On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

19 June 2020

Papers citing "On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems"

50 / 60 papers shown

Title
Spike-timing-dependent Hebbian learning as noisy gradient descent Niklas Dexheimer Sascha Gaudlitz Johannes Schmidt-Hieber 23 0 0 15 May 2025
Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Ruinan Jin Difei Cheng Hong Qiao Xin Shi Shaodong Liu Bo Zhang 21 0 0 17 Apr 2025
A Near Complete Nonasymptotic Generalization Theory For Multilayer Neural Networks: Beyond the Bias-Variance Tradeoff Hao Yu Xiangyang Ji AI4CE 58 0 0 03 Mar 2025
Nesterov acceleration in benignly non-convex landscapes Kanan Gupta Stephan Wojtowytsch 36 2 0 10 Oct 2024
Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm Jinwei Zhao Marco Gori Alessandro Betti S. Melacci Hongtao Zhang Jiedong Liu Xinhong Hei 28 0 0 10 Sep 2024
Lyapunov weights to convey the meaning of time in physics-informed neural networks Gabriel Turinici 21 0 0 31 Jul 2024
Almost sure convergence rates of stochastic gradient methods under gradient domination Simon Weissmann Sara Klein Waïss Azizian Leif Döring 34 3 0 22 May 2024
Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent Holger Dette Carina Graw 18 0 0 21 May 2024
Optimal time sampling in physics-informed neural networks Gabriel Turinici PINN 11 1 0 29 Apr 2024
Federated reinforcement learning for robot motion planning with zero-shot generalization Zhenyuan Yuan Siyuan Xu Minghui Zhu FedML 21 1 0 20 Mar 2024
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity Yiyue Chen H. Vikalo C. Wang FedML 39 5 0 20 Dec 2023
Learning Unorthogonalized Matrices for Rotation Estimation Kerui Gu Zhihao Li Shiyong Liu Jianzhuang Liu Songcen Xu Youliang Yan Michael Bi Mi Kenji Kawaguchi Angela Yao 30 1 0 01 Dec 2023
Adam-like Algorithm with Smooth Clipping Attains Global Minima: Analysis Based on Ergodicity of Functional SDEs Keisuke Suzuki 13 0 0 29 Nov 2023
Riemannian stochastic optimization methods avoid strict saddle points Ya-Ping Hsieh Mohammad Reza Karimi Andreas Krause P. Mertikopoulos 28 5 0 04 Nov 2023
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks Zheyuan Hu K. Shukla George Karniadakis Kenji Kawaguchi PINN AI4CE 63 85 0 23 Jul 2023
Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks Jing An Jianfeng Lu 16 4 0 18 Apr 2023
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance Krishnakumar Balasubramanian Promit Ghosal Ye He 28 5 0 03 Apr 2023
Type-II Saddles and Probabilistic Stability of Stochastic Gradient Descent Liu Ziyin Botao Li Tomer Galanti Masakuni Ueda 37 7 0 23 Mar 2023
On the existence of optimal shallow feedforward networks with ReLU activation Steffen Dereich Sebastian Kassing 19 4 0 06 Mar 2023
On the existence of minimizers in shallow residual ReLU neural network optimization landscapes Steffen Dereich Arnulf Jentzen Sebastian Kassing 21 6 0 28 Feb 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression Bhavya Agrawalla Krishnakumar Balasubramanian Promit Ghosal 23 2 0 20 Feb 2023
Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption Jun Liu Ye Yuan ODL 9 1 0 15 Feb 2023
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering Yongxin Guo Xiaoying Tang Tao R. Lin OOD FedML 30 8 0 29 Jan 2023
Variance Reduction for Score Functions Using Optimal Baselines Ronan L. Keane H. Gao 11 0 0 27 Dec 2022
Efficiency Ordering of Stochastic Gradient Descent Jie Hu Vishwaraj Doshi Do Young Eun 28 6 0 15 Sep 2022
Convergence of Batch Updating Methods with Approximate Gradients and/or Noisy Measurements: Theory and Computational Results Tadipatri Uday M. Vidyasagar 6 0 0 12 Sep 2022
Neural Tangent Kernel: A Survey Eugene Golikov Eduard Pokonechnyy Vladimir Korviakov 13 12 0 29 Aug 2022
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation Jeffrey Willette Seanie Lee Bruno Andreis Kenji Kawaguchi Juho Lee S. Hwang 13 3 0 26 Aug 2022
A unified stochastic approximation framework for learning in games P. Mertikopoulos Ya-Ping Hsieh V. Cevher 26 18 0 08 Jun 2022
A Unified Convergence Theorem for Stochastic Optimization Methods Xiao Li Andre Milzarek 28 11 0 08 Jun 2022
Metrizing Fairness Yves Rychener Bahar Taşkesen Daniel Kuhn FaML 36 4 0 30 May 2022
Uniform Generalization Bound on Time and Inverse Temperature for Gradient Descent Algorithm and its Application to Analysis of Simulated Annealing Keisuke Suzuki AI4CE 19 0 0 25 May 2022
Weak Convergence of Approximate reflection coupling and its Application to Non-convex Optimization Keisuke Suzuki 9 5 0 24 May 2022
A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima Tae-Eon Ko Xiantao Li 20 2 0 21 Mar 2022
$Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations$ Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations Ling Guo Hao Wu Xiao-Jun Yu Tao Zhou PINN AI4CE 16 58 0 16 Mar 2022
On Almost Sure Convergence Rates of Stochastic Gradient Methods Jun Liu Ye Yuan 6 36 0 09 Feb 2022
A subsampling approach for Bayesian model selection Jon Lachmann G. Storvik F. Frommlet Aliaksadr Hubin BDL 19 2 0 31 Jan 2022
On Uniform Boundedness Properties of SGD and its Momentum Variants Xiaoyu Wang M. Johansson 18 3 0 25 Jan 2022
3DPG: Distributed Deep Deterministic Policy Gradient Algorithms for Networked Multi-Agent Systems Adrian Redder Arunselvan Ramaswamy Holger Karl OffRL 11 2 0 03 Jan 2022
Non-Asymptotic Analysis of Online Multiplicative Stochastic Gradient Descent Riddhiman Bhattacharya Tiefeng Jiang 8 0 0 14 Dec 2021
Stationary Behavior of Constant Stepsize SGD Type Algorithms: An Asymptotic Characterization Zaiwei Chen Shancong Mou S. T. Maguluri 17 13 0 11 Nov 2021
Inertial Newton Algorithms Avoiding Strict Saddle Points Camille Castera ODL 11 2 0 08 Nov 2021
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals James A. Brofos Marylou Gabrié Marcus A. Brubaker Roy R. Lederman 17 8 0 25 Oct 2021
Accelerated Almost-Sure Convergence Rates for Nonconvex Stochastic Gradient Descent using Stochastic Learning Rates Theodoros Mamalis D. Stipanović R. Tao 16 2 0 25 Oct 2021
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization Yuhao Ding Junzi Zhang Hyunin Lee Javad Lavaei 30 18 0 19 Oct 2021
Global Convergence and Stability of Stochastic Gradient Descent V. Patel Shushu Zhang Bowen Tian 23 22 0 04 Oct 2021
Stochastic Subgradient Descent on a Generic Definable Function Converges to a Minimizer S. Schechtman 22 1 0 06 Sep 2021
Convergence of gradient descent for learning linear neural networks Gabin Maxime Nguegnang Holger Rauhut Ulrich Terstiege MLT 20 16 0 04 Aug 2021
SGD with a Constant Large Learning Rate Can Converge to Local Maxima Liu Ziyin Botao Li James B. Simon Masakuni Ueda 13 8 0 25 Jul 2021
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses Keegan Harris Daniel Ngo Logan Stapleton Hoda Heidari Zhiwei Steven Wu 14 31 0 12 Jul 2021