Accepted Papers
1. Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang and Brian Ziebart. Distributionally Robust Graphical Models.
2. Kijung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel and Xaq Pitkow. Inference in Probabilistic Graphical Models by Graph Neural Networks
3. Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst and Guy Van den Broeck. Towards Hardware-Aware Tractable Learning of Probabilistic Models
4. Zhe Zeng and Guy Van den Broeck. Efficient Search-Based Weighted Model Integration
5. Ping Liang Tan and Robert Peharz. Hierarchical Decompositional Mixtures of Variational Autoencoders
6. Jhonatan Oliveira, Cory Butz and Sandra Zilles. A Two-Phase Method for Focused Learning in Sum-Product Networks
7. Martin Trapp, Robert Peharz and Franz Pernkopf. Optimisation of Overparametrized Sum-Product Networks
8. Varun Embar, Sriram Srinivasan and Lise Getoor. Tractable Marginal Inference for Hinge-Loss Markov Random Fields
9. Karl Stelzner, Robert Peharz and Kristian Kersting. Faster Attend-Infer-Repeat with Tractable Probabilistic Models
10. Nandini Ramanan, Mayukh Das, Kristian Kersting and Sriraam Natarajan. Discriminative Non-Parametric Learning of Arithmetic Circuits
11. Tahrima Rahman, Shasha Jin and Vibhav Gogate. Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
12. Tahrima Rahman, Shasha Jin and Vibhav Gogate. Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models
13. Andy Shih, Guy Van den Broeck, Paul Beame and Antoine Amarilli. Smoothing Structured Decomposable Circuits
14. Yitao Liang and Guy Van den Broeck. Learning Logistic Circuits
15. Pasha Khosravi, Yitao Liang, Yoojung Choi and Guy Van den Broeck. What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
16. Tal Friedman and Guy Van den Broeck. On Constrained Open-World Probabilistic Databases
17. Eriq Augustine, Lise Getoor and Theodoros Rekatsinas. Tractable Probabilistic Reasoning Through Effective Grounding
18. Chiradeep Roy, Mahsan Nourani, Mahesh Shanbhag, Samia Kabir, Tahrima Rahman, Eric Ragan, Nicholas Ruozzi and Vibhav Gogate. Explainable Activity Recognition in Videos using Dynamic Cutset Networks
19. Ivan Stojkovic, Vladisav Jelisavcic, Jelena Gligorijevic, Djordje Gligorijevic and Zoran Obradovic. Decomposition Based Reparametrization for Efficient Estimation of Sparse Gaussian Conditional Random Fields
20. Lilith Mattei, Décio Soares, Alessandro Antonucci, Denis Maua and Alessandro Facchini. Exploring the Space of Probabilistic Sentential Decision Diagrams
21. Ian Delbridge, David Bindel and Andrew Wilson. Randomly Projected Additive Gaussian Processes
22. Juho Lee, Xenia Miscouridou and François Caron. A unified construction for series representations and finite approximations of completely random measures
23. Yiming Yan, Melissa Ailem and Fei Sha. Amortized Inference of Variational Bounds for Learning Noisy-OR
24. Steven Holtzen, Todd Millstein and Guy Van den Broeck. Symbolic Exact Inference for Discrete Probabilistic Programs
25. Pasha Khosravi, Yoojung Choi, Yitao Liang, Antonio Vergari and Guy Van den Broeck. Tractable Computation of the Moments of Predictive Models
26. Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig and Kristian Kersting. Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures