arXiv:2609.23774v1 Announce Type: new
Abstract: Probabilistic inference is generally only tractable in low-treewidth graphical models, limiting its effective applicability in high-treewidth settings....
By Sagad Hamid, Tanya Braun
arXiv:2606. 19366v1 Announce Type: cross Abstract: Information lattice learning (ILL) learns interpretable rules of a signal by alternately projecting the signal onto a partition lattice that encodes a hierarchy of abstractions and lifting selected rules back to the signal domain.
By Haizi Yu, Lav R. Varshney
arXiv:2410. 06329v4 Announce Type: replace-cross Abstract: Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant objective in statistical signal processing and machine learning.
By Joseph K. Chege, Arie Yeredor, Martin Haardt
Large language models (LLMs) are built from structured high-dimensional objects such as token representations, weights, adaptation updates, caches, and activations, whose multilinear structure is unde...
The paper establishes tight pseudo-dimension bounds for data-driven multiple hyper‑parameter tuning with structured loss functions. By refining upper bounds through real algebraic geometry and analyzing invariant connected sign cells, the authors avoid over‑counting and achieve sharper sample complexities. A multi‑regime lower‑bound framework demonstrates that these upper bounds are tight, and the approach is extended to general bi‑level validation‑loss tuning and broader semi‑algebraic applications.
By Anh Tuan Nguyen, Viet Anh Nguyen
Ensuring model reliability in Explainable AI requires a global assessment of the hypothesis space. We propose a formal framework for the exhaustive analysis of optimal and near-optimal decision trees, called Algebraic Decision Tree Counting (ADTC).