Lifted inference algorithms scale probabilistic inference by exploiting indistinguishable objects, but real‑world data often yields only approximately commutative factors. The paper introduces ε‑commutativity, a relaxation that tolerates small deviations from exact invariance, and shows how it can be used to build lifted models and perform inference with provable error bounds. Empirical results confirm that queries remain accurate while runtime is reduced.
By Malte Luttermann, Jan Speller, Tanya Braun, Marcel Gehrke, Ralf M\"oller
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: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:2407. 07338v4 Announce Type: replace-cross Abstract: We study the problem of restricting a Markov equivalence class of maximal ancestral graphs (MAGs) to only those MAGs that contain certain edge marks, which we refer to as expert or orientation knowledge.
By Aparajithan Venkateswaran, Emilija Perkovi\'c
arXiv:2404. 02692v3 Announce Type: replace-cross Abstract: The explosion of data available in life sciences is fueling an increasing demand for expressive models and computational methods.
By Jakob L. Andersen, Akbar Davoodi, Rolf Fagerberg, Christoph Flamm, Walter Fontana, Juri Kol\v{c}\'ak, Christophe V. F. P. Laurent, Daniel Merkle, Nikolai N{\o}jgaard
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:2608.31120v1 Announce Type: new
Abstract: The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used t...
By Guy Emerson
arXiv:2609. 29466v1 Announce Type: cross Abstract: Approximate message passing on factor graphs underlies two dominant families of probabilistic inference algorithms: expectation propagation (EP) and variational message passing (VMP).
By Ralf Herbrich, Rainer Schlosser, Jan Lemcke, Johann Ukrow, Anna Kazachkova, Nicolas Alder, Leonhard Hennicke, Theo Bardey, Nico Grimm, Luca Kleinschmidt, Philipp Kolbe, Cezary Kujath, Johanna Schlimme, Karl Matti Sch\"utz
arXiv:2606. 27737v1 Announce Type: new Abstract: Programming adaptive behaviors at the cellular level is a long-standing goal that raises the question of how probabilistic computation can be implemented in biochemical systems.
By Mauricio Montes, Gregoire Sergeant-Perthuis
arXiv:2608. 08118v1 Announce Type: new Abstract: There are several methods for searching for graphs with prescribed properties, such as SAT solvers and specialized generators.
By David Seka, Stefan Szeider
arXiv:2606. 28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
By Malte Luttermann, Tanya Braun, Ralf M\"oller, Marcel Gehrke
arXiv:2603. 13854v2 Announce Type: replace-cross Abstract: We introduce power term polynomial algebra, a representation language for Boolean formulae designed to bridge conjunctive normal form (CNF) and algebraic normal form (ANF).
By Emanuele Sansone, Armando Solar-Lezama