11 citations · 12 across the 3 of their papers we have counts for
3 papers
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…
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…
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions
Christopher J. Hazard, Christopher Fusting, Michael Resnick +3
Machine learning models have become more and more complex in order to better approximate complex functions. Although fruitful in many domains, the added complexity has come at the…