conftrace_

convergence rate

607 papers

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Co-occurring keywords

stochastic gradient descent (1091) convex optimization (1321) stochastic optimization (1060) gradient descent (1144) convergence analysis (395) non-convex optimization (547) nonconvex optimization (316) variance reduction (523) stochastic gradient (296) learning theory (519)

Papers

Nonlinear Acceleration of Stochastic Algorithms NIPS 2017
Convergence of Gradient EM on Multi-component Mixture of Gaussians NIPS 2017
AccGenSVM: Selectively Transferring from Previous Hypotheses IJCAI 2017
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence ICML 2017
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates ICML 2017
Follow the Moving Leader in Deep Learning ICML 2017
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems IJCAI 2017
Convergence Rate of Stochastic k-means AISTATS 2017
Dissipativity Theory for Nesterov’s Accelerated Method ICML 2017
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization NIPS 2017
Gradient Descent Can Take Exponential Time to Escape Saddle Points NIPS 2017
Active Heteroscedastic Regression ICML 2017
Kernel Partial Least Squares for Stationary Data JMLR 2017
Second-Order Stochastic Optimization for Machine Learning in Linear Time JMLR 2017
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression JMLR 2017
Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017
Group Sparse Optimization via lp,q Regularization JMLR 2017
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization ICML 2017
Improving Gibbs Sampler Scan Quality with DoGS ICML 2017
Deformable Rapidly-Exploring Random Trees RSS 2017
Stochastic Variance Reduction for Nonconvex Optimization ICML 2016
Fast Rate Analysis of Some Stochastic Optimization Algorithms ICML 2016
Blind Attacks on Machine Learners NIPS 2016
Primal-Dual Rates and Certificates ICML 2016
A General Framework for Consistency of Principal Component Analysis JMLR 2016