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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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Volume 186: International Conference on Probabilistic Graphical Models, 5-7 October 2022, Almerı́a, Spain

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Editors: Antonio Salmerón, Rafael Rumı́

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Limited Memory Influence Diagrams for Attribute Statistical Process Control with Variable Sample Sizes

; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:1-12

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Relevance for Robust Bayesian Network MAP-Explanations

Silja Renooij; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:13-24

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The Functional LiNGAM

Tianle Yang, Joe Suzuki; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:25-36

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Online Single-Microphone Source Separation using Non-Linear Autoregressive Models

Bart van Erp, Bert de Vries; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:37-48

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Anytime Learning of Sum-Product and Sum-Product-Max Networks

Swaraj Pawar, Prashant Doshi; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:49-60

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Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling

Peter Strong, Jim Q. Smith; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:61-72

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Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets

Marco Scutari, Christopher Marquis, Laura Azzimonti; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:73-84

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Robust Estimation of Laplacian Constrained Gaussian Graphical Models with Trimmed Non-convex Regularization

Mariana Vargas Vieyra; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:85-96

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Online Updating of Conditional Linear Gaussian Bayesian Networks

Anders L Madsen, Kristian G Olesen, Frank Jensen, Per Henriksen, Thomas M Larsen, Jørn M Møller; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:97-108

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A Transformational Characterization of Unconditionally Equivalent Bayesian Networks

Alex Markham, Danai Deligeorgaki, Pratik Misra, Liam Solus; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:109-120

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Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound

Kiattikun Chobtham, Anthony C. Constantinou; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:121-132

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Model inclusion lattice of coloured Gaussian graphical models for paired data

Alberto Roverato, Dung Ngoc Nguyen; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:133-144

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Parameterized Completeness Results for Bayesian Inference

Hans L. Bodlaender, Nils Donselaar, Johan Kwisthout; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:145-156

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Convergence of Feedback Arc Set-Based Heuristics for Linear Structural Equation Models

Pierre Gillot, Pekka Parviainen; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:157-168

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You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks

Rafael Ballester-Ripoll, Manuele Leonelli; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:169-180

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Scalable Bayesian Network Structure Learning with Splines

Charupriya Sharma, Peter van Beek; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:181-192

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Highly Efficient Structural Learning of Sparse Staged Trees

Manuele Leonelli, Gherardo Varando; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:193-204

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A Reparameterization of Mixtures of Truncated Basis Functions and its Applications

Antonio Salmerón, Helge Langseth, Andrés Masegosa, Thomas D. Nielsen; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:205-216

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Who did it? Identifying the Most Likely Origins of Events

Marcel Gehrke, Ralf Möller, Tanya Braun; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:217-228

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Speeding up approximate MAP by applying domain knowledge about relevant variables

Johan Kwisthout; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:229-240

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A Hybrid Algorithm for Learning Causal Networks using Uncertain Experts’ Knowledge

Christophe Gonzales, Axel Journe, Ahmed Mabrouk; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:241-252

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A Decision Support System to Predict Acute Fish Toxicity

Anders L Madsen, S. Jannicke Moe, Thomas Braunbeck, Kristin A. Connors, Michelle Embry, Kristin Schirmer, Stefan Scholz, Raoul Wolf, Adam Lillicrap; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:253-264

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Recursive autonomy identification-based learning of augmented naive Bayes classifiers

Shouta Sugahara, Wakaba Kishida, Koya Kato, Maomi Ueno; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:265-276

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Learning Noisy-Or Networks with an Application in Linguistics

František Kratochvíl, Václav Kratochvíl, Jiří Vomlel; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:277-288

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Bounding Counterfactuals under Selection Bias

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:289-300

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The Dual PC Algorithm for Structure Learning

Enrico Giudice, Jack Kuipers, Giusi Moffa; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:301-312

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Structure learning algorithms for multidimensional continuous-time Bayesian network classifiers

Carlos Villa-Blanco, Alessandro Bregoli, Concha Bielza, Pedro Larrañaga, Fabio Stella; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:313-324

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Explaining Deep Tractable Probabilistic Models: The sum-product network case

Bhagirath Athresh Karanam, Saurabh Mathur, Predrag Radivojac, David M Haas, Kristian Kersting, Sriraam Natarajan; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:325-336

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Integrating Bayesian network classifiers to deal with the partial label ranking problem

Juan C. Alfaro, Juan A. Aledo, José A. Gámez; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:337-348

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A Hardware Perspective to Evaluating Probabilistic Circuits

Jelin Leslin, Antti Hyttinen, Karthekeyan Periasamy, Lingyun Yao, Martin Trapp, Martin Andraud; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:349-360

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On the rank of 2×2×2 probability tables

Iván Pérez, Jiřı́ Vomlel; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:361-372

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Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling

Enrique Valero-Leal, Pedro Larrañaga, Concha Bielza; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:373-384

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Knowledge transfer for learning subject-specific causal models

Verónica Rodrı́guez-López, Luis Enrique Sucar; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:385-396

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Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection

Jorge Casajús-Setién, Concha Bielza, Pedro Larrañaga; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:397-408

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Graphical Representations for Algebraic Constraints of Linear Structural Equations Models

Thijs van Ommen, Mathias Drton; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:409-420

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Causal Discovery and Reinforcement Learning: A Synergistic Integration

Arquı́mides Méndez-Molina, Eduardo F.Morales, L. Enrique Sucar; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:421-432

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Approximate Inference for Stochastic Planning in Factored Spaces

Zhennan Wu, Roni Khardon; Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:433-444

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