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stochastic gradient descent

1088 papers

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Also known as

SGD

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

Toward Better Generalization Bounds with Locally Elastic Stability ICML 2021
Convergence rates and approximation results for SGD and its continuous-time counterpart COLT 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms NIPS 2021
Regularizing Neural Networks via Adversarial Model Perturbation CVPR 2021
Communication Efficient SGD via Gradient Sampling With Bayes Prior CVPR 2021
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training NIPS 2021
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems ICML 2021
Simple induction of (deterministic) probabilistic finite-state automata for phonotactics by stochastic gradient descent ACL 2021
Stability and Generalization of Decentralized Stochastic Gradient Descent AAAI 2021
Stochastic Sign Descent Methods: New Algorithms and Better Theory ICML 2021
Asynchronous Stochastic Gradient Descent for Extreme-Scale Recommender Systems AAAI 2021
Selfish Sparse RNN Training ICML 2021
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates AISTATS 2021
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization ICML 2021
Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster AAAI 2021
On ADMM in Deep Learning: Convergence and Saturation-Avoidance JMLR 2021
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization AAAI 2021
Local SGD: Unified Theory and New Efficient Methods AISTATS 2021
Streaming Linear System Identification with Reverse Experience Replay NIPS 2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization ICML 2021
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems NIPS 2021
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models NIPS 2021
Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss AISTATS 2021
Robust Learning for Data Poisoning Attacks ICML 2021
On Monotonic Linear Interpolation of Neural Network Parameters ICML 2021