NIPS Proceedingsβ

Mikhail Belkin

16 Papers

  • Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate (2018)
  • Diving into the shallows: a computational perspective on large-scale shallow learning (2017)
  • Clustering with Bregman Divergences: an Asymptotic Analysis (2016)
  • Graphons, mergeons, and so on! (2016)
  • A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA (2015)
  • Learning with Fredholm Kernels (2014)
  • Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis (2013)
  • Inverse Density as an Inverse Problem: the Fredholm Equation Approach (2013)
  • Data Skeletonization via Reeb Graphs (2011)
  • Semi-supervised Learning using Sparse Eigenfunction Bases (2009)
  • The Value of Labeled and Unlabeled Examples when the Model is Imperfect (2007)
  • Convergence of Laplacian Eigenmaps (2006)
  • On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts (2006)
  • Limits of Spectral Clustering (2004)
  • Using Manifold Stucture for Partially Labeled Classification (2002)
  • Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering (2001)