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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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 AI

Sep 23

On Probabilistic Inference Through Parametric Tensor Decomposition in Base Tensor Networks

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 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 Statistics ML

Sep 23

Likelihood Based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants

arXiv:2404.17763v3 Announce Type: replace-cross Abstract: Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn...

By Yujie Chen, Anindya Bhadra, Antik Chakraborty

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Related stories

Inducing Comparability of Factorised Probability Distributions

RAMP: Recognition parametrisation by Amortised Message Passing

On Probabilistic Inference Through Parametric Tensor Decomposition in Base Tensor Networks

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

Bayes-Sufficient Representations in Supervised Learning

Likelihood Based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants