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

Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance AAAI 2026

Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training CVPR 2025

Finite-Time Analysis of Heterogeneous Federated Temporal Difference Learning IJCAI 2025

Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms JMLR 2025

EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity AAAI 2025

Langevin Monte Carlo Beyond Lipschitz Gradient Continuity AAAI 2025

Decentralized Projected Riemannian Stochastic Recursive Momentum Method for Nonconvex Optimization AAAI 2025

ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-Order Optimization AAAI 2024

PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds IJCAI 2024

Towards Sharper Risk Bounds for Minimax Problems IJCAI 2024

On Unbiased Estimation for Partially Observed Diffusions JMLR 2024

Robust Approximate Sampling via Stochastic Gradient Barker Dynamics AISTATS 2024

Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity NIPS 2024

Fast two-time-scale stochastic gradient method with applications in reinforcement learning COLT 2024

Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets JMLR 2024

autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm AISTATS 2024

Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling JMLR 2024

Discerning Temporal Difference Learning AAAI 2024

Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling NIPS 2024

PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates JMLR 2024

Accelerating Relative Entropy Coding with Space Partitioning NIPS 2024

Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity NIPS 2024

Independent Learning in Constrained Markov Potential Games AISTATS 2024

Compressed and distributed least-squares regression: convergence rates with applications to federated learning JMLR 2024

Nesterov acceleration despite very noisy gradients NIPS 2024