Conference programme

We have prepared several guidelines for both oral and poster presentations. See the map of the area.

Date Time
September, 2018
08:30 - 09:00
09:00 - 09:10
09:10 - 10:10
Approximation techniques - chair: Jirka Vomlel
(09:10 - 09:40) Arthur Choi, Adnan Darwiche
On the Relative Expressiveness of Bayesian and Neural Networks
(09:40 - 10:10) Silja Renooij
Same-Decision Probability: Threshold Robustness and Application to Explanation
10:10 - 10:30
Coffee break
10:30 - 12:00
Learning I - chair: James Cussens
(10:30 - 11:00) Marco Scutari, Catharina E. Graafland and Jose Manuel Gutierrez
Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms?
(11:00-11:30) Antti Hyttinen, Johan Pensar, Juha Kontinen and Jukka Corander
Structure Learning for Bayesian Networks over Labeled DAGs
(11:30-12:00) Janne Leppä-Aho, Santeri Räisänen, Xiao Yang and Teemu Roos
Learning Non-parametric Markov Networks with Mutual Information
12:00 - 14:00
14:00 - 15:30
BN classifiers - chair: Jose A. Gámez
(14:00-14:30) Linda C. van der Gaag and Andrea Capotorti
Naive Bayesian Classifiers with Extreme Probability Features
(14:30-15:00) Andy Shih, Arthur Choi and Adnan Darwiche
Formal Verification of Bayesian Network Classifiers
(15:00-15:30) Bojan Mihaljevic, Concha Bielza Lozoya and Pedro Larranaga
Learning Bayesian network classifiers with completed partially directed acyclic graphs
15:30 - 16:00
Coffee break
16:00 - 16:55
Workshop session (Workshop proceedings) - chair: Milan Studený
software presentation (16:00-16:15) Francisco Javier Díez, Iago París, Jorge Pérez-Martín and Manuel Arias
Teaching Bayesian networks with OpenMarkov
discussion paper (16:15-16:30) Johan Kwisthout
What can the PGM community contribute to the 'Bayesian Brain' hypothesis?
panel discussion (16:30-16:55) Concha Bielza, Ilya Shpitser
Theme: PGM and neuroscience
17:00 - 17:30
Poster spotlights - chair: Václav Kratochvíl
17:30 - 19:00
Poster session 1
Mohammad Ali Javidian and Marco Valtorta
On the Properties of MVR Chain Graphs
Shahab Behjati and Hamid Beigy
An Order-based Algorithm for Learning Structure of Bayesian Networks
Marcin Kozniewski and Marek Druzdzel
Variation Intervals for Posterior Probabilities in Bayesian Networks in Anticipation of Future Observations
Aubrey Barnard and David Page
Causal Structure Learning via Temporal Markov Networks
Jacinto Arias, Jose A. Gámez and Jose M. Puerta
Bayesian Network Classifiers Under the Ensemble Perspective
Joe Suzuki
Branch and Bound for Continuous Bayesian Network Structure Learning
Shouta Sugahara, Masaki Uto and Maomi Ueno
Exact learning augmented naive Bayes classifier
Giso Dal, Alfons Laarman and Peter Lucas
Parallel Probabilistic Inference by Weighted Model Counting
Jesús Joel Rivas, Luis Enrique Sucar and Felipe Orihuela-Espina
Circular Chain Classifiers
Alex Gain and Ilya Shpitser
Structure Learning Under Missing Data
19:00 - 22:00
Welcome party
September, 2018
09:00 - 10:00
Theoretical foundations - chair: Milan Studený
(09:00-09:30) Linda C. van der Gaag, Marco Baioletti and Janneke Bolt
A Lattice Representation of Independence Relations
(09:30-10:00) Jose M. Peña
Unifying DAGs and UGs
10:00 - 10:30
Coffee break
10:30 - 12:00
Learning II - chair: Marco Scutari
(10:30-11:00) Kari Rantanen, Antti Hyttinen and Matti Järvisalo
Learning Optimal Causal Graphs with Exact Search
(11:00-11:30) Topi Talvitie, Ralf Eggeling and Mikko Koivisto
Finding Optimal Bayesian Networks with Local Structure
(11:30-12:00) Aritz Pérez, Christian Blum and Jose A. Lozano
Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical models
12:00 - 14:00
14:00 - 15:30
Sum-product networks - chair: Thomas Nielsen
(14:00-14:30) Diarmaid Conaty, Jesus Martinez Del Rincon and Cassio de Campos
Cascading Sum-Product Networks using Robustness
(14:30-15:00) Priyank Jaini, Amur Ghose and Pascal Poupart
Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks
(15:00-15:30) Alexandra Lefebvre and Gregory Nuel
A sum-product algorithm with polynomials for computing exact derivatives of the likelihood in Bayesian networks.
15:30 - 16:00
Coffee break
16:00 - 16:30
Poster spotlights - chair: Václav Kratochvíl
16:30 - 18:00
Poster session 2
Samuel Montero-Hernadez, Felipe Orihuela-Espina and Luis Enrique Sucar
Intervals of Causal Effects for Learning Causal Graphical Models
Mohammad Ali Javidian and Marco Valtorta
Finding Minimal Separators in LWF Chain Graphs
Federico Tomasi, Veronica Tozzo, Alessandro Verri and Saverio Salzo
Forward-Backward Splitting for Time-Varying Graphical Models
Andrew Li and Peter van Beek
Bayesian Network Structure Learning with Side Constraints
Cory Butz, Jhonatan Oliveira, Andre Dos Santos, Andre Lobo Teixeira, Pascal Poupart and Agastya Kalra
An Empirical Study of Methods for SPN Learning and Inference
Nils Donselaar
Parameterized hardness of active inference
Lasse Petersen
Sparse Learning in Gaussian Chain Graphs for State Space Models
Fernando Rodriguez-Sanchez, Pedro Larrañaga and Concha Bielza
Discrete model-based clustering with overlapping subsets of attributes
Karthika Mohan and Judea Pearl
Consistent Estimation given Missing Data
Alexander Oliver Mader, Jens von Berg, Cristian Lorenz and Carsten Meyer
A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets
Thijs van Ommen
Learning Bayesian Networks by Branching on Constraints
September, 2018
09:00 - 10:00
Invited talk - chair: Radim Jiroušek
Keynote Speaker: Steffen Lauritzen
Local computation - In the talk I shall try to give a bird’s eye look at local computation algorithms, partly with a historical angle but in particular with a view towards similarities and differences between them.
10:00 - 10:30
Coffee break
10:30 - 12:00
Continuous graphical models - chair: Heléne Massam
(10:30-11:00) Alberto Roverato and Robert Castelo
Differential networking with path weights in Gaussian trees
(11:00-11:30) Irene Córdoba, Gherardo Varando, Concha Bielza and Pedro Larrañaga
A partial orthogonalization method for simulating covariance and concentration graph matrices
(11:30-12:00) Manxia Liu, Fabio Stella, Arjen Hommersom and Peter Lucas
Making Continuous Time Bayesian Networks More Flexible
12:00 - 14:00
14:00 - 15:30
Inference I - chair: Concha Bielza
(14:00-14:30) Cong Chen, Changhe Yuan, Ze Ye and Chao Chen
Solving M-Modes in Loopy Graphs Using Tree Decompositions
(14:30-15:00) James Cussens
Markov Random Field MAP as Set Partitioning
(15:00-15:30) Yang Xiang and Abdulrahman Alshememry
Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models
15:30 - 16:00
Coffee break
16:00 - 17:00
General meeting
18:13, 18:22, and 18:32
Trams from Dejvicka tram station to the Břevnov Monastery - see the point 4 in the map. At Dejvicka tram station we will provide each participant two tram tickets (one for the journey to the monastery and the second one for the journey back). See the maps in your conference bag (map 1 and map 2). Please, note that each ticket must be validated after entering the tram.
19:00 - 19:20
Organ concert in Břevnov Monastery Basilica (see the map)
19:30 - 22:30
Conference dinner in Břevnov Monastery (BayesFusion Best Student Paper Award Ceremony, map)
September, 2018
09:00 - 10:00
Inference II - chair: Marek Druzdzel
(09:00-09:30) Anders Madsen, Cory Butz, Jhonatan Oliveira and Andre Dos Santos
Simple Propagation with Arc-Reversal in Bayesian Networks
(09:30-10:00) Petr Tichavský and Jiří Vomlel
Representations of Bayesian networks by low-rank models
10:00 - 10:30
Coffee break
10:30 - 12:00
Learning III - chair: Cassio de Campos
(10:30-11:00) Fattaneh Jabbari, Shyam Visweswaran and Gregory F. Cooper
Instance-Specific Bayesian Network Structure Learning
(11:00-13:30) Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen and Tom Heskes
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
(11:30-12:00) Abdullah Rashwan, Pascal Poupart and Chen Zhitang
Discriminative Training of Sum-Product Networks by Extended Baum-Welch
12:00 - 12:10