NIPS Proceedingsβ

Le Song

22 Papers

  • COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution (2015)
  • Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression (2015)
  • M-Statistic for Kernel Change-Point Detection (2015)
  • Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients (2015)
  • Time-Sensitive Recommendation From Recurrent User Activities (2015)
  • Active Learning and Best-Response Dynamics (2014)
  • Learning Time-Varying Coverage Functions (2014)
  • Scalable Kernel Methods via Doubly Stochastic Gradients (2014)
  • Shaping Social Activity by Incentivizing Users (2014)
  • Robust Low Rank Kernel Embeddings of Multivariate Distributions (2013)
  • Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013)
  • Learning Networks of Heterogeneous Influence (2012)
  • Kernel Bayes' Rule (2011)
  • Kernel Embeddings of Latent Tree Graphical Models (2011)
  • Spectral Methods for Learning Multivariate Latent Tree Structure (2011)
  • Sparsistent Learning of Varying-coefficient Models with Structural Changes (2009)
  • Time-Varying Dynamic Bayesian Networks (2009)
  • Kernelized Sorting (2008)
  • Kernel Measures of Independence for non-iid Data (2008)
  • A Kernel Statistical Test of Independence (2007)
  • Colored Maximum Variance Unfolding (2007)
  • Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface (2005)