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

Continual learning with the neural tangent ensemble NIPS 2024

Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training AISTATS 2024

Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD NIPS 2024

Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning JMLR 2024

Banded Square Root Matrix Factorization for Differentially Private Model Training NIPS 2024

A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent JMLR 2024

The Implicit Bias of Adam on Separable Data NIPS 2024

AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms AISTATS 2024

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters AISTATS 2024

Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing AISTATS 2024

Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression AISTATS 2024

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks AISTATS 2024

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit NIPS 2024

CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference NIPS 2024

Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes AISTATS 2024

On the Role of Server Momentum in Federated Learning AAAI 2024

Sparse Variational Student-t Processes AAAI 2024

A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise NIPS 2024

Performative Control for Linear Dynamical Systems NIPS 2024

Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model JMLR 2024

The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing NIPS 2024

Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case JMLR 2024

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024

Symmetries in Overparametrized Neural Networks: A Mean Field View NIPS 2024

Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning Interference With Gradient Projection WACV 2024