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2015


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easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies

Grimm, Dominik

Eberhard Karls Universität Tübingen, November 2015 (phdthesis)

[BibTex]

2015

[BibTex]


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Causal Discovery Beyond Conditional Independences

Sgouritsa, E.

Eberhard Karls Universität Tübingen, Germany, October 2015 (phdthesis)

link (url) [BibTex]

link (url) [BibTex]


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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

[BibTex]

[BibTex]


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From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding

Muandet, K.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

[BibTex]

[BibTex]


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Machine Learning Approaches to Image Deconvolution

Schuler, C.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

[BibTex]

[BibTex]


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Kernel methods in medical imaging

Charpiat, G., Hofmann, M., Schölkopf, B.

In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Blind Retrospective Motion Correction of MR Images

Loktyushin, A.

University of Tübingen, Germany, May 2015 (phdthesis)

[BibTex]

[BibTex]


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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

[BibTex]

[BibTex]


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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

[BibTex]


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Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

O’Donnell, L. J., Schultz, T.

In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

[BibTex]

[BibTex]


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

Hohmann, M.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

[BibTex]


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Sequential Image Deconvolution Using Probabilistic Linear Algebra

Gao, M.

Technical University of Munich, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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Causal Inference in Neuroimaging

Casarsa de Azevedo, L.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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The effect of frowning on attention

Ibarra Chaoul, A.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

DOI [BibTex]

DOI [BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

link (url) [BibTex]

2014


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Modeling the polygenic architecture of complex traits

Rakitsch, Barbara

Eberhard Karls Universität Tübingen, November 2014 (phdthesis)

[BibTex]

2014

[BibTex]


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Unsupervised identification of neural events in local field potentials

Besserve, M., Schölkopf, B., Logothetis, N. K.

44th Annual Meeting of the Society for Neuroscience (Neuroscience), 2014 (talk)

[BibTex]

[BibTex]


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A Novel Causal Inference Method for Time Series

Shajarisales, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (mastersthesis)

PDF [BibTex]

PDF [BibTex]


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Quantifying statistical dependency

Besserve, M.

Research Network on Learning Systems Summer School, 2014 (talk)

[BibTex]

[BibTex]


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Single-Source Domain Adaptation with Target and Conditional Shift

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

[BibTex]

[BibTex]


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Higher-Order Tensors in Diffusion Imaging

Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.

In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, pages: 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (inbook)

[BibTex]

[BibTex]


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Fuzzy Fibers: Uncertainty in dMRI Tractography

Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G.

In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, pages: 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (inbook)

[BibTex]

[BibTex]


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A global analysis of extreme events and consequences for the terrestrial carbon cycle

Zscheischler, J.

Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

[BibTex]

[BibTex]


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Nonconvex Proximal Splitting with Computational Errors

Sra, S.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (inbook)

[BibTex]

[BibTex]


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Development of advanced methods for improving astronomical images

Schmeißer, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

[BibTex]

[BibTex]


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The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection

Lacosse, E.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

[BibTex]

[BibTex]


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Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size

Huang, B.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

[BibTex]

[BibTex]


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Active Learning - Modern Learning Theory

Balcan, M., Urner, R.

In Encyclopedia of Algorithms, (Editors: Kao, M.-Y.), Springer Berlin Heidelberg, 2014 (incollection)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Analysis of Distance Functions in Graphs

Alamgir, M.

University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)

[BibTex]

[BibTex]

2013


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Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

PDF [BibTex]

2013

PDF [BibTex]


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Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

Web [BibTex]

Web [BibTex]


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A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)

Grosse-Wentrup, M., Schölkopf, B.

In Brain-Computer Interface Research, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Semi-supervised learning in causal and anticausal settings

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.

In Empirical Inference, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

DOI [BibTex]

DOI [BibTex]


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Tractable large-scale optimization in machine learning

Sra, S.

In Tractability: Practical Approaches to Hard Problems, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

[BibTex]

[BibTex]


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Modelling and Learning Approaches to Image Denoising

Burger, HC.

Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

[BibTex]

[BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

PDF [BibTex]

PDF [BibTex]


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Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

[BibTex]

[BibTex]


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On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension

Seldin, Y., Schölkopf, B.

In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

[BibTex]

[BibTex]


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Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis

Mülling, K.

Technical University Darmstadt, Germany, 2013 (phdthesis)

[BibTex]

2009


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Machine Learning for Brain-Computer Interfaces

Hill, NJ.

Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

Abstract
Brain-computer interfaces (BCI) aim to be the ultimate in assistive technology: decoding a user‘s intentions directly from brain signals without involving any muscles or peripheral nerves. Thus, some classes of BCI potentially offer hope for users with even the most extreme cases of paralysis, such as in late-stage Amyotrophic Lateral Sclerosis, where nothing else currently allows communication of any kind. Other lines in BCI research aim to restore lost motor function in as natural a way as possible, reconnecting and in some cases re-training motor-cortical areas to control prosthetic, or previously paretic, limbs. Research and development are progressing on both invasive and non-invasive fronts, although BCI has yet to make a breakthrough to widespread clinical application. The high-noise high-dimensional nature of brain-signals, particularly in non-invasive approaches and in patient populations, make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn‘t matter what classifier you use once you‘ve done your preprocessing right and extracted the right features." I shall show a few examples of how this runs counter to both the empirical reality and the spirit of what needs to be done to bring BCI into clinical application. Along the way I‘ll highlight some of the interesting problems that remain open for machine-learners.

PDF Web Web [BibTex]

2009

PDF Web Web [BibTex]


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PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y.

NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

Abstract
Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of "how many clusters are present in the data?", and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering‘s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

PDF Web Web [BibTex]

PDF Web Web [BibTex]