3 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, limiting flexibility and interpretability. We introduce a model-free framework using surprisal (infor…
cs.SE2023
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…
cs.LG2023
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…