45 results
(View BibTeX file of all listed publications)

**Actively Learning Gaussian Process Dynamics**
2019 (techreport) Submitted

**Prototyping Micro- and Nano-Optics with Focused Ion Beam Lithography**
SL48, pages: 46, SPIE.Spotlight, SPIE Press, Bellingham, WA, 2019 (book)

ics
Solowjow, F., Trimpe, S.
**Event-triggered Learning**
2019 (techreport) Submitted

**Event-triggered Learning for Linear Quadratic Control**
2019 (techreport)

**Supplemental material for ’Communication Rate Analysis for Event-based State Estimation’**
Max Planck Institute for Intelligent Systems, January 2016 (techreport)

**Puppet Flow**
(7), Max Planck Institute for Intelligent Systems, October 2013 (techreport)

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

**Learning and Optimization with Submodular Functions**
ArXiv, May 2013 (techreport)

**A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them**
(CS-10-03), Brown University, Department of Computer Science, January 2013 (techreport)

**MR-Based Attenuation Correction for Combined Brain PET/MR: Robustness of Atlas- and Pattern Recognition Method to Atlas Registration Failures**
IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE MIC), 2013 (talk)

**Animating Samples from Gaussian Distributions**
(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

**Domain Generalization via Invariant Feature Representation**
30th International Conference on Machine Learning (ICML2013), 2013 (talk)

**Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era**
*arXiv:1309.0653*, 2013 (techreport)

**Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars**
*arXiv:1309.0654*, 2013 (techreport)

**Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik**
Springer, 2013 (book)

**A Kernel Method for the Two-Sample-Problem**
20th Annual Conference on Neural Information Processing Systems (NIPS), December 2006 (talk)

**Ab-initio gene finding using machine learning**
NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

**Graph boosting for molecular QSAR analysis**
NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)

**Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions**
NIPS Workshop on Causality and Feature Selection, December 2006 (talk)

**Minimal Logical Constraint Covering Sets**
(155), Max Planck Institute for Biological Cybernetics, Tübingen, December 2006 (techreport)

**Learning Optimal EEG Features Across Time, Frequency and Space**
NIPS Workshop on Current Trends in Brain-Computer Interfacing, December 2006 (talk)

**Semi-Supervised Learning**
Advanced Methods in Sequence Analysis Lectures, November 2006 (talk)

**New Methods for the P300 Visual Speller**
(1), (Editors: Hill, J. ), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2006 (techreport)

**A Machine Learning Approach for Determining the PET Attenuation Map from Magnetic Resonance Images**
IEEE Medical Imaging Conference, November 2006 (talk)

**Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function**
(PA006080), National ICT Australia, Canberra, Australia, October 2006 (techreport)

**Semi-Supervised Support Vector Machines and Application to Spam Filtering**
ECML Discovery Challenge Workshop, September 2006 (talk)

**Semi-Supervised Learning**
pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

**Inferential Structure Determination: Probabilistic determination and validation of NMR structures**
Gordon Research Conference on Computational Aspects of Biomolecular
NMR, September 2006 (talk)

**A tutorial on spectral clustering**
(149), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)

**Machine Learning Algorithms for Polymorphism Detection**
2nd ISCB Student Council Symposium, August 2006 (talk)

**Towards the Inference of Graphs on Ordered Vertexes**
(150), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)

**Inferential structure determination: Overview and new developments**
Sixth CCPN Annual Conference: Efficient and Rapid Structure Determination by NMR, July 2006 (talk)

**MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models**
ICML Workshop on Learning with Nonparametric Bayesian Methods, June 2006 (talk)

**Sampling for non-conjugate infinite latent feature models**
(Editors: Bernardo, J. M.), 8th Valencia International Meeting on Bayesian Statistics (ISBA), June 2006 (talk)

**An Automated Combination of Sequence Motif Kernels for Predicting Protein Subcellular Localization**
(146), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006 (techreport)

**Training a Support Vector Machine in the Primal**
(147), Max Planck Institute for Biological Cybernetics, Tübingen, April 2006, The version in the "Large Scale Kernel Machines" book is more up to date. (techreport)

**An Inventory of Sequence Polymorphisms For Arabidopsis**
17th International Conference on Arabidopsis Research, April 2006 (talk)

**Cross-Validation Optimization for Structured Hessian Kernel Methods**
Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2006 (techreport)

**Gaussian Processes for Machine Learning**
pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

**Statistical Learning of LQG controllers**
*Technical Report-2006-1*, Computational Action and Vision Lab University of Minnesota, 2006, clmc (techreport)

**Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond**
pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)

**Kernel Dependency Estimation**
(98), Max Planck Institute for Biological Cybernetics, August 2002 (techreport)

**A compression approach to support vector model selection**
(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)