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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

Iterative Calculus of Voting under Plurality AAAI 2022
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows AAAI 2022
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings AAAI 2022
Differentially Private Federated Learning With Local Regularization and Sparsification CVPR 2022
Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning NIPS 2022
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution NIPS 2022
Nonlinear MCMC for Bayesian Machine Learning NIPS 2022
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent NIPS 2022
A Unified Convergence Theorem for Stochastic Optimization Methods NIPS 2022
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis NIPS 2022
Revisit last-iterate convergence of mSGD under milder requirement on step size NIPS 2022
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions NIPS 2022
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning CVPR 2022
Iterated Vector Fields and Conservatism, with Applications to Federated Learning ALT 2022
The Dynamics of Riemannian Robbins-Monro Algorithms COLT 2022
On the Convergence of Decentralized Adaptive Gradient Methods ACML 2022
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum L4DC 2022
A Unified Convergence Analysis for Shuffling-Type Gradient Methods JMLR 2021
An Inertial Newton Algorithm for Deep Learning JMLR 2021
A Comprehensively Tight Analysis of Gradient Descent for PCA NIPS 2021
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights NIPS 2021
On the Linear Convergence of Policy Gradient Methods for Finite MDPs AISTATS 2021
Multi-Scale Games: Representing and Solving Games on Networks with Group Structure AAAI 2021
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization NIPS 2021
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization AAAI 2021