Accepted Papers

The camera-ready version of the accepted papers is available on OpenReview.

The videos of the spotlight presentations are available on YouTube.

• Training and Inference on Any-Order Autoregressive Models the Right Way
  Andy Shih, Dorsa Sadigh, Stefano Ermon
  
• Long Horizon Temperature Scaling
  Andy Shih, Dorsa Sadigh, Stefano Ermon
  
• On Modal Clustering with Gaussian Sum-Product Networks
  Tiago Madeira, Denis Mauá
  
• Sum-Product-Set Networks
  Milan Papez, Martin Rektoris, Tomáš Pevný, Vaclav Smidl
  
• Probabilistic Circuits That Know What They Don’t Know
  Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting
  
• Characteristic Circuit
  Zhongjie Yu, Martin Trapp, Kristian Kersting
  
• Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference
  Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan
  
• On test-time active feature selection through tractable acquisition functions
  Athresh Karanam, Sriraam Natarajan
  
• Logarithm-Approximate Floating-Point Multiplier for Hardware-efficient Inference in Probabilistic Circuits
  Lingyun Yao, Martin Trapp, Karthekeyan Periasamy, Jelin Leslin, Gaurav Singh, Martin Andraud
  
• Unifying and Understanding Overparameterized Circuit Representations via Low-Rank Tensor Decompositions
  Antonio Mari, Gennaro Vessio, Antonio Vergari
  
• Encoding Negative Dependencies in Probabilistic Circuits
  Aleksanteri Mikulus Sladek, Martin Trapp, Arno Solin
  
• Negative Mixture Models via Squaring
  Lorenzo Loconte, Stefan Mengel, Nicolas Gillis, Antonio Vergari
  
• Tractable Bounding of Counterfactual Queries by Knowledge Compilation
  David Huber, Yizuo Chen, Alessandro Antonucci, Adnan Darwiche, Marco Zaffalon
  
• Knowledge Intensive Learning of Cutset Networks
  Saurabh Mathur, Vibhav Giridhar Gogate, Sriraam Natarajan
  
• A Probabilistic Approach to Fairness under Label Bias
  Saurav Anchlia, YooJung Choi
  
• Causal normalizing flows: from theory to practice
  Adrián Javaloy, Pablo Sanchez Martin, Isabel Valera
  
• Bayesian Learning of Probabilistic Circuits with Domain Constraints
  Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan
  
• LOFT - Stable Training of Normalizing Flows for Variational Inference
  Daniel Andrade
  
• Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO
  Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter