Detailed program

Wednesday 5th of October

9.30 – 10.20   Plenary Talk. Prakash P. Shenoy: “Incomplete Bayesian Networks: A Dempster-Shafer Belief Function Approach

            Session chair: Serafín Moral

10.20 – 10.40                COFFEE BREAK

10.40 – 12.00   SESSION 1: Causality

            Session chair: Silja Renooij

  • Arquímides Méndez-Molina, Eduardo F Morales and Luis Enrique Sucar. Causal Discovery and Reinforcement Learning: A Synergistic Integration.
  • Verónica Rodríguez-López and Luis Enrique Sucar. Knowledge transfer for learning subject-specific causal models.
  • Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber and Dario Azzimonti. Bounding counterfactuals under selection bias.
  • Christophe Gonzales, Axel Journe and Ahmed Mabrouk. A Hybrid Algorithm for Learning Causal Networks using Uncertain Experts’ Knowledge.

12.10 – 13.10   SESSION 2: Classification

            Session chair: Andrés Cano

  • Shouta Sugahara, Wakaba Kishida, Koya Kato and Maomi Ueno. Recursive autonomy identification-based learning of augmented naive Bayes classifiers.
  • Carlos Villa Blanco, Alessandro Bregoli, Concha Bielza, Pedro Larrañaga and Fabio Stella. Structure learning algorithms for multidimensional continuous time Bayesian network classifiers.
  • Juan C. Alfaro, Juan A. Aledo and José A. Gámez. Integrating Bayesian network classifiers to deal with the partial label ranking problem.

13.10 – 14.20                LUNCH

14.20 – 15.40                 SESSION 3: Learning I           

  Session chair: Helge Langseth

  • Tianle Yang and Joe Suzuki. The Functional LiNGAM.
  • Swaraj Pawar and Prashant Doshi. Anytime Learning of Sum-Product and Sum-Product-Max Networks.
  • Peter Strong and Jim Q. Smith. Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling.
  • Marco Scutari, Christopher Marquis and Laura Azzimonti. Using Mixed-Effect Models to Learn Bayesian Networks from Related Data Sets.

15.40 – 16.00                COFFEE BREAK

16.00 – 17.00    SESSION 4: Foundations I

Session chair: Jiří Vomlel

  • Alex Markham, Danai Deligeorgaki, Pratik Misra and Liam Solus. A Transformational Characterization of Unconditionally Equivalent Bayesian Networks.
  • Alberto Roverato and Dung Ngoc Nguyen. Model inclusion lattice of colored Gaussian graphical models for paired data.
  • Thijs van Ommen and Mathias Drton. Graphical Representations for Algebraic Constraints of Linear Structural Equations Models.

Thursday, 6th of October

9.30 – 10.20      Plenary Talk: Laura Uusitalo: “Bayesian Network modelling of complex systems with sparse data: Ecological case studies”            

Session chair: Julia Flores

10.20 – 10.40    COFFEE BREAK

10.40 – 12.00    SESSION 5: Modeling and prediction            

Session chair: Barry Cobb

  • Jorge Casajús-Setién, Pedro Larrañaga and Concha Bielza. Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection.
  • Anders L Madsen, Kristian G Olesen, Frank Jensen, Per Henriksen, Thomas M Larsen and Jørn M Møller. Online Updating of Conditional Gaussian Models.
  • Anders L Madsen, Jannicke Moe, Thomas Braunbeck, Kristin A Connors, Michelle Embry, Kristin Schirmer, Stefan Scholz, Raoul Wolf and Adam Lillicrap. A Hazard Assessment System to Predict Fish Acute Toxicity.
  • Bart van Erp and Bert de Vries. Online Single-Microphone Source Separation using Non-Linear Autoregressive Models.

12.10 – 13.10    SESSION 6: Inference            

Session chair: José A. Gámez

  • Silja Renooij. Relevance for Robust Bayesian Network MAP-Explanations.
  • Johan Kwisthout. Speeding up approximate MAP by applying domain knowledge about relevant variables.
  • Jelin Leslin, Antti Hyttinen, Karthekeyan Periasamy, Lingyun Yao, Martin Trapp and Martin Andraud. A Hardware Perspective to Evaluating Probabilistic Circuits.

13.10 – 14.20    LUNCH

14.20 – 15.40    SESSION 7: Learning II

            Session chair: Concha Bielza

  • Mariana Vargas Vieyra. Robust Estimation of Laplacian Constrained Gaussian Graphical Models with Trimmed Non-convex Regularization.
  • Kiattikun Chobtham and Anthony C. Constantinou. Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound.
  • Pierre Gillot and Pekka Parviainen. Convergence of feedback arc set-based heuristics for linear structural equation models.
  • Charupriya Sharma and Peter van Beek. Scalable Bayesian Network Structure Learning with Splines.

15.40 – 16.00    COFFEE BREAK

16.00 – 17.00    SESSION 8: Explainability

            Session chair: Enrique Súcar

  • Marcel Gehrke, Ralf Möller and Tanya Braun. Who did it? Identifying the Most Likely Origins of Events.
  • Bhagirath Athresh Karanam, Saurabh Mathur, Predrag Radivojac, David M Haas, Kristian Kersting and Sriraam Natarajan. Explaining Deep Tractable Probabilistic Models: The sum-product network case.
  • Enrique Valero-Leal, Pedro Larrañaga and Concha Bielza. Interpretable Time-Varying Dynamic Bayesian Networks with Applications to Earth Climate Modelling.

Friday, 7th of October

9.30 – 10.30      SESSION 9: Foundations II

            Session chair: Pedro Larrañaga

  • Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen. A Reparameterization of Mixtures of Truncated Basis Functions and its Applications.
  • Iván Pérez and Jirka Vomlel. On the rank of 2×2×2 probability tables.
  • Nils Donselaar, Johan Kwisthout and Hans Bodlaender. Parameterized Completeness Results for Bayesian Inference.

10.30 – 10.50     COFFEE BREAK

10.50 – 12.10    SESSION 10: Learning III

            Session chair: Marco Scutari

  • Rafael Ballester-Ripoll and Manuele Leonelli. You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks.
  • Manuele Leonelli and Gherardo Varando. Highly Efficient Structural Learning of Sparse Staged Trees.
  • Jirka Vomlel, Vaclav Kratochvil and František Kratochvíl. Learning noisy-or networks with an application in linguistics.
  • Enrico Giudice, Jack Kuipers and Giusi Moffa. The Dual PC Algorithm for Structure Learning.

12.20 – 13.00    SESSION 11: Decision models            

Session chair: Manuel Gómez-Olmedo

  • Barry Cobb. Limited Memory Influence Diagrams for Attribute Statistical Process Control with Variable Sample Sizes.
  • Zhennan Wu and Roni Khardon. Approximate Inference for Stochastic Planning in Factored Spaces.

13.00 – 13.45    COMMUNITY MEETING