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2019


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Selecting causal brain features with a single conditional independence test per feature

Mastakouri, A., Schölkopf, B., Janzing, D.

Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference) Accepted

ei

[BibTex]

2019


[BibTex]


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Neural Signatures of Motor Skill in the Resting Brain

Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), October 2019 (conference) Accepted

ei

[BibTex]

[BibTex]


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Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance

Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M.

Engineering in Medicine and Biology Conference (EMBC), July 2019 (conference) Accepted

ei

arXiv PDF [BibTex]

arXiv PDF [BibTex]


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Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 49, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 53, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 124, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Kernel Mean Matching for Content Addressability of GANs

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 3140-3151, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019, *equal contribution (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Abstract
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

ei ps

Code video demo pdf link (url) [BibTex]

Code video demo pdf link (url) [BibTex]


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Generate Semantically Similar Images with Kernel Mean Matching

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference) Accepted

ei

[BibTex]

[BibTex]


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Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Suter, R., Miladinovic, D., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 6056-6065, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Simon-Gabriel, C., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 5809-5817, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E.

In Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2931-2940, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (inproceedings)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Meta learning variational inference for prediction

Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R.

7th International Conference on Learning Representations (ICLR), May 2019 (conference) Accepted

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Lutter, M., Ritter, C., Peters, J.

7th International Conference on Learning Representations (ICLR), May 2019 (conference) Accepted

ei

link (url) [BibTex]

link (url) [BibTex]


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DeepOBS: A Deep Learning Optimizer Benchmark Suite

Schneider, F., Balles, L., Hennig, P.

7th International Conference on Learning Representations (ICLR), May 2019 (conference) Accepted

ei pn

link (url) [BibTex]

link (url) [BibTex]


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Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments

Miladinović*, D., Gondal*, M. W., Schölkopf, B., Buhmann, J. M., Bauer, S.

Deep Generative Models for Highly Structured Data Workshop at ICLR, May 2019, *equal contribution (conference) Accepted

ei

link (url) [BibTex]

link (url) [BibTex]


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SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G.

7th International Conference on Learning Representations (ICLR), May 2019 (conference) Accepted

ei

link (url) [BibTex]

link (url) [BibTex]


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Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

22nd International Conference on Artificial Intelligence and Statistics, April 2019 (conference) Accepted

ei

arXiv [BibTex]

arXiv [BibTex]


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Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features

von Kügelgen, J., Mey, A., Loog, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1361-1369, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Sobolev Descent

Mroueh, Y., Sercu, T., Raj, A.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 2976-2985, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Fast and Robust Shortest Paths on Manifolds Learned from Data

Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei pn

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs

Wenk, P., Gotovos, A., Bauer, S., Gorbach, N., Krause, A., Buhmann, J. M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1351-1360, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

PDF PDF link (url) [BibTex]

PDF PDF link (url) [BibTex]


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Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

de Roos, F., Hennig, P.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

Abstract
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.

pn ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

Abbati*, G., Wenk*, P., Osborne, M. A., Krause, A., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, 2019, *equal contribution (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Kernel Stein Tests for Multiple Model Comparison

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W.

Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

ei

[BibTex]

[BibTex]


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MYND: A Platform for Large-scale Neuroscientific Studies

Hohmann, M. R., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI), 2019 (conference) Accepted

ei

[BibTex]

[BibTex]


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A Kernel Stein Test for Comparing Latent Variable Models

Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A.

2019 (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]


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From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

2019, *equal contribution (conference) Submitted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

ei ps

arXiv [BibTex]


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Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

ei

arXiv [BibTex]

arXiv [BibTex]

2006


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Conformal Multi-Instance Kernels

Blaschko, M., Hofmann, T.

In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-6, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

Abstract
In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in the bag indicate class membership while ignoring vectors that do not. We describe here a kernel-based technique that defines a parametric family of kernels via conformal transformations and jointly learns a discriminant function over bags together with the optimal parameter settings of the kernel. Learning a conformal transformation effectively amounts to weighting regions in the feature space according to their contribution to classification accuracy; regions that are discriminative will be weighted higher than regions that are not. This allows the classifier to focus on regions contributing to classification accuracy while ignoring regions that correspond to vectors found both in positive and in negative bags. We show how parameters of this transformation can be learned for support vector machines by posing the problem as a multiple kernel learning problem. The resulting multiple instance classifier gives competitive accuracy for several multi-instance benchmark datasets from different domains.

ei

PDF Web [BibTex]

2006


PDF Web [BibTex]


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Adapting Spatial Filter Methods for Nonstationary BCIs

Tomioka, R., Hill, J., Blankertz, B., Aihara, K.

In IBIS 2006, pages: 65-70, 2006 Workshop on Information-Based Induction Sciences, November 2006 (inproceedings)

Abstract
A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the possible nonstationarity in the data from the datablock the method is trained on and that the method is applied to. Assuming the joint distributions of the whitened signal and the class label to be identical in two blocks, where the whitening is done in each block independently, we propose a simple adaptation formula that is applicable to a broad class of spatial filtering methods including ICA, CSP, and logistic regression classifiers. We characterize the class of linear transformations for which the above assumption holds. Experimental results on 60 BCI datasets show improved classification accuracy compared to (a) fixed spatial filter approach (no adaptation) and (b) fixed spatial pattern approach (proposed by Hill et al., 2006 [1]).

ei

PDF [BibTex]

PDF [BibTex]


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A Linear Programming Approach for Molecular QSAR analysis

Saigo, H., Kadowaki, T., Tsuda, K.

In MLG 2006, pages: 85-96, (Editors: Gärtner, T. , G. C. Garriga, T. Meinl), International Workshop on Mining and Learning with Graphs, September 2006, Best Paper Award (inproceedings)

Abstract
Small molecules in chemistry can be represented as graphs. In a quantitative structure-activity relationship (QSAR) analysis, the central task is to find a regression function that predicts the activity of the molecule in high accuracy. Setting a QSAR as a primal target, we propose a new linear programming approach to the graph-based regression problem. Our method extends the graph classification algorithm by Kudo et al. (NIPS 2004), which is a combination of boosting and graph mining. Instead of sequential multiplicative updates, we employ the linear programming boosting (LP) for regression. The LP approach allows to include inequality constraints for the parameter vector, which turns out to be particularly useful in QSAR tasks where activity values are sometimes unavailable. Furthermore, the efficiency is improved significantly by employing multiple pricing.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Incremental Aspect Models for Mining Document Streams

Surendran, A., Sra, S.

In PKDD 2006, pages: 633-640, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Abstract
In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call “query-line tracking” i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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PALMA: Perfect Alignments using Large Margin Algorithms

Rätsch, G., Hepp, B., Schulze, U., Ong, C.

In GCB 2006, pages: 104-113, (Editors: Huson, D. , O. Kohlbacher, A. Lupas, K. Nieselt, A. Zell), Gesellschaft für Informatik, Bonn, Germany, German Conference on Bioinformatics, September 2006 (inproceedings)

Abstract
Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task. We present a novel approach based on large margin learning that combines kernel based splice site predictions with common sequence alignment techniques. By solving a convex optimization problem, our algorithm -- called PALMA -- tunes the parameters of the model such that the true alignment scores higher than all other alignments. In an experimental study on the alignments of mRNAs containing artificially generated micro-exons, we show that our algorithm drastically outperforms all other methods: It perfectly aligns all 4358 sequences on an hold-out set, while the best other method misaligns at least 90 of them. Moreover, our algorithm is very robust against noise in the query sequence: when deleting, inserting, or mutating up to 50% of the query sequence, it still aligns 95% of all sequences correctly, while other methods achieve less than 36% accuracy. For datasets, additional results and a stand-alone alignment tool see http://www.fml.mpg.de/raetsch/projects/palma.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Graph Based Semi-Supervised Learning with Sharper Edges

Shin, H., Hill, N., Rätsch, G.

In ECML 2006, pages: 401-412, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning (ECML), September 2006 (inproceedings)

Abstract
In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points‘ (often symmetric)relationships in input space, without considering directionality. However, relationships may be more informative in one direction (e.g. from labelled to unlabelled) than in the reverse direction, and some relationships (e.g. strong weights between oppositely labelled points) are unhelpful in either direction. Undesirable edges may reduce the amount of influence an informative point can propagate to its neighbours -- the point and its outgoing edges have been ``blunted.‘‘ We present an approach to ``sharpening‘‘ in which weights are adjusted to meet an optimization criterion wherever they are directed towards labelled points. This principle can be applied to a wide variety of algorithms. In the current paper, we present one ad hoc solution satisfying the principle, in order to show that it can improve performance on a number of publicly available benchmark data sets.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle

Deisenroth, MP., Ohtsuka, T., Weissel, F., Brunn, D., Hanebeck, UD.

In MFI 2006, pages: 371-376, (Editors: Hanebeck, U. D.), IEEE Service Center, Piscataway, NJ, USA, 6th IEEE International Conference on Multisensor Fusion and Integration, September 2006 (inproceedings)

Abstract
In this paper, an approach to the finite-horizon optimal state-feedback control problem of nonlinear, stochastic, discrete-time systems is presented. Starting from the dynamic programming equation, the value function will be approximated by means of Taylor series expansion up to second-order derivatives. Moreover, the problem will be reformulated, such that a minimum principle can be applied to the stochastic problem. Employing this minimum principle, the optimal control problem can be rewritten as a two-point boundary-value problem to be solved at each time step of a shrinking horizon. To avoid numerical problems, the two-point boundary-value problem will be solved by means of a continuation method. Thus, the curse of dimensionality of dynamic programming is avoided, and good candidates for the optimal state-feedback controls are obtained. The proposed approach will be evaluated by means of a scalar example system.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Uniform Convergence of Adaptive Graph-Based Regularization

Hein, M.

In COLT 2006, pages: 50-64, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

Abstract
The regularization functional induced by the graph Laplacian of a random neighborhood graph based on the data is adaptive in two ways. First it adapts to an underlying manifold structure and second to the density of the data-generating probability measure. We identify in this paper the limit of the regularizer and show uniform convergence over the space of Hoelder functions. As an intermediate step we derive upper bounds on the covering numbers of Hoelder functions on compact Riemannian manifolds, which are of independent interest for the theoretical analysis of manifold-based learning methods.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Regularised CSP for Sensor Selection in BCI

Farquhar, J., Hill, N., Lal, T., Schölkopf, B.

In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 14-15, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

Abstract
The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10--20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Time-Dependent Demixing of Task-Relevant EEG Signals

Hill, N., Farquhar, J., Lal, T., Schölkopf, B.

In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 20-21, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

Abstract
Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP) approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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A Sober Look at Clustering Stability

Ben-David, S., von Luxburg, U., Pal, D.

In COLT 2006, pages: 5-19, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

Abstract
Stability is a common tool to verify the validity of sample based algorithms. In clustering it is widely used to tune the parameters of the algorithm, such as the number k of clusters. In spite of the popularity of stability in practical applications, there has been very little theoretical analysis of this notion. In this paper we provide a formal definition of stability and analyze some of its basic properties. Quite surprisingly, the conclusion of our analysis is that for large sample size, stability is fully determined by the behavior of the objective function which the clustering algorithm is aiming to minimize. If the objective function has a unique global minimizer, the algorithm is stable, otherwise it is unstable. In particular we conclude that stability is not a well-suited tool to determine the number of clusters - it is determined by the symmetries of the data which may be unrelated to clustering parameters. We prove our results for center-based clusterings and for spectral clustering, and support our conclusions by many examples in which the behavior of stability is counter-intuitive.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Information Marginalization on Subgraphs

Huang, J., Zhu, T., Rereiner, R., Zhou, D., Schuurmans, D.

In ECML/PKDD 2006, pages: 199-210, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Abstract
Real-world data often involves objects that exhibit multiple relationships; for example, ‘papers’ and ‘authors’ exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. ‘papers’), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world data—achieving good results in many problems.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Bayesian Active Learning for Sensitivity Analysis

Pfingsten, T.

In ECML 2006, pages: 353-364, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)

Abstract
Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore the behavior of the system, and to compute sensitivity measures as expectations over the input distribution. Monte Carlo methods are a simple approach to estimate these integrals, but they are infeasible when the models are computationally expensive. Using a Gaussian processes prior, expensive simulation runs can be saved. This Bayesian quadrature allows for an active selection of inputs where the simulation promises to be most valuable, and the number of simulation runs can be reduced further. We present an active learning scheme for sensitivity analysis which is rigorously derived from the corresponding Bayesian expected loss. On three fully featured, high dimensional physical models of electro-mechanical sensors, we show that the learning rate in the active learning scheme is significantly better than for passive learning.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Supervised Probabilistic Principal Component Analysis

Yu, S., Yu, K., Tresp, V., Kriegel, H., Wu, M.

In KDD 2006, pages: 464-473, (Editors: Ungar, L. ), ACM Press, New York, NY, USA, 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2006 (inproceedings)

Abstract
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g.,~in a classification or regression task, PCA is however not able to use this information. The problem is more interesting if only part of the input data are labeled, i.e.,~in a semi-supervised setting. In this paper we propose a supervised PCA model called SPPCA and a semi-supervised PCA model called S$^2$PPCA, both of which are extensions of a probabilistic PCA model. The proposed models are able to incorporate the label information into the projection phase, and can naturally handle multiple outputs (i.e.,~in multi-task learning problems). We derive an efficient EM learning algorithm for both models, and also provide theoretical justifications of the model behaviors. SPPCA and S$^2$PPCA are compared with other supervised projection methods on various learning tasks, and show not only promising performance but also good scalability.

ei

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PDF Web DOI [BibTex]


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A Continuation Method for Semi-Supervised SVMs

Chapelle, O., Chi, M., Zien, A.

In ICML 2006, pages: 185-192, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract
Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Trading Convexity for Scalability

Collobert, R., Sinz, F., Weston, J., Bottou, L.

In ICML 2006, pages: 201-208, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how non-convexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Personalized handwriting recognition via biased regularization

Kienzle, W., Chellapilla, K.

In ICML 2006, pages: 457-464, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract
We present a new approach to personalized handwriting recognition. The problem, also known as writer adaptation, consists of converting a generic (user-independent) recognizer into a personalized (user-dependent) one, which has an improved recognition rate for a particular user. The adaptation step usually involves user-specific samples, which leads to the fundamental question of how to fuse this new information with that captured by the generic recognizer. We propose adapting the recognizer by minimizing a regularized risk functional (a modified SVM) where the prior knowledge from the generic recognizer enters through a modified regularization term. The result is a simple personalization framework with very good practical properties. Experiments on a 100 class real-world data set show that the number of errors can be reduced by over 40% with as few as five user samples per character.

ei

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PDF Web DOI [BibTex]


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Deterministic annealing for semi-supervised kernel machines

Sindhwani, V., Keerthi, S., Chapelle, O.

In ICML 2006, pages: 841-848, (Editors: Cohen, W. W., A. Moore), ACM Press, New York, NY, USA, 23rd International Conference on Machine Learning, June 2006 (inproceedings)

Abstract
An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat the unknown labels as additional optimization variables. For margin-based loss functions, one can view this approach as attempting to learn low-density separators. However, this is a hard optimization problem to solve in typical semi-supervised settings where unlabeled data is abundant. The popular Transductive SVM algorithm is a label-switching-retraining procedure that is known to be susceptible to local minima. In this paper, we present a global optimization framework for semi-supervised Kernel machines where an easier problem is parametrically deformed to the original hard problem and minimizers are smoothly tracked. Our approach is motivated from deterministic annealing techniques and involves a sequence of convex optimization problems that are exactly and efficiently solved. We present empirical results on several synthetic and real world datasets that demonstrate the effectiveness of our approach.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]