NIPS Proceedingsβ

Tong Zhang

35 Papers

  • Deep Subspace Clustering Network (2017)
  • Diffusion Approximations for Online Principal Component Estimation and Global Convergence (2017)
  • Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding (2017)
  • On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning (2017)
  • Exact Recovery of Hard Thresholding Pursuit (2016)
  • Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation (2016)
  • Local Smoothness in Variance Reduced Optimization (2015)
  • Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling (2015)
  • Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding (2015)
  • Accelerated Mini-Batch Stochastic Dual Coordinate Ascent (2013)
  • Accelerating Stochastic Gradient Descent using Predictive Variance Reduction (2013)
  • Selective Labeling via Error Bound Minimization (2012)
  • Greedy Model Averaging (2011)
  • Learning to Search Efficiently in High Dimensions (2011)
  • Spectral Methods for Learning Multivariate Latent Tree Structure (2011)
  • Agnostic Active Learning Without Constraints (2010)
  • Deep Coding Network (2010)
  • Multi-Label Prediction via Compressed Sensing (2009)
  • Nonlinear Learning using Local Coordinate Coding (2009)
  • Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models (2008)
  • Multi-stage Convex Relaxation for Learning with Sparse Regularization (2008)
  • Sparse Online Learning via Truncated Gradient (2008)
  • A General Boosting Method and its Application to Learning Ranking Functions for Web Search (2007)
  • The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information (2007)
  • Learning on Graph with Laplacian Regularization (2006)
  • Analysis of Spectral Kernel Design based Semi-supervised Learning (2005)
  • Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification (2004)
  • Support Vector Classification with Input Data Uncertainty (2004)
  • An Infinity-sample Theory for Multi-category Large Margin Classification (2003)
  • Learning Bounds for a Generalized Family of Bayesian Posterior Distributions (2003)
  • Data-Dependent Bounds for Bayesian Mixture Methods (2002)
  • Effective Dimension and Generalization of Kernel Learning (2002)
  • Convergence of Large Margin Separable Linear Classification (2000)
  • Regularized Winnow Methods (2000)
  • Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions (1999)