NIPS Proceedingsβ

Arthur Gretton

21 Papers

  • Interpretable Distribution Features with Maximum Testing Power (2016)
  • Fast Two-Sample Testing with Analytic Representations of Probability Measures (2015)
  • Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families (2015)
  • A Wild Bootstrap for Degenerate Kernel Tests (2014)
  • A Kernel Test for Three-Variable Interactions (2013)
  • B-test: A Non-parametric, Low Variance Kernel Two-sample Test (2013)
  • Optimal kernel choice for large-scale two-sample tests (2012)
  • Kernel Bayes' Rule (2011)
  • A Fast, Consistent Kernel Two-Sample Test (2009)
  • Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions (2009)
  • Nonlinear directed acyclic structure learning with weakly additive noise models (2009)
  • Characteristic Kernels on Groups and Semigroups (2008)
  • Kernel Measures of Independence for non-iid Data (2008)
  • Learning Taxonomies by Dependence Maximization (2008)
  • A Kernel Statistical Test of Independence (2007)
  • Colored Maximum Variance Unfolding (2007)
  • Kernel Measures of Conditional Dependence (2007)
  • A Kernel Method for the Two-Sample-Problem (2006)
  • Correcting Sample Selection Bias by Unlabeled Data (2006)
  • Statistical Convergence of Kernel CCA (2005)
  • Ranking on Data Manifolds (2003)