Michael Feffer, Martin Hirzel, et al.
ICML 2022
We study the convergence of a random iterative sequence of a family of operators on infinite-dimensional Hilbert spaces, inspired by the stochastic gradient descent (SGD) algorithm in the case of the noiseless regression. We identify conditions that are strictly broader than previously known for polynomial convergence rate in various norms, and characterize the roles the randomness plays in determining the best multiplicative constants. Additionally, we prove almost sure convergence of the sequence.
Michael Feffer, Martin Hirzel, et al.
ICML 2022
Jitendra Singh, Smit Marvaniya, et al.
INFORMS 2022
Erik Altman, Jovan Blanusa, et al.
NeurIPS 2023
Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024