Stochastic approximation with averaging and feedback: rapidly convergent "on-line" algorithms

By Harold J. Kushner, Jichuan Yang. 1995

Subfields: Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Optimization & Training

1. Simon Lacoste-Julien
2. Issam Hadj Laradji
3. Gauthier Gidel
4. Sharan Vaswani
5. Nicolas Loizou
6. Peter Richtárik
7. Mark W. Schmidt
8. Aaron Mishkin

Citations in the corpus

• Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates (2019, methodology): “These techniques include using meta-learning procedures to modify the main stochastic algorithm [6, 90, 77, 2, 65, 90, 79], heuristics to adjust the learning rate on the fly [44, 21, 72, 76], and recent adaptive methods inspired by online learning [22, 91, 40, 69, 62, 70, 53].”