Co-occurring keywords · papers mentioning both
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