NIPS Proceedingsβ

Martin J. Wainwright

28 Papers

  • Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences (2016)
  • Information-theoretic lower bounds for distributed statistical estimation with communication constraints (2013)
  • Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation (2013)
  • Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima (2013)
  • Communication-Efficient Algorithms for Statistical Optimization (2012)
  • Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods (2012)
  • Privacy Aware Learning (2012)
  • Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions (2012)
  • Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses (2012)
  • A More Powerful Two-Sample Test in High Dimensions using Random Projection (2011)
  • High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity (2011)
  • Distributed Dual Averaging In Networks (2010)
  • Fast global convergence rates of gradient methods for high-dimensional statistical recovery (2010)
  • A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers (2009)
  • Information-theoretic lower bounds on the oracle complexity of convex optimization (2009)
  • Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness (2009)
  • High-dimensional support union recovery in multivariate regression (2008)
  • Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE (2008)
  • Phase transitions for high-dimensional joint support recovery (2008)
  • Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization (2007)
  • Loop Series and Bethe Variational Bounds in Attractive Graphical Models (2007)
  • High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression (2006)
  • Estimating the wrong Markov random field: Benefits in the computation-limited setting (2005)
  • Semidefinite Relaxations for Approximate Inference on Graphs with Cycles (2003)
  • Exact MAP Estimates by (Hyper)tree Agreement (2002)
  • Tree-based reparameterization for approximate inference on loopy graphs (2001)
  • Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles (2000)
  • Scale Mixtures of Gaussians and the Statistics of Natural Images (1999)