Probabilistic Graphical Models

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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  1. A Practical Guide on Graphical Model Validation

    Sep 22, 2026Mario V. WüthrichActuarial RatemakingProbabilistic Graphical Models

    Department of Mathematics, ETH Zurich

  2. Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach

    Sep 11, 2026Adrián Detavernier, Jasper De BockRobustness VerificationRandom Forest

    Foundations Lab for imprecise probabilities Ghent University Ghent, Belgium

  3. Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships

    Sep 7, 2026Papri DeyProbabilistic Graphical ModelsBayesian

  4. Accelerated Random-Sweep Gibbs Sampling for Gaussian Graphical Models via Dual Normal Factor Graphs

    Jul 30, 2026Borna Khodabandeh, Mehdi MolkaraieProbabilistic Graphical ModelsGibbs

    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

  5. A Characterization of the Orthocomplement of the Tangent Space of Semiparametric Markov Models

    Jul 26, 2026Trung Phung, Ilya ShpitserProbabilistic Graphical ModelsInfluence Function

    Computer Science Department, Johns Hopkins University, Baltimore, Maryland, USA

  6. Graph-Based Correlation Matrix Generation: A Convex Optimization Approach

    Jul 24, 2026Ali Fakhar, K{é}vin Polisano, Ir{è}ne Gannaz +1Full-Rank Correlation MatricesMatrix

    UGA · Univ. Grenoble Alpes, CNRS, Grenoble INP, Inria, LJK, F-38000 Grenoble, France · G-SCOP\_GROG, G-SCOP, Grenoble INP, UGA +2

  7. Parallel Noising in Neural Markov Logic Networks

    Jul 21, 2026Peter Jung, Giuseppe Marra, Ondrej KuzelkaGraph Neural NetworksProbabilistic Graphical Models

    Czech Technical University, Prague, Czech Republic · Department of Computer Science, KU Leuven, Leuven, Belgium

  8. Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics

    Jul 20, 2026Vignesh Tirukkonda, Gautam DasarathyProbabilistic Graphical ModelsOptimal Learner

    Arizona State University

  9. Learning AC0\mathsf{AC}^0 under Locally Sampleable Graphical Models

    Jul 9, 2026Weiming Feng, Xiongxin Yang, Yixiao Yu +1LearnabilityQuantum Learning

    School of Computing and Data Science, The University of Hong Kong. · Department of Computer Science, University of California, Santa Barbara. · State Key Laboratory for Novel Software Technology, New Cornerstone Science Laboratory, Nanjing University.

  10. Characterizing and Identifying Separable Graphical Models

    Jul 1, 2026Christopher Meek, Kayvan SadeghiProbabilistic Graphical ModelsGraph Representations

    Department of Statistics, University of Washington · Department of Statistical Science, University College London

  11. Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing

    Jun 30, 2026Eric Shen, Tony Wu, Mahbod Majid +1Probabilistic Graphical ModelsConditional Independence

    MIT

  12. Lifted Causal Inference

    Jun 26, 2026Malte Luttermann, Tanya Braun, Ralf Möller +1Causal GraphCausal Inferences

    Institute for Humanities-Centered Artificial Intelligence, University of Hamburg, Hamburg, Germany. · German Research Center for Artificial Intelligence (DFKI), Lübeck, Germany. · Data Science Group, University of Münster, Münster, Germany.

  13. Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems

    Jun 14, 2026Víctor A. Braberman, Flavia Bonomo-BrabermanLarge Language Model GenerationGenerative Flow Networks

    Departamento de Computación, FCEN, Universidad de Buenos Aires / ICC, UBA-CONICET Buenos Aires, Argentina

  14. In-Context Graphical Inference

    Jun 3, 2026Zehua Cheng, Wei Dai, Jiahao SunProbabilistic Graphical ModelsProbabilistic Inference

    Department of Computer Science, University of Oxford, Oxford, United Kingdom · FLock.io, London, United Kingdom

  15. Destruction is a General Strategy to Learn Generation; Diffusion's Strength is to Take it Seriously; Exploration is the Future

    May 28, 2026Pierre-André NoëlDiffusion ModelsProbabilistic Graphical Models

    ServiceNow AI Research

  16. On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions

    May 26, 2026Malte Luttermann, Ralf Möller, Marcel GehrkeFactor GraphsProbabilistic Graphical Models

    Institute for Humanities-Centered Artificial Intelligence, University of Hamburg, Germany

  17. Private Adaptive Covariance Estimation via Gaussian Graphical Models

    May 22, 2026Cecilia Ferrando, Miguel Fuentes, Brett Mullins +2CovarianceEmpirical Bayes

    Manning College of Information and Computer Sciences University of Massachusetts Amherst

  18. Testing properties of trees in graphical models with covariance queries

    May 15, 2026Sofiya Burova, Francisco Calvillo, Gábor Lugosi +1Probabilistic Graphical ModelsTrees

    IMTECH and Departament de Matem`atiques, Universitat Polit`nica de Catalunya, Barcelona, Spain and Department of Economics and Business, Pompeu Fabra University, Barcelona, Spain. · LPSM, Sorbonne Universit´e, 4 Place Jussieu, 75005 Paris, France · ICREA, Pg. Llu´ıs Companys 23, 08010 Barcelona, Spain; Department of Economics and Business, Pompeu Fabra University, Barcelona, Spain; Barcelona School of Economics. +1

  19. Proximal Projection for Doubly Sparse Regularized Models

    May 6, 2026Jia Wei He, R. Ayesha Ali, Gerarda DarlingtonProbabilistic Graphical ModelsProximal Operators

    Department of Mathematics & Statistics , University of Guelph

  20. Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States

    Apr 26, 2026Teng Li, Stephen Wu, Yong Huang +2Probabilistic Graphical ModelsGraph Neural Networks

    Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, School of Civil Engineering, Harbin, Institute of Technology, Harbin 150090, China · The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan +2

  21. Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning

    Jul 16, 2025Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee +3Multi--Task LearningMixtures

    IIT Bombay · IBM Research · Red Hat AI Innovation +1

  22. Delta-AI: Local objectives for amortized inference in sparse graphical models

    Oct 3, 2023Jean-Pierre Falet, Hae Beom Lee, Esmeralda S. Whitammer +6Probabilistic InferenceProbabilistic Graphical Models

    Mila – Qu´ebec AI Institute, Universit´e de Montr´eal · Mila – Québec AI Institute, Université de Montréal