NIPS Proceedingsβ

Alex J. Smola

40 Papers

  • FastEx: Hash Clustering with Exponential Families (2012)
  • Learning Networks of Heterogeneous Influence (2012)
  • Multitask Learning without Label Correspondences (2010)
  • Optimal Web-Scale Tiering as a Flow Problem (2010)
  • Parallelized Stochastic Gradient Descent (2010)
  • Word Features for Latent Dirichlet Allocation (2010)
  • Distribution Matching for Transduction (2009)
  • Slow Learners are Fast (2009)
  • Kernelized Sorting (2008)
  • Kernel Measures of Independence for non-iid Data (2008)
  • Robust Near-Isometric Matching via Structured Learning of Graphical Models (2008)
  • Tighter Bounds for Structured Estimation (2008)
  • A Kernel Statistical Test of Independence (2007)
  • Bundle Methods for Machine Learning (2007)
  • COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking (2007)
  • Colored Maximum Variance Unfolding (2007)
  • Convex Learning with Invariances (2007)
  • A Kernel Method for the Two-Sample-Problem (2006)
  • Correcting Sample Selection Bias by Unlabeled Data (2006)
  • A Second Order Cone programming Formulation for Classifying Missing Data (2004)
  • Binet-Cauchy Kernels (2004)
  • Laplace Propagation (2003)
  • Adapting Codes and Embeddings for Polychotomies (2002)
  • Fast Kernels for String and Tree Matching (2002)
  • Hyperkernels (2002)
  • Kernel Machines and Boolean Functions (2001)
  • Online Learning with Kernels (2001)
  • Regularization with Dot-Product Kernels (2000)
  • Sparse Greedy Gaussian Process Regression (2000)
  • Invariant Feature Extraction and Classification in Kernel Spaces (1999)
  • Support Vector Method for Novelty Detection (1999)
  • The Entropy Regularization Information Criterion (1999)
  • v-Arc: Ensemble Learning in the Presence of Outliers (1999)
  • Kernel PCA and De-Noising in Feature Spaces (1998)
  • Semiparametric Support Vector and Linear Programming Machines (1998)
  • Shrinking the Tube: A New Support Vector Regression Algorithm (1998)
  • From Regularization Operators to Support Vector Kernels (1997)
  • Prior Knowledge in Support Vector Kernels (1997)
  • Support Vector Method for Function Approximation, Regression Estimation and Signal Processing (1996)
  • Support Vector Regression Machines (1996)