Skip to content
conftrace_

non-convex optimization

949 papers

Papers

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow ICML 2016 Matrix Completion has No Spurious Local Minimum NeurIPS 2016 Mixed Linear Regression with Multiple Components NeurIPS 2016 MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions JMLR 2016 NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization NeurIPS 2016 On Graduated Optimization for Stochastic Non-Convex Problems ICML 2016 Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes NeurIPS 2016 On the Quality of the Initial Basin in Overspecified Neural Networks ICML 2016 Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization AISTATS 2016 Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent NeurIPS 2016 Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow ICML 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NeurIPS 2016 Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds NeurIPS 2016 SDCA without Duality, Regularization, and Individual Convexity ICML 2016 Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow NeurIPS 2016 Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity ICML 2016 Stochastic Structured Prediction under Bandit Feedback NeurIPS 2016 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning ICML 2016 Stochastic Variance Reduction for Nonconvex Optimization ICML 2016 The non-convex Burer-Monteiro approach works on smooth semidefinite programs NeurIPS 2016 The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM NeurIPS 2016 The Sum-Product Theorem: A Foundation for Learning Tractable Models ICML 2016 Train faster, generalize better: Stability of stochastic gradient descent ICML 2016 Training Neural Networks Without Gradients: A Scalable ADMM Approach ICML 2016 Variance Reduction for Faster Non-Convex Optimization ICML 2016