arXiv AI June 19, 2026 By Haizi Yu, Lav R. Varshney

Information Lattice Learning as Probabilistic Graphical Model Structure Learning

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 24

Inducing Comparability of Factorised Probability Distributions

arXiv:2607. 20502v1 Announce Type: new Abstract: To allow for principled comparison between two probabilistic graphical models defined over non-identical variable sets, they have to be lifted to a common measurable space.

By Jan Speller, Malte Luttermann, Marcel Gehrke, Tanya Braun

arXiv AI

Jul 22

RAMP: Recognition parametrisation by Amortised Message Passing

arXiv:2607. 18883v1 Announce Type: cross Abstract: A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations.

By Lior Fox, Kai Biegun, James Heald, Samo Hromadka, Arielle Rosinski, Maneesh Sahani

arXiv Machine Learning

Aug 3

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

arXiv:2401. 04890v2 Announce Type: replace-cross Abstract: This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors.

By S\'ebastien Lachapelle, Pau Rodr\'iguez L\'opez, Yash Sharma, Katie Everett, R\'emi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

efficiency

arXiv AI

Jun 4

Bayes-Sufficient Representations in Supervised Learning

arXiv:2606. 04045v1 Announce Type: cross Abstract: Representation learning is often described as preserving the information in an input that is relevant for prediction.

By Vasileios Sevetlidis

arXiv Machine Learning

Jul 30

High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption

arXiv:2607. 26357v1 Announce Type: new Abstract: The problem of learning the graphical Markov blanket (MB) of a variable from data has applications in many areas such as structure learning for Bayesian networks and Markov random fields, causal discovery, and feature selection.

By Loong Kuan Lee, Ragavi Krishnamoorthy, Nico Piatkowski

arXiv Machine Learning

Jun 5

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.

By Hazhir Aliahmadi, Irina Babayan, Greg van Anders

Related stories

Inducing Comparability of Factorised Probability Distributions

RAMP: Recognition parametrisation by Amortised Message Passing

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

Bayes-Sufficient Representations in Supervised Learning

High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs