PGM 2022

37 papers

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

A Hardware Perspective to Evaluating Probabilistic Circuits Jelin Leslin, Antti Hyttinen, Karthekeyan Periasamy, Lingyun Yao, Martin Trapp, Martin Andraud

A Hybrid Algorithm for Learning Causal Networks Using Uncertain Experts’ Knowledge Christophe Gonzales, Axel Journe, Ahmed Mabrouk

A Reparameterization of Mixtures of Truncated Basis Functions and Its Applications Antonio Salmerón, Helge Langseth, Andrés Masegosa, Thomas D. Nielsen

A Transformational Characterization of Unconditionally Equivalent Bayesian Networks Alex Markham, Danai Deligeorgaki, Pratik Misra, Liam Solus

Anytime Learning of Sum-Product and Sum-Product-Max Networks Swaraj Pawar, Prashant Doshi

Approximate Inference for Stochastic Planning in Factored Spaces Zhennan Wu, Roni Khardon

Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling Peter Strong, Jim Q. Smith

Bounding Counterfactuals Under Selection Bias Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti

Causal Discovery and Reinforcement Learning: A Synergistic Integration Arquı́mides Méndez-Molina, Eduardo F.Morales, L. Enrique Sucar

Convergence of Feedback Arc Set-Based Heuristics for Linear Structural Equation Models Pierre Gillot, Pekka Parviainen

Discovery and Density Estimation of Latent Confounders in Bayesian Networks with Evidence Lower Bound Kiattikun Chobtham, Anthony C. Constantinou

Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection Jorge Casajús-Setién, Concha Bielza, Pedro Larrañaga

Explaining Deep Tractable Probabilistic Models: The Sum-Product Network Case Bhagirath Athresh Karanam, Saurabh Mathur, Predrag Radivojac, David M Haas, Kristian Kersting, Sriraam Natarajan

Graphical Representations for Algebraic Constraints of Linear Structural Equations Models Thijs Ommen, Mathias Drton

Highly Efficient Structural Learning of Sparse Staged Trees Manuele Leonelli, Gherardo Varando

Integrating Bayesian Network Classifiers to Deal with the Partial Label Ranking Problem Juan C. Alfaro, Juan A. Aledo, José A. Gámez

Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling Enrique Valero-Leal, Pedro Larrañaga, Concha Bielza

Knowledge Transfer for Learning Subject-Specific Causal Models Verónica Rodrı́guez-López, Luis Enrique Sucar

Learning Noisy-or Networks with an Application in Linguistics František Kratochvíl, Václav Kratochvíl, Jiří Vomlel

Limited Memory Influence Diagrams for Attribute Statistical Process Control with Variable Sample Sizes Barry R. Cobb

Model Inclusion Lattice of Coloured Gaussian Graphical Models for Paired Data Alberto Roverato, Dung Ngoc Nguyen

On the Rank of 2×2×2 Probability Tables Iván Pérez, Jiřı́ Vomlel

Online Single-Microphone Source Separation Using Non-Linear Autoregressive Models Bart Erp, Bert Vries

Online Updating of Conditional Linear Gaussian Bayesian Networks Anders L Madsen, Kristian G Olesen, Frank Jensen, Per Henriksen, Thomas M Larsen, Jørn M Møller

Parameterized Completeness Results for Bayesian Inference Hans L. Bodlaender, Nils Donselaar, Johan Kwisthout

Recursive Autonomy Identification-Based Learning of Augmented Naive Bayes Classifiers Shouta Sugahara, Wakaba Kishida, Koya Kato, Maomi Ueno

Relevance for Robust Bayesian Network MAP-Explanations Silja Renooij

Robust Estimation of Laplacian Constrained Gaussian Graphical Models with Trimmed Non-Convex Regularization Mariana Vargas Vieyra

Scalable Bayesian Network Structure Learning with Splines Charupriya Sharma, Peter Beek

Speeding up Approximate MAP by Applying Domain Knowledge About Relevant Variables Johan Kwisthout

Structure Learning Algorithms for Multidimensional Continuous-Time Bayesian Network Classifiers Carlos Villa-Blanco, Alessandro Bregoli, Concha Bielza, Pedro Larrañaga, Fabio Stella

The Dual PC Algorithm for Structure Learning Enrico Giudice, Jack Kuipers, Giusi Moffa

The Functional LiNGAM Tianle Yang, Joe Suzuki

Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets Marco Scutari, Christopher Marquis, Laura Azzimonti

Who Did It? Identifying the Most Likely Origins of Events Marcel Gehrke, Ralf Möller, Tanya Braun

You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks Rafael Ballester-Ripoll, Manuele Leonelli