5 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
Limits of Transfer Learning
Jake Williams, Abel Tadesse, Tyler Sam +2
Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer lea…
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…
Decomposable Probability-of-Success Metrics in Algorithmic Search
Tyler Sam, Jake Williams, Abel Tadesse +2
Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric p…