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"

10 / 60 papers shown

Title
Combining resampling and reweighting for faithful stochastic optimization Jing An Lexing Ying 9 1 0 31 May 2021
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis Stephan Wojtowytsch 23 50 0 04 May 2021
Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis Zezhong Zhang Guangxu Zhu Rui-cang Wang Vincent K. N. Lau Kaibin Huang 33 31 0 20 Apr 2021
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes Steffen Dereich Sebastian Kassing 28 27 0 16 Feb 2021
Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance Anas Barakat Pascal Bianchi W. Hachem S. Schechtman 21 13 0 07 Dec 2020
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER Markus Holzleitner Lukas Gruber Jose A. Arjona-Medina Johannes Brandstetter Sepp Hochreiter 23 38 0 02 Dec 2020
Unconstrained optimisation on Riemannian manifolds T. Truong 6 4 0 25 Aug 2020
Two-Timescale Stochastic Gradient Descent in Continuous Time with Applications to Joint Online Parameter Estimation and Optimal Sensor Placement Louis Sharrock N. Kantas 12 7 0 31 Jul 2020
The limits of min-max optimization algorithms: convergence to spurious non-critical sets Ya-Ping Hsieh P. Mertikopoulos V. Cevher 27 81 0 16 Jun 2020
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions V. Patel 18 23 0 01 Apr 2020