5 citations · 6 across the 4 of their papers we have counts for
Showing 2020 · cs.LGShow all
2 papers · 2 filters
cs.LG2020
An Information-Theoretic Perspective on Overfitting and Underfitting
Daniel Bashir, George D. Montanez, Sonia Sehra +2
We present an information-theoretic framework for understanding overfitting and underfitting in machine learning and prove the formal undecidability of determining whether an arbit…
cs.LG2020★ 5 cited
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity
Pedro Sandoval Segura, Julius Lauw, Daniel Bashir +4
Algorithm performance in supervised learning is a combination of memorization, generalization, and luck. By estimating how much information an algorithm can memorize from a dataset…