1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
Christopher J. Hazard, Michael Resnick, Jacob Beel +7
Traditional machine learning relies on explicit models and domain assumptions, often limiting flexibility and interpretability. We introduce a "model-free" framework of instance-ba…
cs.LG2023★ 1 cited
Surprisal Driven -NN for Robust and Interpretable Nonparametric Learning
Amartya Banerjee, Christopher J. Hazard, Jacob Beel +4
Nonparametric learning is a fundamental concept in machine learning that aims to capture complex patterns and relationships in data without making strong assumptions about the unde…