Accepted Papers

  1. Robert Ganian, Eun Jung Kim, Friedrich Slivovsky and Stefan Szeider. Weighted Counting for Constraint Satisfaction with Default Values: Algorithms and Complexity Results
  2. Martin Trapp, Robert Peharz, Carl E. Rasmussen and Franz Pernkopf. Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
  3. Martin Tegner, Ben Bloem-Reddy and Stephen Roberts. Sequential sampling of Gaussian process latent variable models
  4. Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang and Guy Van den Broeck. A Semantic Loss Function for Deep Learning with Symbolic Knowledge
  5. Yoojung Choi and Guy Van den Broeck. On Robust Trimming of Bayesian Network Classifiers
  6. Tal Friedman and Guy Van den Broeck. Approximate Knowledge Compilation by Online Collapsed Importance Sampling
  7. Johannes K. Fichte, Markus Hecher, Neha Lodha and Stefan Szeider. An SMT Approach to Fractional Hypertree Width
  8. David Steurer and Junyao Zhao. Efficient Learning for Latent Dirichlet Allocation from Few Documents
  9. Yujia Shen, Arthur Choi and Adnan Darwiche. Hierarchical Maps using Structured Bayesian Networks
  10. Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting and Floriana Esposito. Sum-Product Autoencoding: Encoding and Decoding Representations using Sum-Product Networks
  11. Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting and Isabel Valera. Automatic Bayesian Density Analysis
  12. Lukas Sommer, Julian Oppermann, Alejandro Molina, Carsten Binnig, Kristian Kersting and Andreas Koch. Automatic Synthesis of FPGA-based Accelerators for the Sum-Product Network Inference Problem
  13. Song Liu, Wittawat Jitkrittum and Carl Ek. Tractable Inference with Stein Density Ratio Estimation
  14. Daniel Sheldon, Kevin Winner and Debora Sujono. Learning in Integer Latent Variable Models with Nested Automatic Differentiation
  15. Marco Benjumeda, Sergio Luengo-Sanchez, Pedro Larrañaga and Concha Bielza. Bounding the Complexity of Structural Expectation-Maximization
  16. Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle and Kristian Kersting. Efficient Symbolic Integration for Probabilistic Inference