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stat.ML2025
Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory
Einar Urdshals, Edmund Lau, Jesse Hoogland +2
We study neural network compressibility by using singular learning theory to extend the minimum description length (MDL) principle to singular models like neural networks. Through…
stat.ML2023
The Local Learning Coefficient: A Singularity-Aware Complexity Measure
Edmund Lau, Zach Furman, George Wang +2
The Local Learning Coefficient (LLC) is introduced as a novel complexity measure for deep neural networks (DNNs). Recognizing the limitations of traditional complexity measures, th…
stat.ML2023
Variational Bayesian Neural Networks via Resolution of Singularities
Susan Wei, Edmund Lau
In this work, we advocate for the importance of singular learning theory (SLT) as it pertains to the theory and practice of variational inference in Bayesian neural networks (BNNs)…