NIPS Proceedingsβ

Francis R. Bach

27 Papers

  • A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets (2012)
  • Multiple Operator-valued Kernel Learning (2012)
  • Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization (2011)
  • Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning (2011)
  • Shaping Level Sets with Submodular Functions (2011)
  • Trace Lasso: a trace norm regularization for correlated designs (2011)
  • Efficient Optimization for Discriminative Latent Class Models (2010)
  • Network Flow Algorithms for Structured Sparsity (2010)
  • Online Learning for Latent Dirichlet Allocation (2010)
  • Structured sparsity-inducing norms through submodular functions (2010)
  • Asymptotically Optimal Regularization in Smooth Parametric Models (2009)
  • Data-driven calibration of linear estimators with minimal penalties (2009)
  • Clustered Multi-Task Learning: A Convex Formulation (2008)
  • Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning (2008)
  • Kernel Change-point Analysis (2008)
  • Sparse probabilistic projections (2008)
  • Supervised Dictionary Learning (2008)
  • DIFFRAC: a discriminative and flexible framework for clustering (2007)
  • Testing for Homogeneity with Kernel Fisher Discriminant Analysis (2007)
  • Active learning for misspecified generalized linear models (2006)
  • Statistical Convergence of Kernel CCA (2005)
  • Blind One-microphone Speech Separation: A Spectral Learning Approach (2004)
  • Computing regularization paths for learning multiple kernels (2004)
  • Kernel Dimensionality Reduction for Supervised Learning (2003)
  • Learning Spectral Clustering (2003)
  • Learning Graphical Models with Mercer Kernels (2002)
  • Thin Junction Trees (2001)