NeurIPS 2020poster24 citations
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni
Abstract
The notion of implicit bias, or implicit regularization, has been suggested as a means to explain the surprising generalization ability of modern-days overparameterized learning algorithms. This notion refers to the tendency of the optimization algorithm towards a certain structured solution that often generalizes well. Recently, several papers have studied implicit regularization and were able to identify this phenomenon in various scenarios.
BibTeX
@inproceedings{NEURIPS2020_57cd30d9,
author = {Dauber, Assaf and Feder, Meir and Koren, Tomer and Livni, Roi},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {7743--7753},
publisher = {Curran Associates, Inc.},
title = {Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/57cd30d9088b0185cf0ebca1a472ff1d-Paper.pdf},
volume = {33},
year = {2020}
}