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convergence analysis

394 papers

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stochastic gradient descent (1088) convergence rate (606) stochastic optimization (1060) non-convex optimization (546) gradient descent (1143) federated learning (1320) neural network optimization (1293) reinforcement learning (4122) distributed learning (563) neural network (6616)

Papers

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks NIPS 2019
Understanding the Role of Momentum in Stochastic Gradient Methods NIPS 2019
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples NIPS 2019
Stagewise Training Accelerates Convergence of Testing Error Over SGD NIPS 2019
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient UAI 2019
Convergence Analysis of Gradient-Based Learning in Continuous Games UAI 2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions COLT 2019
Communication trade-offs for Local-SGD with large step size NIPS 2019
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes AISTATS 2019
Estimating Convergence of Markov chains with L-Lag Couplings NIPS 2019
A Sufficient Condition for Convergences of Adam and RMSProp CVPR 2019
New Convergence Aspects of Stochastic Gradient Algorithms JMLR 2019
Global Convergence of Block Coordinate Descent in Deep Learning ICML 2019
Convergence of Value Aggregation for Imitation Learning AISTATS 2018
A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization IJCAI 2018
Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation JMLR 2018
LP Heuristics over Conjunctions: Compilation, Convergence, Nogood Learning IJCAI 2018
Adaptive Methods for Nonconvex Optimization NIPS 2018
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity NIPS 2018
Boosting Variational Inference: an Optimization Perspective AISTATS 2018
On Q-learning Convergence for Non-Markov Decision Processes IJCAI 2018
A Bridging Framework for Model Optimization and Deep Propagation NIPS 2018
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption CVPR 2018
On the Convergence of PatchMatch and Its Variants CVPR 2018
On Truly Block Eigensolvers via Riemannian Optimization AISTATS 2018