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stochastic gradient

296 papers

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Also known as

SGVB SVRG SGHMC SGLD SG SGMCMC SGD

Co-occurring keywords

variance reduction (520) markov chain monte carlo (724) stochastic optimization (1060) bayesian inference (1904) stochastic gradient descent (1088) convex optimization (1320) nonconvex optimization (316) convergence rate (606) langevin dynamics (128) variational inference (2069)

Papers

Active Labeling: Streaming Stochastic Gradients NIPS 2022
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization NIPS 2022
On Sample Optimality in Personalized Collaborative and Federated Learning NIPS 2022
Efficient Sampling on Riemannian Manifolds via Langevin MCMC NIPS 2022
Reproducibility in Optimization: Theoretical Framework and Limits NIPS 2022
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization NIPS 2022
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization AISTATS 2022
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond NIPS 2022
Adam Can Converge Without Any Modification On Update Rules NIPS 2022
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold JMLR 2022
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning AISTATS 2022
Anytime Guarantees under Heavy-Tailed Data AAAI 2022
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective ICML 2022
Gradient-Free Method for Heavily Constrained Nonconvex Optimization ICML 2022
On Biased Stochastic Gradient Estimation JMLR 2022
FedNest: Federated Bilevel, Minimax, and Compositional Optimization ICML 2022
Amortized Proximal Optimization NIPS 2022
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization AISTATS 2022
Noisy Riemannian Gradient Descent for Eigenvalue Computation with Application to Inexact Stochastic Recursive Gradient Algorithm ACML 2022
Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization JMLR 2022
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments AAAI 2022
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients ICML 2021
SHPOS: A Theoretical Guaranteed Accelerated Particle Optimization Sampling Method IJCAI 2021
Quasi-Monte Carlo Quasi-Newton in Variational Bayes JMLR 2021
Learning with risk-averse feedback under potentially heavy tails AISTATS 2021