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stochastic gradient descent

1088 papers

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Also known as

SGD ASGD SAGA SGM SGDA PSGD SKGD

Co-occurring keywords

non-convex optimization (546) distributed learning (563) neural network optimization (1293) convergence rate (606) convex optimization (1320) neural network (6616) variance reduction (520) stochastic optimization (1060) convergence analysis (394) differential privacy (1010)

Papers

Data Banzhaf: A Robust Data Valuation Framework for Machine Learning AISTATS 2023
ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks AISTATS 2023
Robust Collaborative Learning with Linear Gradient Overhead ICML 2023
Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning COLT 2023
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks COLT 2023
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics COLT 2023
Learning-Rate-Free Learning by D-Adaptation ICML 2023
Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization ICML 2023
Beyond Uniform Lipschitz Condition in Differentially Private Optimization ICML 2023
Efficient Privacy-Preserving Stochastic Nonconvex Optimization UAI 2023
Alternating Projected SGD for Equality-constrained Bilevel Optimization AISTATS 2023
Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds JMLR 2023
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees JMLR 2023
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data ICML 2023
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance ICML 2023
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods AISTATS 2023
Learning Trajectories are Generalization Indicators NIPS 2023
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods NIPS 2023
Efficient preconditioned stochastic gradient descent for estimation in latent variable models ICML 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces AISTATS 2023
On Learning Rates and Schrödinger Operators JMLR 2023
Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network NIPS 2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference ICML 2023
A Theoretical Analysis of the Learning Dynamics under Class Imbalance ICML 2023
User-level Private Stochastic Convex Optimization with Optimal Rates ICML 2023