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nonconvex optimization

316 papers

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Co-occurring keywords

stochastic gradient descent (1088) stochastic optimization (1060) variance reduction (520) gradient descent (1143) convergence rate (606) non-convex optimization (546) stochastic gradient (296) nonsmooth optimization (49) minimax optimization (186) convergence analysis (394)

Papers

Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization AISTATS 2021
Unique sparse decomposition of low rank matrices NIPS 2021
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization NIPS 2021
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond NIPS 2021
Oracle Complexity in Nonsmooth Nonconvex Optimization NIPS 2021
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods CVPR 2021
L-SVRG and L-Katyusha with Arbitrary Sampling JMLR 2021
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality ICML 2020
D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems AAAI 2020
Towards Better Generalization of Adaptive Gradient Methods NIPS 2020
Convergence Rates of a Momentum Algorithm with Bounded Adaptive Step Size for Nonconvex Optimization ACML 2020
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions ICML 2020
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization ICML 2020
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion JMLR 2020
Continuous-time Lower Bounds for Gradient-based Algorithms ICML 2020
An Effective Hard Thresholding Method Based on Stochastic Variance Reduction for Nonconvex Sparse Learning AAAI 2020
Approximation Schemes for ReLU Regression COLT 2020
On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach JMLR 2020
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems NIPS 2020
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms ICML 2020
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes JMLR 2020
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization JMLR 2020
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function NIPS 2020
Stochastic Recursive Variance-Reduced Cubic Regularization Methods AISTATS 2020
How many samples is a good initial point worth in Low-rank Matrix Recovery? NIPS 2020