NIPS Proceedingsβ

Klaus-Robert Müller

35 Papers

  • Wasserstein Training of Restricted Boltzmann Machines (2016)
  • Covariance shrinkage for autocorrelated data (2014)
  • Generalizing Analytic Shrinkage for Arbitrary Covariance Structures (2013)
  • Robust Spatial Filtering with Beta Divergence (2013)
  • Learning Invariant Representations of Molecules for Atomization Energy Prediction (2012)
  • Layer-wise analysis of deep networks with Gaussian kernels (2010)
  • Efficient and Accurate Lp-Norm Multiple Kernel Learning (2009)
  • Subject independent EEG-based BCI decoding (2009)
  • Estimating vector fields using sparse basis field expansions (2008)
  • Playing Pinball with non-invasive BCI (2008)
  • Heterogeneous Component Analysis (2007)
  • Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing (2007)
  • Denoising and Dimension Reduction in Feature Space (2006)
  • Inducing Metric Violations in Human Similarity Judgements (2006)
  • Logistic Regression for Single Trial EEG Classification (2006)
  • Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach (2006)
  • Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction (2005)
  • Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction (2005)
  • Optimizing spatio-temporal filters for improving Brain-Computer Interfacing (2005)
  • Increase Information Transfer Rates in BCI by CSP Extension to Multi-class (2003)
  • Clustering with the Fisher Score (2002)
  • Combining Features for BCI (2002)
  • Going Metric: Denoising Pairwise Data (2002)
  • A New Discriminative Kernel From Probabilistic Models (2001)
  • Classifying Single Trial EEG: Towards Brain Computer Interfacing (2001)
  • Estimating the Reliability of ICA Projections (2001)
  • Kernel Feature Spaces and Nonlinear Blind Souce Separation (2001)
  • A Mathematical Programming Approach to the Kernel Fisher Algorithm (2000)
  • Invariant Feature Extraction and Classification in Kernel Spaces (1999)
  • Unmixing Hyperspectral Data (1999)
  • v-Arc: Ensemble Learning in the Presence of Outliers (1999)
  • Kernel PCA and De-Noising in Feature Spaces (1998)
  • Analysis of Drifting Dynamics with Neural Network Hidden Markov Models (1997)
  • Adaptive On-line Learning in Changing Environments (1996)
  • Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective? (1995)
  • 1 Book

  • Advances in Neural Information Processing Systems 12 (1999)