# NEWS

- Code for the team formation problem is now available on our code webpage.
- Workshop ``Mathematical Foundations of Learning Theory'' from June 17-19, 2014, at CRM, Barcelona.
- The code for computing normalized or Cheeger hypergraph cuts according to our NIPS 2013 paper is finally online - see the code webpage.
- "Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields" by Martin Slawski and Matthias Hein
has been accepted at Linear Algebra and its Applications.
- "Hitting and Commute Times in Large Random Neighborhood Graphs" by Ulrike von Luxburg, Agnes Radl and Matthias Hein has been accepted at JMLR.
- "Scalable Multitask Representation Learning for Scene Classification" by Maksim Lapin, Matthias Hein and Bernt Schiele has been accepted at CVPR 2014.
- The paper "Learning Using Privileged Information: SVM+ and Weighted SVM" by Maksim Lapin, Matthias Hein and Bernt Schiele discussing the relationship of
SVM+ and weighted SVM has been accepted at Neural Networks.
- The paper "Non-negative least squares for high-dimensional linear models : consistency and sparse recovery without regularization" by Martin Slawski and Matthias Hein has been accepted at Electronic Journal of Statistics.
- Two papers accepted as spotlights (acceptance rate <5%) for NIPS 2013

- The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited

Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram - Matrix Factorization with Binary Components

Martin Slawski, Matthias Hein, Pavlo Lutsik

- The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
- We have improved 274 out of 816 possible best cuts in the graph partitioning benchmark of Chris Walshaw.
- We are organizing the 35th German Conference on Pattern Recognition (GCPR), 3.9.2013 - 6.9.2013, in Saarbruecken together with Joachim Weickert and Bernt Schiele.
- "Towards Realistic Team Formation in Social Networks based on Densest Subgraphs" by Shyam Rangapuram, Thomas Buehler, and Matthias Hein has been accepted at WWW 2013
- IPAM Workshop Convex Relaxation Methods for Geometric Problems in Scientific Computing at UCLA organized by Xavier Bresson, Antonin Chambolle, Tony Chan, Daniel Cremers, Stanley Osher, Thomas Pock and Gabriele Steidl.
- "Constrained fractional set programs and their application in local clustering and community detection" by Thomas Buehler, Shyam Rangapuram, Simon Setzer and Matthias Hein has been accepted at ICML 2013
- Martin's paper on non-negative least squares for deconvolution in peptide mass spectrometry has been accepted at Bioinformatics
- Matthias Hein receives
**ERC starting grant**for his project NOLEPRO - Nonlinear Eigenproblems for Data Analysis. - Ulrike von Luxburg and Matthias Hein organize a minisymposium on "Machine Learning'' at the Annual Meeting of the German Mathematical Society in Saarbruecken (17-21.9.2012).
- Minisymposium "Optimization methods in imaging and learning: From continuous to discrete and reverse'' organized by N. Thorstensen and O. Scherzer at ECCOMAS 2012 in Vienna (10-14.9.2012).
- Oberwolfach Workshop Learning Theory and Approximation (24.6.-30.6.) organized by Kurt Jetter, Steve Smale and Ding-Xuan Zhou.
- Minisymposium Modern matrix methods for large scale data and networks organized by David Gleich at the 2012 SIAM Conference on Applied Linear Algebra.
- Paper "How the result of graph clustering methods depends on the construction of the graph'' of Markus Maier, Ulrike von Luxburg and Matthias Hein accepted at ESAIM: Probability and Statistics.
- Joint paper on "An integer linear programming approach for finding deregulated subgraphs in regulatory networks'' by 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 accepted at Nucleic Acids Research.
- Sparse PCA Code by Thomas Buehler and Matthias Hein based on our NIPS 2010 paper "An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA'' can be downloaded here.
- Paper on "Constrained 1-Spectral Clustering" by Shyam Rangapuram and Matthias Hein accepted at AISTATS 2012. Paper and Matlab Code will be available soon.
- Workshop paper on "Sparse matrix factorizations with simplex constraints'' by Qinqing Zheng, Martin Slawski and Matthias Hein accepted at the NIPS 2011 Workshop "Sparse Representation and Low-rank Approximation''.
- We have achieved
**16 new best cuts**in the bipartitioning task of the graph partitioning benchmark of Chris Walshaw using our new algorithms for balanced graph partitioning. You can download the code for the Cheeger cut: HERE and the code for other balanced graph cuts will be here soon. . - Matthias Hein receives the first
**German Pattern recognition award**("Deutscher Mustererkennungspreis'') of the DAGM (former Olympus Prize). This is the highest German award in the area of pattern recognition, computer vision and machine learning. - Two papers accepted at NIPS 2011
- Martin Slawski and Matthias Hein: Sparse recovery by thresholded non-negative least squares
- Matthias Hein and Simon Setzer: Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts

**Dagstuhl seminar from 17.07.11 - 22.07.11**

**Mathematical and Computational Foundations of Learning Theory**

organized by Matthias Hein, Gabor Lugosi, Lorenzo Rosasco and Steve Smale.**New: Code homepage**

- Code online for Nonlinear eigenproblems and their application in 1-spectral Clustering and sparse PCA from our paper:

M. Hein and T. Buehler

An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA, NIPS 2010. - Code for the amplified commute kernel from our paper

U. von Luxburg, A. Radl and M. Hein

Getting lost in space: Large sample analysis of the commute distance, NIPS 2010.

- Code online for Nonlinear eigenproblems and their application in 1-spectral Clustering and sparse PCA from our paper:

**PhD and Postdoc positions available**- more details can be found here.