Optimization

Krylov Subspace Descent for Deep Learning AISTATS 2012 Large-scale Linear Support Vector Regression JMLR 2012 Learning as MAP Inference in Discrete Graphical Models NeurIPS 2012 Lifted coordinate descent for learning with trace-norm regularization AISTATS 2012 Lifted Linear Programming AISTATS 2012 Lifted Variable Elimination with Arbitrary Constraints AISTATS 2012 Local Supervised Learning through Space Partitioning NeurIPS 2012 Majorization for CRFs and Latent Likelihoods NeurIPS 2012 MAP Inference in Chains using Column Generation NeurIPS 2012 Matrix reconstruction with the local max norm NeurIPS 2012 Max-Margin Ratio Machine ACML 2012 Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL NeurIPS 2012 Minimization of Continuous Bethe Approximations: A Positive Variation NeurIPS 2012 Minimizing Sparse High-Order Energies by Submodular Vertex-Cover NeurIPS 2012 Minimizing Uncertainty in Pipelines NeurIPS 2012 Mirror Descent Meets Fixed Share (and feels no regret) NeurIPS 2012 Multi Kernel Learning with Online-Batch Optimization JMLR 2012 Multiple Operator-valued Kernel Learning NeurIPS 2012 Multiresolution analysis on the symmetric group NeurIPS 2012 Multi-Stage Multi-Task Feature Learning NeurIPS 2012 Near-Optimal Algorithms for Online Matrix Prediction COLT 2012 Near-optimal Differentially Private Principal Components NeurIPS 2012 Near-Optimal MAP Inference for Determinantal Point Processes NeurIPS 2012 Newton-Like Methods for Sparse Inverse Covariance Estimation NeurIPS 2012 Nonconvex Penalization Using Laplace Exponents and Concave Conjugates NeurIPS 2012