Neumann, G.

In Advances in Neural Information Processing Systems 28, pages: 3523-3531, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives

Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G.

In Workshop on Machine Learning for Social Robotics, ICRA, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Extracting Low-Dimensional Control Variables for Movement Primitives

Rueckert, E., Mundo, J., Paraschos, A., Peters, J., Neumann, G.

In IEEE International Conference on Robotics and Automation, pages: 1511-1518, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Self-calibration of optical lenses

Hirsch, M., Schölkopf, B.

In IEEE International Conference on Computer Vision (ICCV 2015), pages: 612-620, IEEE, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Telling cause from effect in deterministic linear dynamical systems

Shajarisales, N., Janzing, D., Schölkopf, B., Besserve, M.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M. R., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, pages: 3187-3191, SMC, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Efficient Learning of Linear Separators under Bounded Noise

Awasthi, P., Balcan, M., Haghtalab, N., Urner, R.

In Proceedings of the 28th Conference on Learning Theory, 40, pages: 167-190, (Editors: Grünwald, P. and Hazan, E. and Kale, S.), JMLR, COLT, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Learning multiple collaborative tasks with a mixture of Interaction Primitives

Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G.

In IEEE International Conference on Robotics and Automation, pages: 1535-1542, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Subspace Alignement based Domain Adaptation for RCNN detector

Raj, A., V., N., Tuytelaars, T.

Proceedings of the 26th British Machine Vision Conference (BMVC 2015), pages: 166.1-166.11, (Editors: Xianghua Xie and Mark W. Jones and Gary K. L. Tam), 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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Practical Probabilistic Programming with Monads

Ścibior, A., Ghahramani, Z., Gordon, A. D.

Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell, pages: 165-176, Haskell ’15, ACM, 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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Developing neural networks with neurons competing for survival

Peng, Z, Braun, DA

pages: 152-153, IEEE, Piscataway, NJ, USA, 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), August 2015 (conference)

Abstract
We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. We show in simulations that this naturally leads to abstractions in higher layers that emerge in a unsupervised fashion. When evaluating the network in a supervised learning paradigm, it is clear that our network is not competitive. What is interesting though is that this performance was achieved by neurons that simply struggle for survival and do not know about performance error. In contrast to most studies on neural evolution that rely on a network-wide fitness function, our goal was to show that learning behaviour can appear in a system without being driven by any specific utility function or reward signal.

DOI [BibTex]

DOI [BibTex]

2002


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Gender Classification of Human Faces

Graf, A., Wichmann, F.

In Biologically Motivated Computer Vision, pages: 1-18, (Editors: Bülthoff, H. H., S.W. Lee, T. A. Poggio and C. Wallraven), Springer, Berlin, Germany, Second International Workshop on Biologically Motivated Computer Vision (BMCV), November 2002 (inproceedings)

Abstract
This paper addresses the issue of combining pre-processing methods—dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)—with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.

PDF PDF DOI [BibTex]

2002

PDF PDF DOI [BibTex]


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Insect-Inspired Estimation of Self-Motion

Franz, MO., Chahl, JS.

In Biologically Motivated Computer Vision, (2525):171-180, LNCS, (Editors: Bülthoff, H.H. , S.W. Lee, T.A. Poggio, C. Wallraven), Springer, Berlin, Germany, Second International Workshop on Biologically Motivated Computer Vision (BMCV), November 2002 (inproceedings)

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

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Combining sensory Information to Improve Visualization

Ernst, M., Banks, M., Wichmann, F., Maloney, L., Bülthoff, H.

In Proceedings of the Conference on Visualization ‘02 (VIS ‘02), pages: 571-574, (Editors: Moorhead, R. , M. Joy), IEEE, Piscataway, NJ, USA, IEEE Conference on Visualization (VIS '02), October 2002 (inproceedings)

Abstract
Seemingly effortlessly the human brain reconstructs the three-dimensional environment surrounding us from the light pattern striking the eyes. This seems to be true across almost all viewing and lighting conditions. One important factor for this apparent easiness is the redundancy of information provided by the sensory organs. For example, perspective distortions, shading, motion parallax, or the disparity between the two eyes' images are all, at least partly, redundant signals which provide us with information about the three-dimensional layout of the visual scene. Our brain uses all these different sensory signals and combines the available information into a coherent percept. In displays visualizing data, however, the information is often highly reduced and abstracted, which may lead to an altered perception and therefore a misinterpretation of the visualized data. In this panel we will discuss mechanisms involved in the combination of sensory information and their implications for simulations using computer displays, as well as problems resulting from current display technology such as cathode-ray tubes.

PDF Web [BibTex]

PDF Web [BibTex]


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Sampling Techniques for Kernel Methods

Achlioptas, D., McSherry, F., Schölkopf, B.

In Advances in neural information processing systems 14 , pages: 335-342, (Editors: TG Dietterich and S Becker and Z Ghahramani), MIT Press, Cambridge, MA, USA, 15th Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations.

PDF Web [BibTex]

PDF Web [BibTex]


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The Infinite Hidden Markov Model

Beal, MJ., Ghahramani, Z., Rasmussen, CE.

In Advances in Neural Information Processing Systems 14, pages: 577-584, (Editors: Dietterich, T.G. , S. Becker, Z. Ghahramani), MIT Press, Cambridge, MA, USA, Fifteenth Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. These three hyperparameters define a hierarchical Dirichlet process capable of capturing a rich set of transition dynamics. The three hyperparameters control the time scale of the dynamics, the sparsity of the underlying state-transition matrix, and the expected number of distinct hidden states in a finite sequence. In this framework it is also natural to allow the alphabet of emitted symbols to be infinite - consider, for example, symbols being possible words appearing in English text.

PDF Web [BibTex]

PDF Web [BibTex]


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A new discriminative kernel from probabilistic models

Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.

In Advances in Neural Information Processing Systems 14, pages: 977-984, (Editors: Dietterich, T.G. , S. Becker, Z. Ghahramani), MIT Press, Cambridge, MA, USA, Fifteenth Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
Recently, Jaakkola and Haussler proposed a method for constructing kernel functions from probabilistic models. Their so called \Fisher kernel" has been combined with discriminative classi ers such as SVM and applied successfully in e.g. DNA and protein analysis. Whereas the Fisher kernel (FK) is calculated from the marginal log-likelihood, we propose the TOP kernel derived from Tangent vectors Of Posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments our new discriminative TOP kernel compares favorably to the Fisher kernel.

PDF Web [BibTex]

PDF Web [BibTex]


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Incorporating Invariances in Non-Linear Support Vector Machines

Chapelle, O., Schölkopf, B.

In Advances in Neural Information Processing Systems 14, pages: 609-616, (Editors: TG Dietterich and S Becker and Z Ghahramani), MIT Press, Cambridge, MA, USA, 15th Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a digit recognition task that the proposed approach is superior to the Virtual Support Vector method, which previously had been the method of choice.

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel feature spaces and nonlinear blind source separation

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

In Advances in Neural Information Processing Systems 14, pages: 761-768, (Editors: Dietterich, T. G., S. Becker, Z. Ghahramani), MIT Press, Cambridge, MA, USA, Fifteenth Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)

Abstract
In kernel based learning the data is mapped to a kernel feature space of a dimension that corresponds to the number of training data points. In practice, however, the data forms a smaller submanifold in feature space, a fact that has been used e.g. by reduced set techniques for SVMs. We propose a new mathematical construction that permits to adapt to the intrinsic dimension and to find an orthonormal basis of this submanifold. In doing so, computations get much simpler and more important our theoretical framework allows to derive elegant kernelized blind source separation (BSS) algorithms for arbitrary invertible nonlinear mixings. Experiments demonstrate the good performance and high computational efficiency of our kTDSEP algorithm for the problem of nonlinear BSS.

PDF Web [BibTex]

PDF Web [BibTex]


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Algorithms for Learning Function Distinguishable Regular Languages

Fernau, H., Radl, A.

In Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, pages: 64-73, (Editors: Caelli, T. , A. Amin, R. P.W. Duin, M. Kamel, D. de Ridder), Springer, Berlin, Germany, Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, August 2002 (inproceedings)

Abstract
Function distinguishable languages were introduced as a new methodology of defining characterizable subclasses of the regular languages which are learnable from text. Here, we give details on the implementation and the analysis of the corresponding learning algorithms. We also discuss problems which might occur in practical applications.

PDF DOI [BibTex]

PDF DOI [BibTex]