NIPS Proceedingsβ

Matthias Hein

18 Papers

  • Clustering Signed Networks with the Geometric Mean of Laplacians (2016)
  • Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods (2016)
  • Efficient Output Kernel Learning for Multiple Tasks (2015)
  • Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices (2015)
  • Top-k Multiclass SVM (2015)
  • Tight Continuous Relaxation of the Balanced k-Cut Problem (2014)
  • Matrix factorization with binary components (2013)
  • The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited (2013)
  • Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts (2011)
  • Sparse recovery by thresholded non-negative least squares (2011)
  • An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA (2010)
  • Getting lost in space: Large sample analysis of the resistance distance (2010)
  • Robust Nonparametric Regression with Metric-Space Valued Output (2009)
  • Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction (2009)
  • Influence of graph construction on graph-based clustering measures (2008)
  • Non-parametric Regression Between Manifolds (2008)
  • Manifold Denoising (2006)
  • Measure Based Regularization (2003)