We have prepared several guidelines for both oral and poster presentations. See the map of the area.
| Date | Time | |
|---|---|---|
|
11
Tuesday
September, 2018
|
08:30 - 09:00 |
Registration
|
| 09:00 - 09:10 |
Opening
|
|
| 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
|
|
|
12
Wednesday
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 |
|
|
13
Thursday
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)
|
|
|
14
Friday
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 |
Closing
|
