Accepted Papers

(With GatherTown spots [II-])

1. 1. [II-A1] Analytically Tractable Inference in Deep Neural Networks
      Luong-Ha Nguyen, James-A. Goulet [paper] [poster] [talk]

   2. [II-A3] Whittle Networks: A Deep Likelihood Model for Time Series
      Zhongjie Yu, Fabrizio Ventola, Kristian Kersting [paper] [poster] [talk]

   3. [II-A2] RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting
      Nils Thoma, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting [paper] [poster] [talk]

   4. [II-A5] Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression
      Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting [paper] [poster] [talk]

   5. [II-B1] Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
      Craig Greenberg, Sebastian Macaluso, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum [paper] [poster] [talk]

   6. [II-B2] Easy Variational Inference for Categorical Observations via a New View of Diagonal Orthant Probit Models
      Michael Wojnowicz, Shuchin Aeron, Eric Miller, Michael C Hughes [paper] [poster] [talk]

   7. [II-B3] Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper
      Shami Nisimov, Yaniv Gurwicz, Raanan Yehezkel Rohekar, Gal Novik [paper] [poster] [talk]

   8. [II-B4] On Recovering from Modeling Errors Using Testing Bayesian Networks
      Haiying Huang, Adnan Darwiche [paper] [poster] [talk]

   9. [II-B5] No-Regret Approximate Inference via Bayesian Optimisation
      Rafael Oliveira, Lionel Ott, Fabio Ramos [paper] [poster] [talk]

   10. [II-C1] HyperSPNs: Compact and Expressive Probabilistic Circuits
       Andy Shih, Dorsa Sadigh, Stefano Ermon [paper] [poster] [talk]

   11. [II-C2] Probabilistic Circuits for Variational Inference in Discrete Graphical Models
       Andy Shih, Stefano Ermon [paper] [poster] [talk]

   12. [II-G1] Probabilistic Sufficient Explanations
       Eric Wang, Pasha Khosravi, Guy Van den Broeck [paper][poster] [talk]

   13. [II-C3] Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
       Benjie Wang, Clare Lyle, Marta Kwiatkowska [paper][poster] [talk]

   14. [II-C4] Conditionally Tractable Density Estimation using Neural Networks
       Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Giridhar Gogate, Nicholas Ruozzi [poster] [talk]

   15. [II-C5] Dynamic Cutset Networks
       Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Vibhav Giridhar Gogate, Nicholas Ruozzi [paper] [poster] [talk]

   16. [II-D1] CInC Flow: Characterizable Invertible 3X3 Convolution
       Sandeep Nagar, Marius Dufraisse, Girish Varma [paper] [poster] [talk]

   17. [II-G2] Tractable Regularization of Probabilistic Circuits
       Anji Liu, Guy Van den Broeck [paper] [poster] [talk]

   18. [II-A4] Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
       Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting [paper] [poster] [talk]

   19. [II-D2] Random Probabilistic Circuits
       Nicola Di Mauro, Gennaro Gala, Marco Iannotta, Teresa M.A. Basile [paper] [poster] [talk]

   20. [II-D3] Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption
       Alessandro Antonucci, Alessandro Facchini, Lilith Mattei [paper] [poster] [talk]

   21. [II-D4] Negative Weights in Hinge-Loss Markov Random Fields
       Charles Andrew Dickens, Eriq Augustine, Connor Pryor, Lise Getoor [paper] [poster] [talk]

   22. [II-D5] Fast And Accurate Learning of Probabilistic Circuits by Random Projections
       Renato Geh, Denis Mauá [paper] [poster] [talk]

   23. [II-G4] Probabilistic Generating Circuits
       Honghua Zhang, Brendan Juba, Guy Van den Broeck [paper] [poster] [talk]

   24. [II-F1] Automatic variational inference with cascading flows
       Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven [paper] [poster] [talk]

   25. [II-E1] Trumpets: Injective Flows for Inference and Inverse Problems
       Konik Kothari, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanić [paper] [poster] [talk]

   26. [II-G5] IL-Strudel : Independence-Based Learning of Structured-Decomposable Probabilistic Circuit Ensembles
       Shreyas Kowshik, Yitao Liang, Guy Van den Broeck [paper] [poster] [talk]

   27. [II-E5] Composing Normalizing Flows for Inverse Problems
       Jay Whang, Erik Lindgren, Alex Dimakis [paper] [poster] [talk]

   28. [II-H1] Is Parameter Learning via Weighted Model Integration Tractable?
       Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Andrea Passerini, Guy Van den Broeck [paper] [poster] [talk]

   29. [II-F5] Exact and Efficient Adversarial Robustness with Decomposable Neural Networks
       Pranav Shankar Subramani, Antonio Vergari, Gautam Kamath, Robert Peharz [paper] [poster] [talk]

   30. [II-G3] A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference
       Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck [paper] [poster] [talk]