3 citations · 4 across the 13 of their papers we have counts for
4 papers · 1 filter
Interpretive Efficiency: Information-Geometric Foundations of Data Usefulness
Ronald Katende
Interpretability is central to trustworthy machine learning, yet existing metrics rarely quantify how effectively data support an interpretive representation. We propose Interpreti…
Geometry as a Missing Axis of Representation Quality: The Variational Geometric Information Bottleneck under Data Scarcity
Ronald Katende
We study latent geometry as an explicit component of representation quality in data-scarce learning. For an encoder (ϕ), we define (Q_{β,γ}(ϕ)=I(ϕ(X);Y)-β\mathcal C(ϕ)-γd_{\mathrm{…
Structured Variational -Decomposition for Accurate and Stable Low-Rank Approximation
Ronald Katende
We introduce the -decomposition, a non-orthogonal matrix factorization of the form , where , , and…
Counterfactual Operator Relevance for PDE Discovery: Screening, Pruning, and Identifiability
Ronald Katende
We study operator relevance in data-driven partial differential equation (PDE) discovery. Sparse residual methods can select terms that improve residual fit, but residual contribut…