Publications of the Machine Learning Group (since 2007)
For the publication list of the group members: People
[2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006 2005, 2004, 2003]2013
S. Rangapuram, T. Buehler, and M. Hein,
Towards Realistic Team Formation in Social Networks based on Densest Subgraphs
accepted at WWW 2013
2012
M. Slawski, R. Hussong, A. Tholey, T. Jakoby, B. Gregorius, A. Hildebrandt, M. Hein
Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching,
BMC Bioinformatics 2012, 13:291 (8 November 2012) .
M. Maier, U. von Luxburg and M. Hein,
How the result of graph clustering methods depends on the construction of the graph,
ESAIM: Probability and Statistics. Link
C. Backes, A. Rurainski, G.W. Klau, O. Müller, D. Stöckel, A. Gerasch, J. Küntzer, D. Maisel, N. Ludwig, M. Hein, A. Keller, H. Burtscher, M. Kaufmann, E. Meese, H.-P. Lenhof,
An integer linear programming approach for finding deregulated subgraphs in regulatory networks,
Nucleic Acids Research, 40(6):e43. PDF
2011
M. Slawski and M. Hein,
Robust sparse recovery with non-negativity constraints,
In Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS), 2011. PDF
2010
M. Slawski and M. Hein,
Sparse Recovery for Protein Mass Spectrometry Data,
In NIPS Workshop ``Practical Application of Sparse Modeling: Open Issues and New Directions'', 2010. PDF
F. Steinke, M. Hein, B. Schoelkopf.
Non-parametric regression between general Riemannian manifolds,
SIAM Journal on Imaging Sciences, 3:527-563, 2010. PDF
U. von Luxburg, A. Radl, M. Hein.
Hitting times, commute distances and the spectral gap for large random geometric graphs,
arXiv:1003.1266v1 Link
M. Slawski, W. zu Castell, G. Tutz.
Feature Selection Guided by Structural Information,
Annals of Applied Statistics, 4:1056-1080, 2010. Link
2009
M. Hein,
Robust Nonparametric Regression with Metric-Space valued Output,
In Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta, editors, Advances in Neural Information Processing Systems 22 (NIPS 2009), 718-726, MIT Press, Cambridge, MA, 2010, PDF (Supplementary material: PDF)
K.I. Kim, F. Steinke and M. Hein,
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction,
In Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta, editors, Advances in Neural Information Processing Systems 22 (NIPS 2009), 979-987, MIT Press, Cambridge, MA, 2010, PDF (Supplementary material: PDF)
A. Keller, N. Ludwig, S. Heisel, P. Leidinger, C. Andres, W.-I. Steudel, H. Huwer, B. Burgeth, M. Hein, J. Weickert, E. Meese und H.-P. Lenhof.
Large-scale antibody profiling of human blood sera: The future of molecular diagnosis,
Informatik-Spektrum, 32:332-338, 2009. Link
M. Maier, M. Hein, U. von Luxburg.
Optimal construction of k-nearest neighbor graphs for identifying noisy clusters,
Theoretical Computer Science, 410:1749-1764, 2009. PDF
2008
F. Steinke, M. Hein,
Non-parametric Regression between Manifolds,
In D. Koller and D. Schuurmans and Y. Bengio and L. Bottou, editors, Advances in Neural Information Processing Systems 21 (NIPS 2008), 1561 - 1568, MIT Press, Cambridge, MA, 2009, PDF
M. Maier, U. von Luxburg, M. Hein,
Influence of Graph Construction on Graph-based Clustering Measures,
In D. Koller and D. Schuurmans and Y. Bengio and L. Bottou, editors, Advances in Neural Information Processing Systems 21 (NIPS 2008), 1025 - 1032, MIT Press, Cambridge, MA, 2009, PDF
Markus Maier obtained for this paper the Outstanding Student Paper Award at NIPS 2008.
M. Hein, F. Steinke, B. Schoelkopf.
Nonparametric regression between manifolds,
Oberwolfach Report 30:34-35, 2008.
P. Didyk, R. Mantiuk, M. Hein, H. P. Seidel.
Enhancement of Bright Video Features for HDR Displays,
Computer Graphics Forum, 27:1265-1274, 2008. (Proceedings of Eurographics Symposium on Rendering 2008).
F. Steinke, M. Hein, J. Peters, B. Schoelkopf.
Manifold-valued Thin-Plate Splines with Applications in Computer Graphics,
Computer Graphics Forum, 27:437-448, 2008. PDF (Proceedings of EUROGRAPHICS 2008).
M. Hein.
Binary Classification under Sample Selection Bias,
in J. Quinonero Candela, M. Sugiyama, A. Schwaighofer, N. D. Lawrence (editors), "Dataset Shift", 2008. PDF
M. Hein, F. Steinke, B. Schoelkopf.
Energy functionals for manifold-valued mappings and their properties,
Technical Report 167, Max Planck Institute for Biological Cybernetics, January 2008. PDF
2007
M. Hein, J.-Y. Audibert, U. von Luxburg.
Convergence of graph Laplacians on random neighborhood graphs,
Journal of Machine Learning Research, 8:1325-1370, 2007. PDF
M. Maier, M. Hein, U. von Luxburg.
Cluster Identification in neighborhood graphs,
In M. Hutter, R. Servedio, and E. Takimoto, editors, Proceedings of the 18th International Confererence on Algorithmic Learning Theory (ALT 2007), 196 - 210, Springer, New York, 2007, PDF
Markus Maier obtained for this paper the E. M. Gold Award (best student paper) at ALT 2007.
Corresponding technical report:
M. Maier, M. Hein, U. von Luxburg.
Cluster identification in nearest-neighbor graphs,
Technical Report 163, Max Planck Institute for Biological Cybernetics, May 2007. PDF
M. Hein, M. Maier.
Manifold Denoising for finding natural representations of data,
Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-07), 1646-1649, AAAI Press, PDF
2006
M. Hein, M. Maier.
Manifold Denoising,
In B. Schoelkopf, J. Platt, and T. Hofmann, editors, Advances in Neural Information Processing Systems 19 (NIPS 2006), 561 - 568, MIT Press, Cambridge, MA, 2007, PDF
M. Hein.
Uniform convergence of adaptive graph-based regularization,
In G. Lugosi and H. U. Simon, editors, Proceedings of the 19th Annual Conference on Learning Theory (COLT 2006), 50-64, Springer, New York, 2006, PDF
2005
M. Hein, O. Bousquet, B. Schoelkopf.
Maximal margin classification for metric spaces,
Journal of Computer and System Sciences, 71:333-359, 2005. PDF
M. Hein, J.-Y. Audibert.
Intrinsic dimensionality estimation of submanifolds in Euclidean space,
In L. de Raedt and S. Wrobel, editors, Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), 289 - 296, ACM press, 2005, PDF
M. Hein, J.-Y. Audibert, U. von Luxburg.
From graphs to manifolds - weak and strong pointwise consistency of graph Laplacians,
In R. Meir and P. Auer, editors, Proceedings of the 18th Conference on Learning Theory (COLT 2005), 470-485, Springer, New York, 2005, PDF
This paper has won a best student paper award at COLT 2005.
M. Hein and O. Bousquet.
Hilbertian metrics and positive definite kernels on probability measures,
In Z. Ghahramani and R. Cowell, editors, Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS). Society for Artificial Intelligence and Statistics, 2005. PDF
2004
M. Hein, T. N. Lal, O. Bousquet.
Hilbertian metrics on probability measures and their application in SVMs,
In C. E. Rasmussen, H. H. Buelthoff, M. Giese, and B. Schoelkopf, editors, Proceedings of the 26th DAGM Symposium, 270-277, Springer, Berlin, 2004, PDF
Corresponding technical report:
M. Hein, O. Bousquet.
Hilbertian metrics and positive definite kernels on probability measures,
Technical Report 126, Max Planck Institute for Biological Cybernetics, July 2004. PDF
M. Hein, O. Bousquet.
Kernels, associated structures and generalizations,
Technical Report 127, Max Planck Institute for Biological Cybernetics, July 2004. PDF
2003
O. Bousquet, O. Chapelle, and M. Hein.
Measure based regularization,
In S. Thrun, L. Saul, and B. Schoelkopf, editors, Advances in Neural Information Processing Systems 16 (NIPS 2003), MIT Press, Cambridge, MA, 2004, PDF
M. Hein and O. Bousquet.
Maximal margin classification for metric spaces,
In B. Schoelkopf and M. K. Warmuth, editors, 16th Annual Conference on Learning Theory (COLT 2003), Berlin, 2003. Springer. PDF