Factor Graphs

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Direct Message Approximation (DMA): A Consistency-Based Framework for Tractable Approximate Inference on Factor Graphs
Sep 24, 2026Ralf Herbrich, Rainer Schlosser, Jan Lemcke +11Message Passing Neural Networks Probabilistic Inference
Hasso Plattner Institute University of Potsdam Potsdam, Germany
Multi-Session Multimodal Underwater Mapping with Acoustic and Optical Imaging
Sep 15, 2026Precious Philip-Ifabiyi, Valerio Franchi, Fausto Ferreira +1Geometric Bathymetric Data Underwater Acoustic Target Recognition
Computer Vision and Robotics Research Institute (ViCOROB), University of Girona, Campus Montilivi, Edifici P4, Girona, 17003, Catalonia, Spain · University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia · University of Zagreb Faculty of Electrical Engineering and Computing, Laboratory for Underwater Systems and Technologies (LABUST), Zagreb, Croatia +1
Differentiable Mesh State Estimation via Factor Graph Inference for Deformable Object Reconstruction
Sep 15, 2026Lidia Al-Zogbi, Fangjie Li, Samuel Tobin +13State Estimation Geometric Reconstruction
Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA
A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
Aug 12, 2026Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko +2Gaussian Process Factor Graphs
Eindhoven University of Technology & Lazy Dynamics B.V., the Netherlands · Eindhoven University of Technology, Eindhoven, the Netherlands · Lazy Dynamics B.V., the Netherlands
Beyond Co-Movement: Locality by Exposures Enables a Joint Factor-Graph Framework for Portfolio Diversification
Aug 6, 2026Sara Chehab, Giorgos Iacovides, Parisa Yazdanparast +1Portfolio Construction Factor Graphs
Imperial College London London, UK
Accelerated Random-Sweep Gibbs Sampling for Gaussian Graphical Models via Dual Normal Factor Graphs
Jul 30, 2026Borna Khodabandeh, Mehdi MolkaraieProbabilistic Graphical Models Gibbs
Department of Statistics, University of Oxford, Oxford OX1 3LB, UK · Department of Signal Theory and Communications, UPC, 08034 Barcelona, Spain · Department of Statistical Sciences, University of Toronto, ON M5G 1X6, Canada
Amortized Bayesian Causal Discovery of Extended Factor Graphs
Jul 24, 2026Yichen Gu, Yuxuan Song, Weizhou Qian +2Causal Graph Regulatory Networks
Department of Electrical and Computer Engineering University of Michigan Ann Arbor, MI 48109, USA · Department of Computational Medicine and Bioinformatics University of Michigan, Ann Arbor Ann Arbor, MI 48109, USA · Department of Statistics University of Michigan, Ann Arbor Ann Arbor, MI 48109, USA +1
Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs
Jul 24, 2026Zhangzhi Xiong, Minzhang Li, Haotian Yu +6Protein Structure Prediction Feature Alignment
School of Information Science and Technology, ShanghaiTech University
Multi-Contact Force Estimation for Continuum Robots via Gaussian-Parameterized Factor Graphs
Jun 28, 2026Aditya Prakash, Panagiotis TsiotrasGround Reaction Forces Cosserat Rod Theory
School of Aerospace Engineering Georgia Institute of Technology Atlanta, GA, USA
Information Lattice Learning as Probabilistic Graphical Model Structure Learning
Jun 11, 2026Haizi Yu, Lav R. VarshneyProbabilistic Inference Factor Graphs
Kocree, Inc. · AI Innovation Institute, Stony Brook University
Exploiting Chordal Sparsity for Globally Optimal Estimation with Factor Graphs
May 28, 2026Avinash Subramanian, Connor Holmes, Timothy D. Barfoot +2Factor Graphs State Estimation
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA · Robotics Institute, University of Toronto, Toronto, ON, Canada · Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
May 28, 2026Mykola Lukashchuk, Kyrylo Yemets, Wouter M. Kouw +4Variational Inference Factor Graphs
Eindhoven University of Technology, the Netherlands · Lviv Polytechnic National University, Lviv, Ukraine · Lazy Dynamics, Utrecht, the Netherlands +1
On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
May 26, 2026Malte Luttermann, Ralf Möller, Marcel GehrkeFactor Graphs Probabilistic Graphical Models
Institute for Humanities-Centered Artificial Intelligence, University of Hamburg, Germany
Loosely Coupled Factor Graph Optimization for Pseudolite-Augmented Navigation
May 24, 2026Chih-Chun Chen, Lipeng Tan, Shiyu Bai +1Global Navigation Satellite System Factor Graphs
Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularization
May 18, 2026Chuansen Peng, Xiaojing ShenRiemannian Manifolds Factor Graphs
School of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, China
DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments
May 13, 2026Mikolaj Kliniewski, Jesse Morris, Yiduo Wang +2Dynamic Environments Planning
Australian Centre For Robotics (ACFR), School of Aerospace, Mechanical and Mechatronic Engineering (AMME), University of Sydney, 2006 Sydney, Australia.
Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs
May 9, 2026Connor Holmes, Sven Lilge, Zi Cong Guo +2Factor Graphs State Estimation
University of Toronto · Georgia Institute of Technology
On the Safety of Graph Representation Learning
May 7, 2026Xiaoguang Guo, Zehong Wang, Ziming Li +5Graph Representation Learning Graph Neural Networks
University of Connecticut · University of Notre Dame
FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation
May 7, 2026Mingfeng Lin, Jiakun Chen, Liang Han +1Multi-Reference Image Generation Image Generation
Harbin Institute of Technology (Shenzhen)
CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision
May 7, 2026Liang Qian, Penggao Yan, Penghui Xu +1Global Navigation Satellite System Factor Graphs
Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University, Hong Kong, China
Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
May 6, 2026Filip Rydin, Morteza Haghir Chehreghani, Balázs KulcsárVehicle Routing Problem Factor Graphs
Chalmers University of Technology · University of Gothenburg
Temporally Consistent Object 6D Pose Estimation for Robot Control
May 4, 2026Kateryna Zorina, Vojtech Priban, Mederic Fourmy +2Category-Level Object Pose Estimation Robotic Perception
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague
AnchorD: Metric Grounding of Monocular Depth Using Factor Graphs
May 4, 2026Simon Dorer, Martin Büchner, Nick Heppert +1Monocular Depth Estimation Robotic Perception
Department of Computer Science, University of Freiburg, Germany. · 2Zuse School ELIZA
Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
Apr 29, 2026Zhangzhi Xiong, Haoyi Wu, You Wu +3Time Series Spatio-Temporal Transformers
School of Information Science and Technology, ShanghaiTech University

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Direct Message Approximation (DMA): A Consistency-Based Framework for Tractable Approximate Inference on Factor Graphs

Ralf Herbrich, Rainer Schlosser, Jan Lemcke, Johann Ukrow+10

Hasso Plattner Institute University of Potsdam Potsdam, Germany

Message Passing Neural Networks Probabilistic Inference Factor Graphs Factor

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Approximate message passing on factor graphs underlies two dominant families of probabilistic inference algorithms: expectation propagation (EP) and variational message passing (VMP). Both methods approximate the marginal at each factor edge, forcing an iterative round-robin schedule, risking negative-precision messages, and, for VMP, collapsing to point estimates at Dirac-delta factors. We introduce Direct Message Approximation (DMA), which approximates factor-to-variable messages directly rather than the marginal. For normalisable factors, we define a consistency condition (requiring exactness when all other incoming messages are Dirac deltas) to guide message construction. We prove a master theorem (proper messages, any graph) bounding marginal KL from message KL, with three structural corollaries: Dirac-input consistency, no EP-style inner-loop iteration, and no negative-precision messages. Further, we prove a complementary

O(1/r^2)

guarantee for the inherently improper backward message of the product factor, whose closed-form treatment has resisted prior work. As a concrete instantiation, we derive explicit DMA messages for the product and leaky-ReLU factors and assemble a Bayesian neural network (BNN) inference algorithm with one forward/backward sweep per training example and no gradient learning-rate hyperparameter, validating that the structural guarantees translate to predictive uncertainty that widens in data-sparse regions, including under model mismatch.

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