1 citations · 1 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…
Trading Off Scalability, Privacy, and Performance in Data Synthesis
Xiao Ling, Tim Menzies, Christopher Hazard +2
Synthetic data has been widely applied in the real world recently. One typical example is the creation of synthetic data for privacy concerned datasets. In this scenario, synthetic…
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…