11 citations · 17 across the 16 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Exact Schur-Sylvester Dimensionality Reductions for Non-Smooth Stochastic Complexity and Manifold Sampling
Trenton Lau, Gary P. T. Choi
The exact computation of the Normalized Maximum Likelihood (NML) codelength for regular non-smooth estimators (e.g., Lasso) has been historically limited by the cubic scaling walls…
cs.LG2026
The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling
Trenton Lau, Gary P. T. Choi
The Normalized Maximum Likelihood (NML) codelength, or stochastic complexity, represents a principled criterion for universal coding. While recent coarea-based formulations provide…