convex optimization

Marginal Regression For Multitask Learning AISTATS 2012 Mirror Descent Meets Fixed Share (and feels no regret) NeurIPS 2012 Multi Kernel Learning with Online-Batch Optimization JMLR 2012 Multi-Task Averaging NeurIPS 2012 Newton-Like Methods for Sparse Inverse Covariance Estimation NeurIPS 2012 No-Regret Algorithms for Unconstrained Online Convex Optimization NeurIPS 2012 On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking NeurIPS 2012 Optimal Regularized Dual Averaging Methods for Stochastic Optimization NeurIPS 2012 Perturbation based Large Margin Approach for Ranking AISTATS 2012 Private Convex Empirical Risk Minimization and High-dimensional Regression COLT 2012 Proximal Newton-type methods for convex optimization NeurIPS 2012 Query Strategies for Evading Convex-Inducing Classifiers JMLR 2012 Regularization Paths with Guarantees for Convex Semidefinite Optimization AISTATS 2012 Regularized Off-Policy TD-Learning NeurIPS 2012 Sparse Prediction with the k-Support Norm NeurIPS 2012 Stochastic Gradient Descent with Only One Projection NeurIPS 2012 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NeurIPS 2012 Structured Learning of Gaussian Graphical Models NeurIPS 2012 Structured Sparsity via Alternating Direction Methods JMLR 2012 There’s a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems AISTATS 2012 Variable Selection for Gaussian Graphical Models AISTATS 2012 Volume Regularization for Binary Classification NeurIPS 2012 A Convergence Analysis of Log-Linear Training NeurIPS 2011 Algorithms and hardness results for parallel large margin learning NeurIPS 2011 Approximating Semidefinite Programs in Sublinear Time NeurIPS 2011