2025 "stochastic gradient descent" Papers
19 papers found
Approximating Metric Magnitude of Point Sets
Rayna Andreeva, James Ward, Primoz Skraba et al.
Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
Sebastian Kassing, Simon Weissmann, Leif Döring
Convergence of Clipped SGD on Convex $(L_0,L_1)$-Smooth Functions
Ofir Gaash, Kfir Y. Levy, Yair Carmon
Descent with Misaligned Gradients and Applications to Hidden Convexity
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar et al.
Emergence and scaling laws in SGD learning of shallow neural networks
Yunwei Ren, Eshaan Nichani, Denny Wu et al.
Gaussian Approximation and Concentration of Constant Learning-Rate Stochastic Gradient Descent
Ziyang Wei, Jiaqi Li, Zhipeng Lou et al.
Gradient correlation is a key ingredient to accelerate SGD with momentum
Julien Hermant, Marien Renaud, Jean-François Aujol et al.
Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD
Ze Peng, Jian Zhang, Yisen Wang et al.
Neural Thermodynamics: Entropic Forces in Deep and Universal Representation Learning
Liu Ziyin, Yizhou Xu, Isaac Chuang
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation
Marina Sheshukova, Denis Belomestny, Alain Oliviero Durmus et al.
Online robust locally differentially private learning for nonparametric regression
Chenfei Gu, Qiangqiang Zhang, Ting Li et al.
Online Statistical Inference in Decision Making with Matrix Context
Qiyu Han, Will Wei Sun, Yichen Zhang
On the Convergence of Stochastic Smoothed Multi-Level Compositional Gradient Descent Ascent
Xinwen Zhang, Hongchang Gao
Optimal Rates in Continual Linear Regression via Increasing Regularization
Ran Levinstein, Amit Attia, Matan Schliserman et al.
Revisiting Large-Scale Non-convex Distributionally Robust Optimization
Qi Zhang, Yi Zhou, Simon Khan et al.
Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations
Shaocong Ma, Heng Huang
Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation
Chenyu Zhang, Xu Chen, Xuan Di
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang
Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks
Xuan Tang, Han Zhang, Yuan Cao et al.