Image de couverture de livre

Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Crawshaw, Michael, Liu, Mingrui
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
par: Crawshaw, Michael, et autres
Publié: (2025)

Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
par: Wu, Jingfeng, et autres
Publié: (2025)

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression
par: Crawshaw, Michael, et autres
Publié: (2025)

Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness
par: Crawshaw, Michael, et autres
Publié: (2025)

Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes
par: Zhang, Ruiqi, et autres
Publié: (2025)

An Exploration of Non-Euclidean Gradient Descent: Muon and its Many Variants
par: Crawshaw, Michael, et autres
Publié: (2025)

Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
par: Wu, Jingfeng, et autres
Publié: (2024)

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
par: Crawshaw, Michael, et autres
Publié: (2024)

Non-Euclidean Gradient Descent Operates at the Edge of Stability
par: Islamov, Rustem, et autres
Publié: (2026)

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
par: Wu, Jingfeng, et autres
Publié: (2025)

From Logistic Regression to the Perceptron Algorithm: Exploring Gradient Descent with Large Step Sizes
par: Tyurin, Alexander
Publié: (2024)

Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
par: Meng, Si Yi, et autres
Publié: (2024)

Exponential Convergence of (Stochastic) Gradient Descent for Separable Logistic Regression
par: Kale, Sacchit, et autres
Publié: (2026)

Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere?
par: Meng, Si Yi, et autres
Publié: (2025)

Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent
par: Zhang, Chenyang, et autres
Publié: (2026)

Negative Stepsizes Make Gradient-Descent-Ascent Converge
par: Shugart, Henry, et autres
Publié: (2025)

Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
par: Cai, Yuhang, et autres
Publié: (2024)

Coupling-based Convergence Diagnostic and Stepsize Scheme for Stochastic Gradient Descent
par: Li, Xiang, et autres
Publié: (2024)

Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
par: Xiong, Wenjun, et autres
Publié: (2025)

Learning Curves of Stochastic Gradient Descent in Kernel Regression
par: Zhang, Haihan, et autres
Publié: (2025)

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
par: Brown, Gavin, et autres
Publié: (2024)

Stochastic Gradient Descent for Nonparametric Additive Regression
par: Chen, Xin, et autres
Publié: (2024)

Posterior Approximation using Stochastic Gradient Ascent with Adaptive Stepsize
par: Lim, Kart-Leong, et autres
Publié: (2024)

Tight Regret Bounds for Bayesian Optimization in One Dimension
par: Scarlett, Jonathan
Publié: (2018)

Riemannian Gradient Descent for Low-Rank Architectures
par: Knight, Nicholas
Publié: (2026)

Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
par: Ma, Wenquan, et autres
Publié: (2026)

Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression
par: Jiang, Jiarui, et autres
Publié: (2025)

Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression
par: Ding, Shihong, et autres
Publié: (2025)

Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
par: Rajpal, Rajit, et autres
Publié: (2025)

Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
par: Chiang, John
Publié: (2022)

Complex Equation Learner: Rational Symbolic Regression with Gradient Descent in Complex Domain
par: Garmaev, Sergei, et autres
Publié: (2026)

Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
par: Zhang, Chenyang, et autres
Publié: (2025)

Tight Generalization Bounds for Large-Margin Halfspaces
par: Larsen, Kasper Green, et autres
Publié: (2025)

Information-Theoretic Generalization Bounds for Stochastic Gradient Descent with Predictable Virtual Noise
par: Partohaghighi, Mohammad
Publié: (2026)

Which Algorithms Have Tight Generalization Bounds?
par: Gastpar, Michael, et autres
Publié: (2024)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
par: Orvieto, Antonio, et autres
Publié: (2024)

Attacking Large Language Models with Projected Gradient Descent
par: Geisler, Simon, et autres
Publié: (2024)

$γ$-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning
par: Lu, Rongwei, et autres
Publié: (2025)

Efficient Logistic Regression with Mixture of Sigmoids
par: Di Gennaro, Federico, et autres
Publié: (2026)

Compact Memory for Continual Logistic Regression
par: Jung, Yohan, et autres
Publié: (2025)